fix the logit overflow caused by pad_token https://github.com/asahi417/lmppl/issues/5

experiments/analysis/correlation/after.all.csv

@@ -1,9 +0,0 @@
-,A,B,C,D,E,F,G,Others
-A,100,61,82,68,72,75,74,83
-B,61,100,61,60,63,59,60,66
-C,82,61,100,72,69,75,74,83
-D,68,60,72,100,68,69,70,77
-E,72,63,69,68,100,69,72,76
-F,75,59,75,69,69,100,70,78
-G,74,60,74,70,72,70,100,79
-Avg,76,66,76,72,73,74,74,78

experiments/analysis/correlation/after.is_competitor-rival_of.csv

@@ -1,9 +0,0 @@
-,A,B,C,D,E,F,G,Others
-A,100.0,56.54319390515396,79.87133431279439,70.95818750915267,74.03711383203164,78.69931852275197,78.87638094632628,86.08225526308935
-B,56.54319390515396,100.0,48.04659857958964,39.17693516383341,59.64637012600053,58.85352304643422,51.618426893789746,56.86820300674108
-C,79.87133431279439,48.04659857958964,100.0,76.54855391331245,67.6085519390185,74.60666967684703,75.55332383117188,81.0788877824353
-D,70.95818750915267,39.17693516383341,76.54855391331245,100.0,54.06490863378692,67.8861938889971,69.76801705759928,75.8361454463992
-E,74.03711383203164,59.64637012600053,67.6085519390185,54.06490863378692,100.0,70.09210566043514,67.33088872309497,74.86739649782788
-F,78.69931852275197,58.85352304643422,74.60666967684703,67.8861938889971,70.09210566043514,100.0,66.34696159870124,80.42278452805233
-G,78.87638094632628,51.618426893789746,75.55332383117188,69.76801705759928,67.33088872309497,66.34696159870124,100.0,79.33053104999443
-Avg,76.99793271831585,59.12643538782879,74.60500460753342,68.34325659524026,70.39713413062397,73.78353891345239,72.78485700724049,76.35517193921994

experiments/analysis/correlation/after.is_friend-ally_of.csv

@@ -1,9 +0,0 @@
-,A,B,C,D,E,F,G,Others
-A,100.0,73.5158066035359,85.12066706809678,69.40314867296632,73.66824765822612,78.1088578701807,73.39476096101995,86.8532003252799
-B,73.5158066035359,100.0,74.93994575530773,53.67391003422357,63.95426568650334,72.91672265302968,65.73772236823466,76.24715921844862
-C,85.12066706809678,74.93994575530773,100.0,69.1201397730712,71.65542383420254,77.33646092998272,74.26522935021809,87.95166180614709
-D,69.40314867296632,53.67391003422357,69.1201397730712,100.0,62.95972780275258,60.291767924280904,66.01345780309363,70.5188423648465
-E,73.66824765822612,63.95426568650334,71.65542383420254,62.95972780275258,100.0,66.68150265645419,70.60536454057583,75.61675189464589
-F,78.1088578701807,72.91672265302968,77.33646092998272,60.291767924280904,66.68150265645419,100.0,74.95839266319567,80.06289154527263
-G,73.39476096101995,65.73772236823466,74.26522935021809,66.01345780309363,70.60536454057583,74.95839266319567,100.0,78.39054390307113
-Avg,79.03021269057511,72.10548187154784,78.9196952444113,68.78030743005546,72.78921888267351,75.75624352816055,74.99641824090541,79.37729300824454

experiments/analysis/correlation/after.is_influenced_by.csv

@@ -1,9 +0,0 @@
-,A,B,C,D,E,F,G,Others
-A,100.0,51.94171331444277,76.64686404594195,68.55138766065575,68.67476656701089,60.56268637566388,71.07198336389125,76.65846418330784
-B,51.94171331444277,100.0,56.216149636956715,63.88710689660275,49.767674630280254,34.211316819741576,55.5705129270886,56.22898643303505
-C,76.64686404594195,56.216149636956715,100.0,74.50188749125914,69.52037492991374,70.19678111002037,76.59617234660793,84.5912792667659
-D,68.55138766065575,63.88710689660275,74.50188749125914,100.0,65.21611910474178,53.97509331865549,71.07394893761723,76.96075529482714
-E,68.67476656701089,49.767674630280254,69.52037492991374,65.21611910474178,100.0,65.44921765837152,71.50287989073686,70.9076829805752
-F,60.56268637566388,34.211316819741576,70.19678111002037,53.97509331865549,65.44921765837152,100.0,62.66192236851534,63.29379817092439
-G,71.07198336389125,55.5705129270886,76.59617234660793,71.07394893761723,71.50287989073686,62.66192236851534,100.0,78.37981277607685
-Avg,71.06420018965807,58.79921060358753,74.81117565152854,71.02936334421888,70.01871896872215,63.86528823585259,72.63963140492247,72.43153987221605

experiments/analysis/correlation/after.is_known_for.csv

@@ -1,9 +0,0 @@
-,A,B,C,D,E,F,G,Others
-A,100.0,74.74442833532684,84.13532982131655,78.11282022609298,79.7217723101429,80.27736779799007,78.06582585030257,88.44840275453632
-B,74.74442833532684,100.0,71.88484545113157,70.96478619650367,73.26309074151189,65.84376066947098,70.95702296183376,76.45141083803824
-C,84.13532982131655,71.88484545113157,100.0,78.34676361771689,77.61657753751035,76.34426804007906,80.7906110355711,88.67600784268357
-D,78.11282022609298,70.96478619650367,78.34676361771689,100.0,76.21394151356353,81.9798764256365,75.33292538603865,83.16422966540911
-E,79.7217723101429,73.26309074151189,77.61657753751035,76.21394151356353,100.0,71.81655636035532,76.05645487800281,80.843952632741
-F,80.27736779799007,65.84376066947098,76.34426804007906,81.9798764256365,71.81655636035532,100.0,72.46069739803418,81.11414255091312
-G,78.06582585030257,70.95702296183376,80.7906110355711,75.33292538603865,76.05645487800281,72.46069739803418,100.0,82.87601809487577
-Avg,82.15107776302456,75.37970490796839,81.3026279290465,80.13587333793603,79.24119904872668,78.38893238450945,79.09479107282615,83.08202348274244

experiments/analysis/correlation/after.is_similar_to.csv

@@ -1,9 +0,0 @@
-,A,B,C,D,E,F,G,Others
-A,100.0,60.00975228758444,81.54101511133754,74.43602738182507,77.56590405414345,78.39753384441264,73.41096267200922,82.58933416066628
-B,60.00975228758444,100.0,63.40923847092774,66.93455347751886,64.98314809483378,60.945408506618904,62.85800772150512,70.3739329356405
-C,81.54101511133754,63.40923847092774,100.0,74.89990701550816,74.51949080720523,74.1472801283226,70.62798083887134,80.30647168681931
-D,74.43602738182507,66.93455347751886,74.89990701550816,100.0,76.8730463177485,77.64379432171697,73.59632760275106,84.47353824438342
-E,77.56590405414345,64.98314809483378,74.51949080720523,76.8730463177485,100.0,74.62112320369897,77.74721316545488,82.78509781266935
-F,78.39753384441264,60.945408506618904,74.1472801283226,77.64379432171697,74.62112320369897,100.0,73.7701157624435,79.73809286146313
-G,73.41096267200922,62.85800772150512,70.62798083887134,73.59632760275106,77.74721316545488,73.7701157624435,100.0,78.98319006106121
-Avg,77.90874219304463,68.4485869369984,77.02070176745323,77.7690937310098,78.04427509186927,77.07503653817336,76.00151539471929,79.89280825181473

experiments/analysis/correlation/before.all.csv

@@ -1,9 +0,0 @@
-,A,B,C,D,E,F,G,Others
-A,100,52,78,60,62,69,67,79
-B,52,100,52,48,50,49,48,58
-C,78,52,100,62,56,69,68,78
-D,60,48,62,100,52,56,59,67
-E,62,50,56,52,100,58,60,66
-F,69,49,69,56,58,100,63,73
-G,67,48,68,59,60,63,100,73
-Avg,70,57,69,62,62,66,66,71

experiments/analysis/correlation/before.is_competitor-rival_of.csv

@@ -1,9 +0,0 @@
-,A,B,C,D,E,F,G,Others
-A,100.0,52.28721031777685,73.01988202167145,61.86751259067569,59.02327389906606,69.62464900519876,74.58759543955621,81.14875407290214
-B,52.28721031777685,100.0,47.78332950150525,33.57173333983089,51.487850121438896,47.45439315170696,51.1517480290777,55.33264230791658
-C,73.01988202167145,47.78332950150525,100.0,64.98466726607829,46.820061474869846,65.96915924507293,65.14892715557822,72.93086309794178
-D,61.86751259067569,33.57173333983089,64.98466726607829,100.0,27.065771191857724,58.48419600706666,59.06263159011146,64.35814337603051
-E,59.02327389906606,51.487850121438896,46.820061474869846,27.065771191857724,100.0,57.565084203818515,50.06001864754375,57.88758692047003
-F,69.62464900519876,47.45439315170696,65.96915924507293,58.48419600706666,57.565084203818515,100.0,64.22063751846045,75.01817026033358
-G,74.58759543955621,51.1517480290777,65.14892715557822,59.06263159011146,50.06001864754375,64.22063751846045,100.0,75.46874464331518
-Avg,70.058589039135,54.819466351619496,66.24657523782516,57.862358855088665,56.0031513626564,66.18830273304631,66.31879405433254,68.87784352555855

experiments/analysis/correlation/before.is_friend-ally_of.csv

@@ -1,9 +0,0 @@
-,A,B,C,D,E,F,G,Others
-A,100.0,64.00716670487974,80.96929991489021,60.91711038747562,51.07234033415702,69.93055219508739,61.54228944153153,80.81008710100899
-B,64.00716670487974,100.0,64.6463769759354,39.18445396059515,45.28171497683371,63.865856548521215,54.85618995287416,66.84599833129245
-C,80.96929991489021,64.6463769759354,100.0,60.98343341269177,49.48409115451416,69.15399781710762,67.14087372027983,81.05528565973273
-D,60.91711038747562,39.18445396059515,60.98343341269177,100.0,39.803858867062125,37.87022510983166,57.10120713061978,59.73916828666306
-E,51.07234033415702,45.28171497683371,49.48409115451416,39.803858867062125,100.0,46.676485112587955,56.43076386123579,56.737579516621196
-F,69.93055219508739,63.865856548521215,69.15399781710762,37.87022510983166,46.676485112587955,100.0,61.7547119793853,71.06535432862361
-G,61.54228944153153,54.85618995287416,67.14087372027983,57.10120713061978,56.43076386123579,61.7547119793853,100.0,73.2101891094599
-Avg,69.77696556828879,61.6916798742342,70.33972471363128,56.551469838325154,55.535607758055825,64.17883268036016,65.54657658370377,69.92338033334313

experiments/analysis/correlation/before.is_influenced_by.csv

@@ -1,9 +0,0 @@
-,A,B,C,D,E,F,G,Others
-A,100.0,41.13916123239298,72.7891745489556,60.38517263617589,65.39476441892661,56.59326764561505,67.2494054690764,74.89564177488828
-B,41.13916123239298,100.0,42.52077993986884,47.19483292507752,34.29897205100569,25.691451698984398,38.022325185482174,44.75182675614367
-C,72.7891745489556,42.52077993986884,100.0,58.52916677892595,59.20916995743405,64.6044634829157,69.52603806039939,77.81298422977686
-D,60.38517263617589,47.19483292507752,58.52916677892595,100.0,53.728902194119485,45.629522263837686,61.88801859214723,64.90467803921561
-E,65.39476441892661,34.29897205100569,59.20916995743405,53.728902194119485,100.0,58.54579111991007,63.97748570260461,64.69585985116137
-F,56.59326764561505,25.691451698984398,64.6044634829157,45.629522263837686,58.54579111991007,100.0,59.49798850743312,60.774236785423696
-G,67.2494054690764,38.022325185482174,69.52603806039939,61.88801859214723,63.97748570260461,59.49798850743312,100.0,72.96323307529103
-Avg,66.22156370730609,46.98107471897309,66.73982753835708,61.05080219861197,62.16501220628579,58.651783531242295,65.73732307387755,65.82835150170007

experiments/analysis/correlation/before.is_known_for.csv

@@ -1,9 +0,0 @@
-,A,B,C,D,E,F,G,Others
-A,100.0,67.60542521315183,78.99258414561156,70.60729823080707,74.68943205270077,73.99844745656023,70.94236998891486,85.0291674589494
-B,67.60542521315183,100.0,64.40711519776913,61.948046919934484,60.83803213187071,57.95402107485319,61.0893228550051,68.76444945558835
-C,78.99258414561156,64.40711519776913,100.0,72.5243047524468,71.08735855760287,71.72798299872653,78.79869372505223,86.64419989131211
-D,70.60729823080707,61.948046919934484,72.5243047524468,100.0,64.16978480034184,74.0475244254116,64.46888949548037,76.86855504489868
-E,74.68943205270077,60.83803213187071,71.08735855760287,64.16978480034184,100.0,62.80562081530741,67.78495226346166,74.65875054250918
-F,73.99844745656023,57.95402107485319,71.72798299872653,74.0475244254116,62.80562081530741,100.0,67.67681353308181,77.38669659002932
-G,70.94236998891486,61.0893228550051,78.79869372505223,64.46888949548037,67.78495226346166,67.67681353308181,100.0,78.22620278337227
-Avg,76.69079386967805,67.6917090560835,76.79114848245845,72.53797837491746,71.62502580304076,72.60148718627725,72.96586312299944,78.22543168095133

experiments/analysis/correlation/before.is_similar_to.csv

@@ -1,9 +0,0 @@
-,A,B,C,D,E,F,G,Others
-A,100.0,50.788390527450986,81.3309571392445,65.93758595023561,71.13540455548613,76.2911846293017,64.67431705394428,81.95818829358748
-B,50.788390527450986,100.0,50.29789364051304,46.1791780099053,49.29530173722065,45.834119144143784,44.635319406219466,56.74584338567736
-C,81.3309571392445,50.29789364051304,100.0,66.78448204534237,68.81163175266447,71.55517763803671,65.09737826880126,80.70066572829978
-D,65.93758595023561,46.1791780099053,66.78448204534237,100.0,63.17852638209246,62.87725600577802,59.207656007848755,72.62927605237593
-E,71.13540455548613,49.29530173722065,68.81163175266447,63.17852638209246,100.0,63.1726268025905,62.37114999056743,75.09891932367825
-F,76.2911846293017,45.834119144143784,71.55517763803671,62.87725600577802,63.1726268025905,100.0,64.02868701383065,74.86624945198784
-G,64.67431705394428,44.635319406219466,65.09737826880126,59.207656007848755,62.37114999056743,64.02868701383065,100.0,70.49994963795264
-Avg,72.8796914079519,55.29002892363617,71.98250292637177,66.30924062874321,68.28066303151738,69.10843589052591,65.71635824874456,73.21415598193705

experiments/analysis/get_error_in_top_bottom.py

@@ -1,163 +0,0 @@
-import json
-from random import uniform, seed
-from statistics import mean
-import pandas as pd
-from datasets import load_dataset
-from scipy.stats import spearmanr
-with pd.option_context("max_colwidth", 1000):
-
-    # baselines
-    target = {
-        "flan-t5-xxl": "Flan-T5\textsubscript{XXL}",
-        "opt-13b": "OPT\textsubscript{13B}",
-        "davinci": "GPT-3\textsubscript{davinci}"
-    }
-    pretty_name = {
-        'is competitor/rival of': "Rival",
-        'is friend/ally of': "Ally",
-        'is influenced by': "Inf",
-        'is known for': "Know",
-        'is similar to': "Sim",
-        'average': "Avg",
-    }
-
-
-    # def get_iaa(scores_all):
-    #     avg = [[mean(__s for _m, __s in enumerate(_s) if _m != _n) for _s in scores_all] for _n in range(7)]
-    #     single = [[_s[_n] for _s in scores_all] for _n in range(7)]
-    #     tmptmp = []
-    #     ps = []
-    #     for a, s in zip(avg, single):
-    #         c = round(pd.DataFrame([a, s]).T.corr("spearman").values[0][1] * 100, 1)
-    #         ps.append(spearmanr(a, s)[1] < 0.05)
-    #         # if str(c) == "nan":
-    #         #     seed(0)
-    #         #     c_tmp = []
-    #         #     for _ in range(1000):
-    #         #         s_tmp = [_s + uniform(-0.5, 0.5) for _s in s]
-    #         #         c_tmp.append(round(pd.DataFrame([a, s_tmp]).T.corr("spearman").values[0][1] * 100, 1))
-    #         #     c = mean(c_tmp)
-    #         tmptmp.append(c)
-    #     list(zip(tmptmp, ps))
-    #     return mean(tmptmp)
-
-
-    def format_text(_x, _y, _z):
-        bf = max(_x, _y, _z)
-        wf = str(min(_x, _y, _z))
-        _x = "\textcolor{blue}{" + str(_x) + "}" if _x == bf else str(_x)
-        _y = "\textcolor{blue}{" + str(_y) + "}" if _y == bf else str(_y)
-        _z = "\textcolor{blue}{" + str(_z) + "}" if _z == bf else str(_z)
-        _x = "\textcolor{red}{" + str(_x) + "}" if _x == wf else str(_x)
-        _y = "\textcolor{red}{" + str(_y) + "}" if _y == wf else str(_y)
-        _z = "\textcolor{red}{" + str(_z) + "}" if _z == wf else str(_z)
-        return f"{_x} / {_y} / {_z}"
-
-
-    data = load_dataset("cardiffnlp/relentless_full", split="test")
-    table_full = []
-    for prompt in ['qa', 'lc']:
-        output = []
-        for d in data:
-
-            for i in target.keys():
-                with open(f"experiments/results/lm_{prompt}/{i}/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl") as f:
-                    ppl = [json.loads(x)['perplexity'] for x in f.read().split("\n") if len(x) > 0]
-                    rank_map = {p: n for n, p in enumerate(sorted(ppl), 1)}
-                    prediction = [rank_map[p] for p in ppl]
-
-                # get index
-                total_n = len(d['ranks'])
-                p = int(total_n/3)
-                top_n = [0, int(total_n * p / 100) + 1]
-                top_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[top_n[0]: top_n[1]]]
-                bottom_n = [total_n - int(total_n * p / 100), total_n]
-                bottom_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[bottom_n[0]: bottom_n[1]]]
-                mid_n = [top_n[1], bottom_n[0]]
-                mid_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[mid_n[0]: mid_n[1]]]
-
-                # top
-                top_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[top_n[0]: top_n[1]]]
-                top_acc = len(set(top_pred).intersection(set(top_label))) / len(top_label) * 100
-                # middle
-                mid_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[mid_n[0]: mid_n[1]]]
-                mid_acc = len(set(mid_pred).intersection(set(mid_label))) / len(mid_label) * 100
-                # top
-                bottom_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[bottom_n[0]: bottom_n[1]]]
-                bottom_acc = len(set(bottom_pred).intersection(set(bottom_label))) / len(bottom_label) * 100
-
-                output.append({"model": i, "relation_type": d['relation_type'], "top": round(top_acc, 1), "bottom": round(bottom_acc, 1), "middle": round(mid_acc, 1)})
-
-        for i in target.keys():
-            output.append({
-                "model": i, "relation_type": "average",
-                "top": round(mean([o['top'] for o in output if o['model'] == i]), 0),
-                "bottom": round(mean([o['bottom'] for o in output if o['model'] == i]), 0),
-                "middle": round(mean([o['middle'] for o in output if o['model'] == i]), 0)
-            })
-
-        df = pd.DataFrame(output)
-        df['accuracy'] = [format_text(x, y, z) for x, y, z in zip(df['top'], df['middle'], df['bottom'])]
-        table = df.pivot(index="relation_type", columns="model", values="accuracy")
-        table.columns.name = None
-        table.index.name = None
-        table = table[target.keys()]
-        table.columns = [target[i] for i in table.columns]
-        table.index = [pretty_name[i] for i in table.index]
-        table = table.T[list(pretty_name.values())]
-        table = table.T
-        table = table.to_latex(escape=False)
-        table = table.split(r"\midrule")[1].split(r"\bottomrule")[0]
-        table = r"\multicolumn{4}{l}{\emph{" + prompt.upper() + r" template}} \\ " + table
-        table_full.append(table)
-
-    table_full = "\midrule".join(table_full)
-
-    #
-    # output = []
-    # top_all = []
-    # mid_all = []
-    # bottom_all = []
-    #
-    # for d in data:
-    #     if d['relation_type'] == "is influenced by":
-    #         break
-    #     total_n = len(d['ranks'])
-    #     p = int(total_n / 3)
-    #     top_n = [0, int(total_n * p / 100) + 1]
-    #     top_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[top_n[0]: top_n[1]]]
-    #     bottom_n = [total_n - int(total_n * p / 100), total_n]
-    #     bottom_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[bottom_n[0]: bottom_n[1]]]
-    #     mid_n = [top_n[1], bottom_n[0]]
-    #     mid_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[mid_n[0]: mid_n[1]]]
-    #
-    #     output.append({
-    #         "model": "IAA", "relation_type": d['relation_type'],
-    #         "top": round(get_iaa([d['scores_all'][_i] for _i in top_label]), 1),
-    #         "bottom": round(get_iaa([d['scores_all'][_i] for _i in mid_label]), 1),
-    #         "middle": round(get_iaa([d['scores_all'][_i] for _i in bottom_label]), 1)
-    #     })
-    #     top_all += [d['scores_all'][_i] for _i in top_label]
-    #     mid_all += [d['scores_all'][_i] for _i in mid_label]
-    #     bottom_all += [d['scores_all'][_i] for _i in bottom_label]
-    # output.append({
-    #     "model": "IAA", "relation_type": "average",
-    #     "top": round(get_iaa(top_all), 1),
-    #     "bottom": round(get_iaa(mid_all), 1),
-    #     "middle": round(get_iaa(bottom_all), 1)
-    # })
-    #
-    # df = pd.DataFrame(output)
-    # df['accuracy'] = [format_text(x, y, z) for x, y, z in zip(df['top'], df['middle'], df['bottom'])]
-    # table = df.pivot(index="relation_type", columns="model", values="accuracy")
-    # table.columns.name = None
-    # table.index.name = None
-    # table.index = [pretty_name[i] for i in table.index]
-    # table = table.T[list(pretty_name.values())]
-    # table = table.to_latex(escape=False)
-    # table = table.split(r"\midrule")[1].split(r"\bottomrule")[0]
-    # # table = r"\multicolumn{4}{l}{\emph{" + prompt.upper() + r" template}} \\ " + table
-    # table_full = table_full + table
-    print()
-    print()
-    print(table_full)

experiments/analysis/get_qualitative.py

@@ -1,95 +0,0 @@
-import os
-import json
-import pandas as pd
-from datasets import load_dataset
-
-pd.set_option('display.max_rows', None)
-pd.set_option('display.max_columns', None)
-os.makedirs("experiments/analysis/qualitative", exist_ok=True)
-
-# baselines
-target = {
-    "flan-t5-xxl": "Flan-T5\textsubscript{XXL}",
-    "opt-13b": "OPT\textsubscript{13B}",
-    "davinci": "GPT-3\textsubscript{davinci}"
-}
-pretty_name = {
-    'average': "Avg",
-    'is competitor/rival of': "Rival",
-    'is friend/ally of': "Ally",
-    'is influenced by': "Inf",
-    'is known for': "Know",
-    'is similar to': "Sim"
-}
-p = 30
-data = load_dataset("cardiffnlp/relentless_full", split="test")
-for prompt in ['qa', 'lc']:
-    output = []
-    for d in data:
-        for i in target.keys():
-            with open(f"experiments/results/lm_{prompt}/{i}/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl") as f:
-                ppl = [json.loads(x)['perplexity'] for x in f.read().split("\n") if len(x) > 0]
-                rank_map = {p: n for n, p in enumerate(sorted(ppl), 1)}
-                prediction = [rank_map[p] for p in ppl]
-
-            # get index
-            total_n = len(d['ranks'])
-            p = int(total_n / 3)
-            top_n = [0, int(total_n * p / 100) + 1]
-            top_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[top_n[0]: top_n[1]]]
-            bottom_n = [total_n - int(total_n * p / 100), total_n]
-            bottom_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[bottom_n[0]: bottom_n[1]]]
-            mid_n = [top_n[1], bottom_n[0]]
-            mid_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[mid_n[0]: mid_n[1]]]
-
-            # top
-            top_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[top_n[0]: top_n[1]]]
-            top_acc = len(set(top_pred).intersection(set(top_label))) / len(top_label) * 100
-            # middle
-            mid_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[mid_n[0]: mid_n[1]]]
-            mid_acc = len(set(mid_pred).intersection(set(mid_label))) / len(mid_label) * 100
-            # top
-            bottom_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[bottom_n[0]: bottom_n[1]]]
-            bottom_acc = len(set(bottom_pred).intersection(set(bottom_label))) / len(bottom_label) * 100
-
-            # the index of bottom p percent
-            output.append({
-                "relation_type": d['relation_type'],
-                "model": i,
-                "top_pred_and_bottom_gold": [" : ".join(d['pairs'][x]) for x in set(top_pred).intersection(bottom_label)],
-                "bottom_pred_and_top_gold": [" : ".join(d['pairs'][x]) for x in set(bottom_pred).intersection(top_label)],
-            })
-
-    df = pd.DataFrame(output)
-    df.to_csv(f"experiments/analysis/qualitative/{prompt}.{p}.csv", index=False)
-    # df.pop("top_num")
-    # df.pop("bottom_num")
-    df['relation_type'] = [pretty_name[i] for i in df['relation_type']]
-    print(df)
-
-    new_df = []
-    for _, i in df.iterrows():
-        top_pred_and_bottom_gold = i['top_pred_and_bottom_gold'][:min(len(i['top_pred_and_bottom_gold']), 4)]
-        bottom_pred_and_top_gold = i['bottom_pred_and_top_gold'][:min(len(i['bottom_pred_and_top_gold']), 4)]
-        for x in range(max(len(bottom_pred_and_top_gold), len(top_pred_and_bottom_gold))):
-        # for x in range(max(len(bottom_pred_and_top_gold), len(top_pred_and_bottom_gold)) // 3):
-            if len(top_pred_and_bottom_gold) >= x + 1:
-                t = ", ".join(top_pred_and_bottom_gold[x * 1:min(len(top_pred_and_bottom_gold) + 1, (x + 1)*1)])
-            else:
-                t = ""
-            if len(bottom_pred_and_top_gold) >= x + 1:
-                b = ", ".join(bottom_pred_and_top_gold[x*1:min(len(bottom_pred_and_top_gold) + 1, (x + 1)*1)])
-            else:
-                b = ""
-            new_df.append({"relation_type": i['relation_type'], "model": i['model'], "top": t, "bottom": b})
-    df_new = pd.DataFrame(new_df)
-    df_new['model'] = [target[i] for i in df_new['model']]
-    df_new = df_new[['model', 'relation_type', 'top', 'bottom']]
-    df_new = df_new.sort_values(by=['model', 'relation_type'])
-    df_new.to_csv(f"experiments/analysis/qualitative/{prompt}.{p}.format.csv", index=False)
-    with pd.option_context("max_colwidth", 1000):
-        table = df_new.to_latex(index=False, escape=False)
-        table = table.split(r"\midrule")[1].split(r"\bottomrule")[0]
-        print(table)
-
-

experiments/analysis/qualitative/lc.30.csv

@@ -1,16 +0,0 @@
-relation_type,model,top_pred_and_bottom_gold,bottom_pred_and_top_gold
-is competitor/rival of,flan-t5-xxl,['Germany : Austria'],"['Eminem : MGK', 'AWS : GCP']"
-is competitor/rival of,opt-13b,['Bashar al-Assad : Christianity'],['Netflix : Disney Plus']
-is competitor/rival of,davinci,['Serena Williams : Andy Murray'],"['Netflix : Disney Plus', 'Eminem : MGK']"
-is friend/ally of,flan-t5-xxl,"['Liam Gallagher : Noel Gallagher', 'Joseph Stalin : Josip Broz Tito', 'Sophia Loren : Marlon Brando', 'Catherine Zeta-Jones : Johnny Knoxville', 'Armenia : Azerbaijan', 'Russia : Georgia']","['Gondor : Rohan', 'FTX : Alameda Research', 'Red Bull : GoPro', 'Aznar : Bush', 'Windows : Xbox', 'Rishi Sunak : Joe Biden']"
-is friend/ally of,opt-13b,"['Joseph Stalin : Josip Broz Tito', 'Sophia Loren : Marlon Brando', 'Armenia : Azerbaijan']","['FTX : Alameda Research', 'Elsa : Anna', 'Aznar : Bush', 'Windows : Xbox', 'Rishi Sunak : Joe Biden']"
-is friend/ally of,davinci,"['Joseph Stalin : Josip Broz Tito', 'Sophia Loren : Marlon Brando', 'Armenia : Azerbaijan']","['FTX : Alameda Research', 'Elsa : Anna', 'Rishi Sunak : Joe Biden']"
-is influenced by,flan-t5-xxl,"['Joe Biden : Donald Trump', 'Harry Potter : Wizard of Oz', 'Singaporean food : Malaysian food', 'James Brown : Michael Jackson', 'Brazil : Spain']","['Prince Harry : Monarchy', 'trending music : TikTok', 'Hoover : Dyson', 'English : William Shakespeare']"
-is influenced by,opt-13b,"['Alicia Vikander : Richard Attenborough', 'Joe Biden : Donald Trump', 'Harry Potter : Wizard of Oz', 'Singaporean food : Malaysian food', 'James Brown : Michael Jackson']","['Prince Harry : Monarchy', 'trending music : TikTok', 'Stephen King : Arthur Machen', 'Wales : Westminster', 'English : William Shakespeare']"
-is influenced by,davinci,"['Singaporean food : Malaysian food', 'Harry Potter : Wizard of Oz']","['Prince Harry : Monarchy', 'trending music : TikTok', 'English : William Shakespeare']"
-is known for,flan-t5-xxl,"['Michael Jordan : Tessa Thompson', 'Italy : Hawaiian pizza', 'Inglourious Basterds : Sergio Busquets', 'Neil Armstrong : Korean War', 'Italy : tea', 'Coca-Cola : Pepsi', 'Rafael Nadal : Ralph Macchio', 'Romania : Roman Catholicism', 'Charles Bronson : Rory McIlroy']","['Greggs : sausage rolls', 'Thomas Edison : light bulb', 'Canada : maple syrup', 'Harvey Weinstein : Miramax', 'Europe : The Final Countdown', 'OpenAI : ChatGPT', 'UK : rain', 'Spain : olive oil', 'Valencia : paella']"
-is known for,opt-13b,"['Inglourious Basterds : Sergio Busquets', 'Coca-Cola : Pepsi']","['Valencia : paella', 'OpenAI : ChatGPT', 'UK : rain']"
-is known for,davinci,"['Inglourious Basterds : Sergio Busquets', 'Coca-Cola : Pepsi', 'Sophie Turner : Sylvia Plath', 'George Washington : Kiribati']","['Valencia : paella', 'OpenAI : ChatGPT', 'Bill Nye : scientist', 'Nvidia : GPUs']"
-is similar to,flan-t5-xxl,"['Uzbekistan : United States', 'Dionysus : Toyota Corolla', ""Chess : Rubik's Cube""]","['Counter Strike : Rainbow Six', 'fusilli : rotini', 'Primark : Shein', 'PS5 : XBox', 'Cerave : Nivea']"
-is similar to,opt-13b,"['Nicolae Ceaușescu : Javier Hernández', ""Chess : Rubik's Cube""]","['pill : tablet', 'Great Britian : British Empire', 'bourbon : Scotch whisky', 'fusilli : rotini', 'Minnesota : Wisconsin']"
-is similar to,davinci,"['Nicolae Ceaușescu : Javier Hernández', ""Chess : Rubik's Cube""]","['pill : tablet', 'bourbon : Scotch whisky', 'fusilli : rotini', 'Primark : Shein', 'Homebase : IKEA', 'Cerave : Nivea']"

experiments/analysis/qualitative/lc.30.format.csv

@@ -1,51 +0,0 @@
-model,relation_type,top,bottom
-Flan-T5	extsubscript{XXL},Ally,Liam Gallagher : Noel Gallagher,Gondor : Rohan
-Flan-T5	extsubscript{XXL},Ally,Joseph Stalin : Josip Broz Tito,FTX : Alameda Research
-Flan-T5	extsubscript{XXL},Ally,Sophia Loren : Marlon Brando,Red Bull : GoPro
-Flan-T5	extsubscript{XXL},Ally,Catherine Zeta-Jones : Johnny Knoxville,Aznar : Bush
-Flan-T5	extsubscript{XXL},Inf,Joe Biden : Donald Trump,Prince Harry : Monarchy
-Flan-T5	extsubscript{XXL},Inf,Harry Potter : Wizard of Oz,trending music : TikTok
-Flan-T5	extsubscript{XXL},Inf,Singaporean food : Malaysian food,Hoover : Dyson
-Flan-T5	extsubscript{XXL},Inf,James Brown : Michael Jackson,English : William Shakespeare
-Flan-T5	extsubscript{XXL},Know,Michael Jordan : Tessa Thompson,Greggs : sausage rolls
-Flan-T5	extsubscript{XXL},Know,Italy : Hawaiian pizza,Thomas Edison : light bulb
-Flan-T5	extsubscript{XXL},Know,Inglourious Basterds : Sergio Busquets,Canada : maple syrup
-Flan-T5	extsubscript{XXL},Know,Neil Armstrong : Korean War,Harvey Weinstein : Miramax
-Flan-T5	extsubscript{XXL},Rival,Germany : Austria,Eminem : MGK
-Flan-T5	extsubscript{XXL},Rival,,AWS : GCP
-Flan-T5	extsubscript{XXL},Sim,Uzbekistan : United States,Counter Strike : Rainbow Six
-Flan-T5	extsubscript{XXL},Sim,Dionysus : Toyota Corolla,fusilli : rotini
-Flan-T5	extsubscript{XXL},Sim,Chess : Rubik's Cube,Primark : Shein
-Flan-T5	extsubscript{XXL},Sim,,PS5 : XBox
-GPT-3	extsubscript{davinci},Ally,Joseph Stalin : Josip Broz Tito,FTX : Alameda Research
-GPT-3	extsubscript{davinci},Ally,Sophia Loren : Marlon Brando,Elsa : Anna
-GPT-3	extsubscript{davinci},Ally,Armenia : Azerbaijan,Rishi Sunak : Joe Biden
-GPT-3	extsubscript{davinci},Inf,Singaporean food : Malaysian food,Prince Harry : Monarchy
-GPT-3	extsubscript{davinci},Inf,Harry Potter : Wizard of Oz,trending music : TikTok
-GPT-3	extsubscript{davinci},Inf,,English : William Shakespeare
-GPT-3	extsubscript{davinci},Know,Inglourious Basterds : Sergio Busquets,Valencia : paella
-GPT-3	extsubscript{davinci},Know,Coca-Cola : Pepsi,OpenAI : ChatGPT
-GPT-3	extsubscript{davinci},Know,Sophie Turner : Sylvia Plath,Bill Nye : scientist
-GPT-3	extsubscript{davinci},Know,George Washington : Kiribati,Nvidia : GPUs
-GPT-3	extsubscript{davinci},Rival,Serena Williams : Andy Murray,Netflix : Disney Plus
-GPT-3	extsubscript{davinci},Rival,,Eminem : MGK
-GPT-3	extsubscript{davinci},Sim,Nicolae Ceaușescu : Javier Hernández,pill : tablet
-GPT-3	extsubscript{davinci},Sim,Chess : Rubik's Cube,bourbon : Scotch whisky
-GPT-3	extsubscript{davinci},Sim,,fusilli : rotini
-GPT-3	extsubscript{davinci},Sim,,Primark : Shein
-OPT	extsubscript{13B},Ally,Joseph Stalin : Josip Broz Tito,FTX : Alameda Research
-OPT	extsubscript{13B},Ally,Sophia Loren : Marlon Brando,Elsa : Anna
-OPT	extsubscript{13B},Ally,Armenia : Azerbaijan,Aznar : Bush
-OPT	extsubscript{13B},Ally,,Windows : Xbox
-OPT	extsubscript{13B},Inf,Alicia Vikander : Richard Attenborough,Prince Harry : Monarchy
-OPT	extsubscript{13B},Inf,Joe Biden : Donald Trump,trending music : TikTok
-OPT	extsubscript{13B},Inf,Harry Potter : Wizard of Oz,Stephen King : Arthur Machen
-OPT	extsubscript{13B},Inf,Singaporean food : Malaysian food,Wales : Westminster
-OPT	extsubscript{13B},Know,Inglourious Basterds : Sergio Busquets,Valencia : paella
-OPT	extsubscript{13B},Know,Coca-Cola : Pepsi,OpenAI : ChatGPT
-OPT	extsubscript{13B},Know,,UK : rain
-OPT	extsubscript{13B},Rival,Bashar al-Assad : Christianity,Netflix : Disney Plus
-OPT	extsubscript{13B},Sim,Nicolae Ceaușescu : Javier Hernández,pill : tablet
-OPT	extsubscript{13B},Sim,Chess : Rubik's Cube,Great Britian : British Empire
-OPT	extsubscript{13B},Sim,,bourbon : Scotch whisky
-OPT	extsubscript{13B},Sim,,fusilli : rotini

experiments/analysis/qualitative/lc.31.csv

@@ -1,16 +0,0 @@
-relation_type,model,top_pred_and_bottom_gold,bottom_pred_and_top_gold
-is competitor/rival of,flan-t5-xxl,[],['AWS : GCP']
-is competitor/rival of,opt-13b,[],[]
-is competitor/rival of,davinci,[],['Netflix : Disney Plus']
-is friend/ally of,flan-t5-xxl,"['Liam Gallagher : Noel Gallagher', 'Joseph Stalin : Josip Broz Tito', 'Sophia Loren : Marlon Brando', 'Catherine Zeta-Jones : Johnny Knoxville', 'Armenia : Azerbaijan', 'Russia : Georgia']","['Gondor : Rohan', 'FTX : Alameda Research', 'Red Bull : GoPro', 'Aznar : Bush', 'Windows : Xbox', 'Rishi Sunak : Joe Biden']"
-is friend/ally of,opt-13b,"['Joseph Stalin : Josip Broz Tito', 'Sophia Loren : Marlon Brando', 'Armenia : Azerbaijan']","['FTX : Alameda Research', 'Elsa : Anna', 'Aznar : Bush', 'Windows : Xbox', 'Rishi Sunak : Joe Biden']"
-is friend/ally of,davinci,"['Joseph Stalin : Josip Broz Tito', 'Sophia Loren : Marlon Brando', 'Armenia : Azerbaijan']","['FTX : Alameda Research', 'Elsa : Anna', 'Rishi Sunak : Joe Biden']"
-is influenced by,flan-t5-xxl,"['Joe Biden : Donald Trump', 'Brazil : Spain', 'Harry Potter : Wizard of Oz', 'James Brown : Michael Jackson']","['Prince Harry : Monarchy', 'trending music : TikTok', 'English : William Shakespeare']"
-is influenced by,opt-13b,"['Singaporean food : Malaysian food', 'Joe Biden : Donald Trump', 'James Brown : Michael Jackson', 'Harry Potter : Wizard of Oz']","['Prince Harry : Monarchy', 'trending music : TikTok', 'Wales : Westminster', 'English : William Shakespeare']"
-is influenced by,davinci,"['Singaporean food : Malaysian food', 'Harry Potter : Wizard of Oz']","['Prince Harry : Monarchy', 'trending music : TikTok', 'English : William Shakespeare']"
-is known for,flan-t5-xxl,"['Michael Jordan : Tessa Thompson', 'Italy : Hawaiian pizza', 'Inglourious Basterds : Sergio Busquets', 'Neil Armstrong : Korean War', 'Italy : tea', 'Coca-Cola : Pepsi', 'Rafael Nadal : Ralph Macchio', 'Romania : Roman Catholicism', 'Charles Bronson : Rory McIlroy']","['Red Bull : energy drinks', 'Thomas Edison : light bulb', 'Canada : maple syrup', 'Harvey Weinstein : Miramax', 'Alphabet Inc. : Google', 'Europe : The Final Countdown', 'OpenAI : ChatGPT', 'UK : rain', 'Spain : olive oil', 'Valencia : paella']"
-is known for,opt-13b,"['India : Gurkhas', 'Coca-Cola : Pepsi', 'Inglourious Basterds : Sergio Busquets']","['Valencia : paella', 'OpenAI : ChatGPT', 'UK : rain']"
-is known for,davinci,"['India : Gurkhas', 'Inglourious Basterds : Sergio Busquets', 'Sophie Turner : Sylvia Plath', 'Coca-Cola : Pepsi', 'George Washington : Kiribati']","['Valencia : paella', 'OpenAI : ChatGPT', 'Bill Nye : scientist', 'Nvidia : GPUs']"
-is similar to,flan-t5-xxl,"['Uzbekistan : United States', 'Dionysus : Toyota Corolla', ""Chess : Rubik's Cube""]","['Counter Strike : Rainbow Six', 'fusilli : rotini', 'Shark : Bush', 'PS5 : XBox', 'Cerave : Nivea']"
-is similar to,opt-13b,"['Nicolae Ceaușescu : Javier Hernández', ""Chess : Rubik's Cube""]","['pill : tablet', 'Great Britian : British Empire', 'fusilli : rotini', 'Shark : Bush', 'Minnesota : Wisconsin']"
-is similar to,davinci,['Nicolae Ceaușescu : Javier Hernández'],"['pill : tablet', 'fusilli : rotini', 'Shark : Bush', 'Homebase : IKEA', 'Cerave : Nivea']"

experiments/analysis/qualitative/lc.31.format.csv

@@ -1,48 +0,0 @@
-model,relation_type,top,bottom
-Flan-T5	extsubscript{XXL},Ally,Liam Gallagher : Noel Gallagher,Gondor : Rohan
-Flan-T5	extsubscript{XXL},Ally,Joseph Stalin : Josip Broz Tito,FTX : Alameda Research
-Flan-T5	extsubscript{XXL},Ally,Sophia Loren : Marlon Brando,Red Bull : GoPro
-Flan-T5	extsubscript{XXL},Ally,Catherine Zeta-Jones : Johnny Knoxville,Aznar : Bush
-Flan-T5	extsubscript{XXL},Inf,Joe Biden : Donald Trump,Prince Harry : Monarchy
-Flan-T5	extsubscript{XXL},Inf,Brazil : Spain,trending music : TikTok
-Flan-T5	extsubscript{XXL},Inf,Harry Potter : Wizard of Oz,English : William Shakespeare
-Flan-T5	extsubscript{XXL},Inf,James Brown : Michael Jackson,
-Flan-T5	extsubscript{XXL},Know,Michael Jordan : Tessa Thompson,Red Bull : energy drinks
-Flan-T5	extsubscript{XXL},Know,Italy : Hawaiian pizza,Thomas Edison : light bulb
-Flan-T5	extsubscript{XXL},Know,Inglourious Basterds : Sergio Busquets,Canada : maple syrup
-Flan-T5	extsubscript{XXL},Know,Neil Armstrong : Korean War,Harvey Weinstein : Miramax
-Flan-T5	extsubscript{XXL},Rival,,AWS : GCP
-Flan-T5	extsubscript{XXL},Sim,Uzbekistan : United States,Counter Strike : Rainbow Six
-Flan-T5	extsubscript{XXL},Sim,Dionysus : Toyota Corolla,fusilli : rotini
-Flan-T5	extsubscript{XXL},Sim,Chess : Rubik's Cube,Shark : Bush
-Flan-T5	extsubscript{XXL},Sim,,PS5 : XBox
-GPT-3	extsubscript{davinci},Ally,Joseph Stalin : Josip Broz Tito,FTX : Alameda Research
-GPT-3	extsubscript{davinci},Ally,Sophia Loren : Marlon Brando,Elsa : Anna
-GPT-3	extsubscript{davinci},Ally,Armenia : Azerbaijan,Rishi Sunak : Joe Biden
-GPT-3	extsubscript{davinci},Inf,Singaporean food : Malaysian food,Prince Harry : Monarchy
-GPT-3	extsubscript{davinci},Inf,Harry Potter : Wizard of Oz,trending music : TikTok
-GPT-3	extsubscript{davinci},Inf,,English : William Shakespeare
-GPT-3	extsubscript{davinci},Know,India : Gurkhas,Valencia : paella
-GPT-3	extsubscript{davinci},Know,Inglourious Basterds : Sergio Busquets,OpenAI : ChatGPT
-GPT-3	extsubscript{davinci},Know,Sophie Turner : Sylvia Plath,Bill Nye : scientist
-GPT-3	extsubscript{davinci},Know,Coca-Cola : Pepsi,Nvidia : GPUs
-GPT-3	extsubscript{davinci},Rival,,Netflix : Disney Plus
-GPT-3	extsubscript{davinci},Sim,Nicolae Ceaușescu : Javier Hernández,pill : tablet
-GPT-3	extsubscript{davinci},Sim,,fusilli : rotini
-GPT-3	extsubscript{davinci},Sim,,Shark : Bush
-GPT-3	extsubscript{davinci},Sim,,Homebase : IKEA
-OPT	extsubscript{13B},Ally,Joseph Stalin : Josip Broz Tito,FTX : Alameda Research
-OPT	extsubscript{13B},Ally,Sophia Loren : Marlon Brando,Elsa : Anna
-OPT	extsubscript{13B},Ally,Armenia : Azerbaijan,Aznar : Bush
-OPT	extsubscript{13B},Ally,,Windows : Xbox
-OPT	extsubscript{13B},Inf,Singaporean food : Malaysian food,Prince Harry : Monarchy
-OPT	extsubscript{13B},Inf,Joe Biden : Donald Trump,trending music : TikTok
-OPT	extsubscript{13B},Inf,James Brown : Michael Jackson,Wales : Westminster
-OPT	extsubscript{13B},Inf,Harry Potter : Wizard of Oz,English : William Shakespeare
-OPT	extsubscript{13B},Know,India : Gurkhas,Valencia : paella
-OPT	extsubscript{13B},Know,Coca-Cola : Pepsi,OpenAI : ChatGPT
-OPT	extsubscript{13B},Know,Inglourious Basterds : Sergio Busquets,UK : rain
-OPT	extsubscript{13B},Sim,Nicolae Ceaușescu : Javier Hernández,pill : tablet
-OPT	extsubscript{13B},Sim,Chess : Rubik's Cube,Great Britian : British Empire
-OPT	extsubscript{13B},Sim,,fusilli : rotini
-OPT	extsubscript{13B},Sim,,Shark : Bush

experiments/analysis/qualitative/qa.30.csv

@@ -1,16 +0,0 @@
-relation_type,model,top_pred_and_bottom_gold,bottom_pred_and_top_gold
-is competitor/rival of,flan-t5-xxl,[],['Isaac Newton : Gottfried Leibniz']
-is competitor/rival of,opt-13b,"['Serena Williams : Andy Murray', 'Olympic Games : Helicobacter pylori', 'Mikhail Khodorkovsky : Hezbollah', 'Bashar al-Assad : Christianity']","['Netflix : Disney Plus', 'AWS : GCP']"
-is competitor/rival of,davinci,"['Serena Williams : Andy Murray', 'Olympic Games : Helicobacter pylori', 'Bashar al-Assad : Christianity']","['Netflix : Disney Plus', 'Eminem : MGK', 'Saudi Arabia : Israel']"
-is friend/ally of,flan-t5-xxl,"['Keir Starmer : Jeremy Corbyn', 'Liam Gallagher : Noel Gallagher', 'Russia : Georgia', 'Armenia : Azerbaijan']","['Ron Weasley : Neville Longbottom', 'Elsa : Anna', 'Aznar : Bush', 'Rishi Sunak : Joe Biden']"
-is friend/ally of,opt-13b,"['Joseph Stalin : Josip Broz Tito', 'Sophia Loren : Marlon Brando', 'Armenia : Azerbaijan']","['Microsoft : LinkedIn', 'FTX : Alameda Research', 'Red Bull : GoPro', 'Aznar : Bush', 'Windows : Xbox', 'Rishi Sunak : Joe Biden']"
-is friend/ally of,davinci,"['Walter White : Gus Fring', 'Joseph Stalin : Josip Broz Tito', 'Sophia Loren : Marlon Brando', 'Armenia : Azerbaijan']","['FTX : Alameda Research', 'Elsa : Anna', 'Aznar : Bush', 'Windows : Xbox', 'Rishi Sunak : Joe Biden']"
-is influenced by,flan-t5-xxl,"['Luke Bryan : Hank Williams', 'Harry Potter : Wizard of Oz', 'Singaporean food : Malaysian food', 'James Brown : Michael Jackson', 'heavy metal : punk music']","['Prince Harry : Monarchy', 'Pepsi : Coca-Cola', 'trending music : TikTok', 'Wales : Westminster', 'Coca-Cola : Pepsi', 'Ethereum : Bitcoin', 'Apple Music : Spotify']"
-is influenced by,opt-13b,"['Alicia Vikander : Richard Attenborough', 'Joe Biden : Donald Trump', 'Harry Potter : Wizard of Oz', 'Singaporean food : Malaysian food', 'James Brown : Michael Jackson']","['Prince Harry : Monarchy', 'trending music : TikTok', 'Stephen King : Arthur Machen', 'Wales : Westminster', 'European Union : Germany', 'Commonwealth : United Kingdom', 'English : William Shakespeare']"
-is influenced by,davinci,"['Singaporean food : Malaysian food', 'James Brown : Michael Jackson', 'Alicia Vikander : Richard Attenborough', 'Harry Potter : Wizard of Oz']","['Prince Harry : Monarchy', 'trending music : TikTok', 'Stephen King : Arthur Machen', 'Commonwealth : United Kingdom', 'English : William Shakespeare']"
-is known for,flan-t5-xxl,"['Romania : Roman Catholicism', 'Belgium : wine']","['Harvey Weinstein : Miramax', 'France : cheese', 'Boris Johnson : Brexit', 'Europe : The Final Countdown', 'Jeff Goldblum : Jurassic Park']"
-is known for,opt-13b,"['Inglourious Basterds : Sergio Busquets', 'Sophie Turner : Sylvia Plath', 'Steve Jobs : AirPods', 'Coca-Cola : Pepsi', 'Rafael Nadal : Ralph Macchio', 'Pixar : Novosibirsk', 'Belgium : wine']","['Europe : The Final Countdown', 'OpenAI : ChatGPT', 'UK : rain', 'Jackson Pollock : action painting', 'Spain : olive oil', 'Valencia : paella']"
-is known for,davinci,"['Inglourious Basterds : Sergio Busquets', 'Rafael Nadal : Ralph Macchio', 'Coca-Cola : Pepsi', 'Sophie Turner : Sylvia Plath']","['Valencia : paella', 'OpenAI : ChatGPT', 'UK : rain', 'Spain : olive oil']"
-is similar to,flan-t5-xxl,"['New York : York', 'cannoli : canneloni', 'sphinx : sphynx', 'Gameboy : Nintendo']",['Suits : Law & Order']
-is similar to,opt-13b,"['Gisele Bündchen : Orson Welles', 'Eduardo Saverin : Guinea-Bissau', 'Nicolae Ceaușescu : Javier Hernández', ""Chess : Rubik's Cube"", 'sphinx : sphynx']","['pill : tablet', 'Great Britian : British Empire', 'bourbon : Scotch whisky', 'fusilli : rotini', 'Minnesota : Wisconsin']"
-is similar to,davinci,"['Gisele Bündchen : Orson Welles', 'Eduardo Saverin : Guinea-Bissau', 'Nicolae Ceaușescu : Javier Hernández', ""Chess : Rubik's Cube"", 'sphinx : sphynx']","['pill : tablet', 'Great Britian : British Empire', 'fusilli : rotini', 'Primark : Shein', 'PS5 : XBox', 'Cerave : Nivea', 'Minnesota : Wisconsin']"

experiments/analysis/qualitative/qa.30.format.csv

@@ -1,20 +0,0 @@
-relation_type,model,top,bottom
-Rival,OPT	extsubscript{13B},"Serena Williams : Andy Murray, Olympic Games : Helicobacter pylori","Netflix : Disney Plus, AWS : GCP"
-Rival,GPT-3	extsubscript{davinci},"Serena Williams : Andy Murray, Olympic Games : Helicobacter pylori","Netflix : Disney Plus, Eminem : MGK"
-Ally,Flan-T5	extsubscript{XXL},"Keir Starmer : Jeremy Corbyn, Liam Gallagher : Noel Gallagher","Ron Weasley : Neville Longbottom, Elsa : Anna"
-Ally,OPT	extsubscript{13B},"Joseph Stalin : Josip Broz Tito, Sophia Loren : Marlon Brando","Microsoft : LinkedIn, FTX : Alameda Research"
-Ally,OPT	extsubscript{13B},Armenia : Azerbaijan,"Red Bull : GoPro, Aznar : Bush"
-Ally,GPT-3	extsubscript{davinci},"Walter White : Gus Fring, Joseph Stalin : Josip Broz Tito","FTX : Alameda Research, Elsa : Anna"
-Inf,Flan-T5	extsubscript{XXL},"Luke Bryan : Hank Williams, Harry Potter : Wizard of Oz","Prince Harry : Monarchy, Pepsi : Coca-Cola"
-Inf,Flan-T5	extsubscript{XXL},"Singaporean food : Malaysian food, James Brown : Michael Jackson","trending music : TikTok, Wales : Westminster"
-Inf,OPT	extsubscript{13B},"Alicia Vikander : Richard Attenborough, Joe Biden : Donald Trump","Prince Harry : Monarchy, trending music : TikTok"
-Inf,OPT	extsubscript{13B},"Harry Potter : Wizard of Oz, Singaporean food : Malaysian food","Stephen King : Arthur Machen, Wales : Westminster"
-Inf,GPT-3	extsubscript{davinci},"Singaporean food : Malaysian food, James Brown : Michael Jackson","Prince Harry : Monarchy, trending music : TikTok"
-Know,Flan-T5	extsubscript{XXL},"Romania : Roman Catholicism, Belgium : wine","Harvey Weinstein : Miramax, France : cheese"
-Know,OPT	extsubscript{13B},"Inglourious Basterds : Sergio Busquets, Sophie Turner : Sylvia Plath","Europe : The Final Countdown, OpenAI : ChatGPT"
-Know,OPT	extsubscript{13B},"Steve Jobs : AirPods, Coca-Cola : Pepsi","UK : rain, Jackson Pollock : action painting"
-Know,GPT-3	extsubscript{davinci},"Inglourious Basterds : Sergio Busquets, Rafael Nadal : Ralph Macchio","Valencia : paella, OpenAI : ChatGPT"
-Sim,Flan-T5	extsubscript{XXL},"New York : York, cannoli : canneloni",
-Sim,OPT	extsubscript{13B},"Gisele Bündchen : Orson Welles, Eduardo Saverin : Guinea-Bissau","pill : tablet, Great Britian : British Empire"
-Sim,GPT-3	extsubscript{davinci},"Gisele Bündchen : Orson Welles, Eduardo Saverin : Guinea-Bissau","pill : tablet, Great Britian : British Empire"
-Sim,GPT-3	extsubscript{davinci},"Nicolae Ceaușescu : Javier Hernández, Chess : Rubik's Cube","fusilli : rotini, Primark : Shein"

experiments/analysis/qualitative/qa.31.csv

@@ -1,16 +0,0 @@
-relation_type,model,top_pred_and_bottom_gold,bottom_pred_and_top_gold
-is competitor/rival of,flan-t5-xxl,[],[]
-is competitor/rival of,opt-13b,"['Bashar al-Assad : Christianity', 'Olympic Games : Helicobacter pylori', 'Serena Williams : Andy Murray', 'Mikhail Khodorkovsky : Hezbollah']","['Netflix : Disney Plus', 'AWS : GCP']"
-is competitor/rival of,davinci,['Olympic Games : Helicobacter pylori'],['Netflix : Disney Plus']
-is friend/ally of,flan-t5-xxl,"['Liam Gallagher : Noel Gallagher', 'Russia : Georgia', 'Armenia : Azerbaijan']","['Elsa : Anna', 'Aznar : Bush', 'Rishi Sunak : Joe Biden']"
-is friend/ally of,opt-13b,"['Joseph Stalin : Josip Broz Tito', 'Sophia Loren : Marlon Brando', 'Armenia : Azerbaijan']","['Microsoft : LinkedIn', 'FTX : Alameda Research', 'Red Bull : GoPro', 'Aznar : Bush', 'Windows : Xbox', 'Rishi Sunak : Joe Biden']"
-is friend/ally of,davinci,"['Walter White : Gus Fring', 'Joseph Stalin : Josip Broz Tito', 'Sophia Loren : Marlon Brando', 'Armenia : Azerbaijan']","['FTX : Alameda Research', 'Elsa : Anna', 'Aznar : Bush', 'Windows : Xbox', 'Rishi Sunak : Joe Biden']"
-is influenced by,flan-t5-xxl,"['Luke Bryan : Hank Williams', 'James Brown : Michael Jackson', 'heavy metal : punk music', 'Harry Potter : Wizard of Oz']","['Prince Harry : Monarchy', 'trending music : TikTok', 'Wales : Westminster', 'Coca-Cola : Pepsi', 'Ethereum : Bitcoin', 'Apple Music : Spotify']"
-is influenced by,opt-13b,"['Alicia Vikander : Richard Attenborough', 'Joe Biden : Donald Trump', 'Harry Potter : Wizard of Oz', 'Singaporean food : Malaysian food', 'James Brown : Michael Jackson']","['Prince Harry : Monarchy', 'trending music : TikTok', 'Wales : Westminster', 'European Union : Germany', 'Commonwealth : United Kingdom', 'English : William Shakespeare']"
-is influenced by,davinci,"['Singaporean food : Malaysian food', 'James Brown : Michael Jackson', 'Alicia Vikander : Richard Attenborough', 'Harry Potter : Wizard of Oz']","['Prince Harry : Monarchy', 'trending music : TikTok', 'Commonwealth : United Kingdom', 'English : William Shakespeare']"
-is known for,flan-t5-xxl,"['India : Gurkhas', 'France : rococo movement', 'Romania : Roman Catholicism', 'Belgium : wine']","['Harvey Weinstein : Miramax', 'France : cheese', 'Boris Johnson : Brexit', 'Europe : The Final Countdown', 'Jeff Goldblum : Jurassic Park']"
-is known for,opt-13b,"['India : Gurkhas', 'Inglourious Basterds : Sergio Busquets', 'Sophie Turner : Sylvia Plath', 'Steve Jobs : AirPods', 'Coca-Cola : Pepsi', 'Rafael Nadal : Ralph Macchio', 'Pixar : Novosibirsk', 'Belgium : wine']","['Europe : The Final Countdown', 'OpenAI : ChatGPT', 'UK : rain', 'Jackson Pollock : action painting', 'Spain : olive oil', 'Valencia : paella']"
-is known for,davinci,"['India : Gurkhas', 'Inglourious Basterds : Sergio Busquets', 'Sophie Turner : Sylvia Plath', 'Coca-Cola : Pepsi', 'Rafael Nadal : Ralph Macchio']","['OpenAI : ChatGPT', 'Bill Nye : scientist', 'UK : rain', 'Spain : olive oil', 'Valencia : paella']"
-is similar to,flan-t5-xxl,"['New York : York', 'cannoli : canneloni', 'sphinx : sphynx', 'Gameboy : Nintendo']","['Shark : Bush', 'Suits : Law & Order']"
-is similar to,opt-13b,"['Gisele Bündchen : Orson Welles', 'Eduardo Saverin : Guinea-Bissau', 'Nicolae Ceaușescu : Javier Hernández', ""Chess : Rubik's Cube"", 'sphinx : sphynx']","['pill : tablet', 'Great Britian : British Empire', 'fusilli : rotini', 'Shark : Bush', 'Minnesota : Wisconsin']"
-is similar to,davinci,"['Gisele Bündchen : Orson Welles', 'Eduardo Saverin : Guinea-Bissau', 'Nicolae Ceaușescu : Javier Hernández', ""Chess : Rubik's Cube"", 'sphinx : sphynx']","['pill : tablet', 'Great Britian : British Empire', 'fusilli : rotini', 'Shark : Bush', 'PS5 : XBox', 'Cerave : Nivea', 'Minnesota : Wisconsin']"

experiments/analysis/qualitative/qa.31.format.csv

@@ -1,53 +0,0 @@
-model,relation_type,top,bottom
-Flan-T5	extsubscript{XXL},Ally,Liam Gallagher : Noel Gallagher,Elsa : Anna
-Flan-T5	extsubscript{XXL},Ally,Russia : Georgia,Aznar : Bush
-Flan-T5	extsubscript{XXL},Ally,Armenia : Azerbaijan,Rishi Sunak : Joe Biden
-Flan-T5	extsubscript{XXL},Inf,Luke Bryan : Hank Williams,Prince Harry : Monarchy
-Flan-T5	extsubscript{XXL},Inf,James Brown : Michael Jackson,trending music : TikTok
-Flan-T5	extsubscript{XXL},Inf,heavy metal : punk music,Wales : Westminster
-Flan-T5	extsubscript{XXL},Inf,Harry Potter : Wizard of Oz,Coca-Cola : Pepsi
-Flan-T5	extsubscript{XXL},Know,India : Gurkhas,Harvey Weinstein : Miramax
-Flan-T5	extsubscript{XXL},Know,France : rococo movement,France : cheese
-Flan-T5	extsubscript{XXL},Know,Romania : Roman Catholicism,Boris Johnson : Brexit
-Flan-T5	extsubscript{XXL},Know,Belgium : wine,Europe : The Final Countdown
-Flan-T5	extsubscript{XXL},Sim,New York : York,Shark : Bush
-Flan-T5	extsubscript{XXL},Sim,cannoli : canneloni,Suits : Law & Order
-Flan-T5	extsubscript{XXL},Sim,sphinx : sphynx,
-Flan-T5	extsubscript{XXL},Sim,Gameboy : Nintendo,
-GPT-3	extsubscript{davinci},Ally,Walter White : Gus Fring,FTX : Alameda Research
-GPT-3	extsubscript{davinci},Ally,Joseph Stalin : Josip Broz Tito,Elsa : Anna
-GPT-3	extsubscript{davinci},Ally,Sophia Loren : Marlon Brando,Aznar : Bush
-GPT-3	extsubscript{davinci},Ally,Armenia : Azerbaijan,Windows : Xbox
-GPT-3	extsubscript{davinci},Inf,Singaporean food : Malaysian food,Prince Harry : Monarchy
-GPT-3	extsubscript{davinci},Inf,James Brown : Michael Jackson,trending music : TikTok
-GPT-3	extsubscript{davinci},Inf,Alicia Vikander : Richard Attenborough,Commonwealth : United Kingdom
-GPT-3	extsubscript{davinci},Inf,Harry Potter : Wizard of Oz,English : William Shakespeare
-GPT-3	extsubscript{davinci},Know,India : Gurkhas,OpenAI : ChatGPT
-GPT-3	extsubscript{davinci},Know,Inglourious Basterds : Sergio Busquets,Bill Nye : scientist
-GPT-3	extsubscript{davinci},Know,Sophie Turner : Sylvia Plath,UK : rain
-GPT-3	extsubscript{davinci},Know,Coca-Cola : Pepsi,Spain : olive oil
-GPT-3	extsubscript{davinci},Rival,Olympic Games : Helicobacter pylori,Netflix : Disney Plus
-GPT-3	extsubscript{davinci},Sim,Gisele Bündchen : Orson Welles,pill : tablet
-GPT-3	extsubscript{davinci},Sim,Eduardo Saverin : Guinea-Bissau,Great Britian : British Empire
-GPT-3	extsubscript{davinci},Sim,Nicolae Ceaușescu : Javier Hernández,fusilli : rotini
-GPT-3	extsubscript{davinci},Sim,Chess : Rubik's Cube,Shark : Bush
-OPT	extsubscript{13B},Ally,Joseph Stalin : Josip Broz Tito,Microsoft : LinkedIn
-OPT	extsubscript{13B},Ally,Sophia Loren : Marlon Brando,FTX : Alameda Research
-OPT	extsubscript{13B},Ally,Armenia : Azerbaijan,Red Bull : GoPro
-OPT	extsubscript{13B},Ally,,Aznar : Bush
-OPT	extsubscript{13B},Inf,Alicia Vikander : Richard Attenborough,Prince Harry : Monarchy
-OPT	extsubscript{13B},Inf,Joe Biden : Donald Trump,trending music : TikTok
-OPT	extsubscript{13B},Inf,Harry Potter : Wizard of Oz,Wales : Westminster
-OPT	extsubscript{13B},Inf,Singaporean food : Malaysian food,European Union : Germany
-OPT	extsubscript{13B},Know,India : Gurkhas,Europe : The Final Countdown
-OPT	extsubscript{13B},Know,Inglourious Basterds : Sergio Busquets,OpenAI : ChatGPT
-OPT	extsubscript{13B},Know,Sophie Turner : Sylvia Plath,UK : rain
-OPT	extsubscript{13B},Know,Steve Jobs : AirPods,Jackson Pollock : action painting
-OPT	extsubscript{13B},Rival,Bashar al-Assad : Christianity,Netflix : Disney Plus
-OPT	extsubscript{13B},Rival,Olympic Games : Helicobacter pylori,AWS : GCP
-OPT	extsubscript{13B},Rival,Serena Williams : Andy Murray,
-OPT	extsubscript{13B},Rival,Mikhail Khodorkovsky : Hezbollah,
-OPT	extsubscript{13B},Sim,Gisele Bündchen : Orson Welles,pill : tablet
-OPT	extsubscript{13B},Sim,Eduardo Saverin : Guinea-Bissau,Great Britian : British Empire
-OPT	extsubscript{13B},Sim,Nicolae Ceaușescu : Javier Hernández,fusilli : rotini
-OPT	extsubscript{13B},Sim,Chess : Rubik's Cube,Shark : Bush