Isotropy, Clusters, and Classifiers

2024-08-25,26: 第16回 最先端NLP勉強会
https://sites.google.com/view/snlp-jp/home/2024

Transcript

1. Isotropy, Clusters, and Classi fi ers D2, Graduate School of Informatics, Nagoya University, Japan Hayato Tsukagoshi Timothee Mickus, Stig-Arne Grönroos, Joseph Attieh ACL 2024
 https://aclanthology.org/2024.acl-short.7/
2. •TL;DR: ຒΊࠐΈදݱͷ౳ํੑ޲্͸ • ҙຯతྨࣅ౓Λଌఆ͢ΔλεΫ (e.g. STS)ͷੑೳʹͱͬͯ༗ӹ • ΫϥελϦϯάλεΫͷੑೳʹͱͬͯෆརӹ •ߴ඼࣭ͳຒΊࠐΈදݱͷ֫ಘʹ͸౳ํੑ͕ॏཁͱ৴͡ΒΕ͖ͯͨ • ౳ํੑ: ຒΊࠐΈදݱ͕ۭؒதʹࢄΒ͹ͬͯ෼෍͍ͯ͠Δ͔ʁ •ຊݚڀ͸ඞͣ͠΋౳ํੑͷ޲্͕λεΫੑೳ޲্ʹد༩͠ͳ͍͜ͱΛࢦఠ • ͔ͳΓ໰୊ఏىدΓͷ࿦จ •࣮ࡍʹԼྲྀλεΫͰΫϥελϦϯάͱͯ͠ͷྑ͞ͱ౳ํੑʹͱͬͯͷྑ͞ͷ ૬൓ݱ৅Λ֬ೝͨ͠ ֓ཁ 2
3. •ࣄલ४උ •IsoScore, Silhouette scoreͷ঺հɾؔ܎ •౳ํੑͱઢܗ෼ྨثͷؔ܎ •ධՁ࣮ݧ ໔੹ࣄ߲ •εϥΠυதͷਤද͸֤εϥΠυͰݴٴ͞Ε͍ͯΔ࿦จ͔ΒͷҾ༻Ͱ͢ •࿦จதͷ਺ࣜͱ͸ҟͳΔจࣈΛ࢖͍ͬͯΔ৔߹͕͋Γ·͢ ൃද໨࣍ / ໔੹ࣄ߲ 3
4. •੩త୯ޠຒΊࠐΈɾࣄલֶशࡁΈݴޠϞσϧʹ͓͚Δҟํੑͷࢦఠ •౳ํੑΛվળ͢Δख๏ͷొ৔ •ରরֶशͷ୆಄ɺ౳ํੑ৴ڼͷڧԽ •౳ํੑ৴ڼʹର͢Δٙ໰ఏى (͍·ίί) ࣄલ४උ: ຊݚڀͷྺ࢙తҐஔ෇͚ 4
5. •౳ํత: ຒΊࠐΈදݱ͕ۭؒதʹࢄΒ͹ͬͯ෼෍͍ͯ͠Δ͜ͱ • ਺ֶతఆٛ: ෼ࢄڞ෼ࢄߦྻ͕୯Ґߦྻʹൺྫ͍ͯ͠Δ͜ͱ •ҟํత: ຒΊࠐΈදݱ͕ۭؒதʹภͬͯ෼෍͍ͯ͠Δ͜ͱ Isotropy (౳ํੑ) ͱ Anisotropy (ҟํੑ) 5 ਤ͸ Rudman et al. 2021 ΑΓҾ༻ ҟํతͳຒΊࠐΈ ͪΐͬͱ౳ํతʹͳͬͨຒΊࠐΈ
6. •౳ํత: ຒΊࠐΈදݱ͕ۭؒதʹࢄΒ͹ͬͯ෼෍͍ͯ͠Δ͜ͱ • ਺ֶతఆٛ: ෼ࢄڞ෼ࢄߦྻ͕୯Ґߦྻʹൺྫ͍ͯ͠Δ͜ͱ •ҟํత: ຒΊࠐΈදݱ͕ۭؒதʹภͬͯ෼෍͍ͯ͠Δ͜ͱ Isotropy (౳ํੑ) ͱ Anisotropy (ҟํੑ) 6 ਤ͸ Rudman et al. 2021 ΑΓҾ༻ ҟํతͳຒΊࠐΈ ͪΐͬͱ౳ํతʹͳͬͨຒΊࠐΈ ;ΜΘΓ Isotropy ͔ͬͪΓ Isotropy
7. •Word2Vec΍GloVeͳͲͷֶशࡁΈ੩త୯ޠຒΊࠐΈ͸ҟํతʹ෼෍ [1] •BERT΍GPT-2ͳͲͷจ຺Խ୯ޠຒΊࠐΈ΋ҟํతʹ෼෍ [2] •→ʮ౳ํੑΛ޲্ͤ͞Δ͜ͱ͕ੑೳ޲্ʹͭͳ͕Δ͸ͣʯͱ͍͏৴ڼͷ஀ੜ •౳ํੑ޲্ͷఆੑతޮՌ (ಛʹന৭Խ) • ੩త୯ޠຒΊࠐΈʹରͯ͠: ୯ޠස౓ʹΑΔόΠΞεΛআڈ͢Δ [3] • ಈత୯ޠຒΊࠐΈʹରͯ͠: ͳΜΒ͔ଞͷόΠΞεΛআڈ͢Δ •౳ํੑΛ޲্ͤͭͭ͞දݱֶश͢Δख๏ͱͯ͠ରরֶश͕୆಄ [1] Mu et al., All-but-the-Top: Simple and E ff ective Postprocessing for Word Representations, arXiv 2017 [2] Ethayaraja, How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings, arXiv 2019 [3] Sasaki et al., Examining the e ff ect of whitening on static and contextualized word embeddings, Information Processing & Management 2023 ຒΊࠐΈදݱͱ౳ํੑ 7 [
8. •Word2Vec΍GloVeͳͲͷֶशࡁΈ੩త୯ޠຒΊࠐΈ͸ҟํతʹ෼෍ [1] •BERT΍GPT-2ͳͲͷจ຺Խ୯ޠຒΊࠐΈ΋ҟํతʹ෼෍ [2] •→ʮ౳ํੑΛ޲্ͤ͞Δ͜ͱ͕ੑೳ޲্ʹͭͳ͕Δ͸ͣʯͱ͍͏৴ڼͷ஀ੜ •౳ํੑ޲্ͷఆੑతޮՌ (ಛʹന৭Խ) • ੩త୯ޠຒΊࠐΈʹରͯ͠: ୯ޠස౓ʹΑΔόΠΞεΛআڈ͢Δ [3] • ಈత୯ޠຒΊࠐΈʹରͯ͠: ͳΜΒ͔ଞͷόΠΞεΛআڈ͢Δ •౳ํੑΛ޲্ͤͭͭ͞දݱֶश͢Δख๏ͱͯ͠ରরֶश͕୆಄ [1] Mu et al., All-but-the-Top: Simple and E ff ective Postprocessing for Word Representations, arXiv 2017 [2] Ethayaraja, How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings, arXiv 2019 [3] Sasaki et al., Examining the e ff ect of whitening on static and contextualized word embeddings, Information Processing & Management 2023 ຒΊࠐΈදݱͱ౳ํੑ 8 [
9. •දݱֶश (representation learning) ͷख๏ͷҰͭ •ਖ਼ྫಉ͕࢜ۙͮ͘Α͏ʹɺ͔ͭɺෛྫಉ͕࢜཭ΕΔΑ͏ʹֶश͢Δ • ਖ਼ྫಉ࢜ͷྨࣅ౓࠷େԽ & ෛྫಉ࢜ͷྨࣅ౓࠷খԽ ଛࣦ (InfoNCE [4]) ͷܭࢉ •ਖ਼ྫಉ࢜ͷຒΊࠐΈදݱͷcosྨࣅ౓ΛٻΊΔ •ෛྫಉ࢜ͷຒΊࠐΈදݱͷcosྨࣅ౓ΛٻΊΔ •ྨࣅ౓Λฒ΂ͯԹ౓ύϥϝʔλΛద༻͢Δ •Softmaxؔ਺Λద༻ͯ֬͠཰෼෍ͱΈͳ͢ •ਖ਼ྫʹ͚ͩ1ཱ͕ͭ෼෍ʹ͚ۙͮΔ [4] Oord et al., Representation Learning with Contrastive Predictive Coding, arXiv 2018 ରরֶश 9
10. •දݱֶश (representation learning) ͷख๏ͷҰͭ •ਖ਼ྫಉ͕࢜ۙͮ͘Α͏ʹɺ͔ͭɺෛྫಉ͕࢜཭ΕΔΑ͏ʹֶश͢Δ • ਖ਼ྫಉ࢜ͷྨࣅ౓࠷େԽ & ෛྫಉ࢜ͷྨࣅ౓࠷খԽ ଛࣦ (InfoNCE [4]) ͷܭࢉ •ਖ਼ྫಉ࢜ͷຒΊࠐΈදݱͷcosྨࣅ౓ΛٻΊΔ •ෛྫಉ࢜ͷຒΊࠐΈදݱͷcosྨࣅ౓ΛٻΊΔ •ྨࣅ౓Λฒ΂ͯԹ౓ύϥϝʔλΛద༻͢Δ •Softmaxؔ਺Λద༻ͯ֬͠཰෼෍ͱΈͳ͢ •ਖ਼ྫʹ͚ͩ1ཱ͕ͭ෼෍ʹ͚ۙͮΔ [4] Oord et al., Representation Learning with Contrastive Predictive Coding, arXiv 2018 ରরֶश 10
11. •ରরֶशʹ͓͚Δදݱͷ “ྑ͞” ͷࢦඪ [5] • ௒ٿ໘্ʹຒΊࠐΈ͕Ͳ͏෼෍͍ͯ͠Δ͔ʁ Alignment •ਖ਼ྫϖΞͷຒΊࠐΈ͕े෼͍͍ۙͮͯΔ͔ʁ Uniformity •ຒΊࠐΈදݱͷฏۉೋ఺ؒڑ཭ •౳ํੑͷධՁʹར༻͞ΕΔ͜ͱ͕͋Δ (e.g. SimCSE) • ݫີʹ͸౳ํੑ͸ଌΕ͍ͯͳ͍
 (෼ࢄڞ෼ࢄߦྻΛݟ͍ͯͳ͍) [5] Wang et al., Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere, ICML 2020 ౳ํੑͷଌΓํ: Alignment & Uniformity 11
12. •ରরֶशʹ͓͚Δදݱͷ “ྑ͞” ͷࢦඪ [5] • ௒ٿ໘্ʹຒΊࠐΈ͕Ͳ͏෼෍͍ͯ͠Δ͔ʁ Alignment •ਖ਼ྫϖΞͷຒΊࠐΈ͕े෼͍͍ۙͮͯΔ͔ʁ Uniformity •ຒΊࠐΈදݱͷฏۉೋ఺ؒڑ཭ •౳ํੑͷධՁʹར༻͞ΕΔ͜ͱ͕͋Δ (e.g. SimCSE) • ݫີʹ͸౳ํੑ͸ଌΕ͍ͯͳ͍
 (෼ࢄڞ෼ࢄߦྻΛݟ͍ͯͳ͍) [5] Wang et al., Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere, ICML 2020 ౳ํੑͷଌΓํ: Alignment & Uniformity 12
13. •ࢦඪͷཧղ: ۭؒதͷͲΕ͘Β͍ͷ࣍ݩΛۉ౳ʹར༻͍ͯ͠Δ͔ͷׂ߹ •ܭࢉํ๏ͷେࡶ೺ͳཧղ: • ຒΊࠐΈͷू߹ΛPCAͯ࣍͠ݩ͝ͱແ૬ؔԽ • ෼ࢄϕΫτϧΛͦͷϊϧϜͰׂͬͯਖ਼نԽˠ 1ϕΫτϧͱͷζϨΛଌΔ [6] Rudman et al., IsoScore: Measuring the Uniformity of Embedding Space Utilization, ACL 2022 fi ndings ౳ํੑͷଌΓํ: IsoScore [6] 13 0.9996 0.6105 0.0281 2࣍ݩΨ΢ε෼෍ʹ͓͚Δ෼෍ܗঢ়ͱIsoScoreͷؔ܎
14. •ࢦඪͷཧղ: ۭؒதͷͲΕ͘Β͍ͷ࣍ݩΛۉ౳ʹར༻͍ͯ͠Δ͔ͷׂ߹ •ܭࢉํ๏ͷେࡶ೺ͳཧղ: • ຒΊࠐΈͷू߹ΛPCAͯ࣍͠ݩ͝ͱແ૬ؔԽ • ෼ࢄϕΫτϧΛͦͷϊϧϜͰׂͬͯਖ਼نԽˠ 1ϕΫτϧͱͷζϨΛଌΔ [6] Rudman et al., IsoScore: Measuring the Uniformity of Embedding Space Utilization, ACL 2022 fi ndings ౳ํੑͷଌΓํ: IsoScore [6] 14 0.9996 0.6105 0.0281 2࣍ݩΨ΢ε෼෍ʹ͓͚Δ෼෍ܗঢ়ͱIsoScoreͷؔ܎
15. •ΫϥελϦϯάʹ͓͚Δྑ͞ͱ౳ํੑʹ͓͚Δྑ͞͸ཱ྆͠ͳ͍ ΫϥελϦϯάʹ͓͚Δྑ͞ •ಉ͡ϥϕϧׂ͕Γ౰ͯΒΕͨࣄྫಉ࢜͸͍͍ۙͮͯͯ΄͍͠ •ҟͳΔϥϕϧׂ͕Γ౰ͯΒΕͨࣄྫಉ࢜͸ԕ͔͍ͬͯͯ͟ཉ͍͠ ౳ํੑʹ͓͚Δྑ͞ •͢΂ͯͷࣄྫ͕ۉ౳ʹࢄΒ͹͍ͬͯͯཉ͍͠ • i.e. ͍͍ۙͮͯͯཉ͍͠ࣄྫ͸ଘࡏ͠ͳ͍ ຊݚڀͷओு 15
16. •ΫϥελϦϯάʹ͓͚Δྑ͞ΛଌΔͨΊͷࢦඪ [7] • ಉΫϥε͕ۙ͘ɺผΫϥε͕ԕ͍͔ʁ [7] Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics 1987. Silhouette score: γϧΤοτείΞ 16 ∈ [−1,1]
17. •ΫϥελϦϯάʹ͓͚Δྑ͞ΛଌΔͨΊͷࢦඪ [7] • ಉΫϥε͕ۙ͘ɺผΫϥε͕ԕ͍͔ʁ [7] Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics 1987. Silhouette score: γϧΤοτείΞ 17 ∈ [−1,1]
18. •ΫϥελϦϯάʹ͓͚Δྑ͞ΛଌΔͨΊͷࢦඪ [7] • ಉΫϥε͕ۙ͘ɺผΫϥε͕ԕ͍͔ʁ [7] Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics 1987. Silhouette score: γϧΤοτείΞ 18 ͋Δࣄྫɾ͋Δू߹ͷ
 ฏۉϢʔΫϦουϊϧϜ ∈ [−1,1]
19. •ΫϥελϦϯάʹ͓͚Δྑ͞ΛଌΔͨΊͷࢦඪ [7] • ಉΫϥε͕ۙ͘ɺผΫϥε͕ԕ͍͔ʁ [7] Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics 1987. Silhouette score: γϧΤοτείΞ 19 ͋Δࣄྫɾ͋Δू߹ͷ
 ฏۉϢʔΫϦουϊϧϜ ͋ΔࣄྫͱಉΫϥεͷࣄྫ
 ͱͷίετ (Intra-cluster) ∈ [−1,1]
20. •ΫϥελϦϯάʹ͓͚Δྑ͞ΛଌΔͨΊͷࢦඪ [7] • ಉΫϥε͕ۙ͘ɺผΫϥε͕ԕ͍͔ʁ [7] Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics 1987. Silhouette score: γϧΤοτείΞ 20 ͋Δࣄྫɾ͋Δू߹ͷ
 ฏۉϢʔΫϦουϊϧϜ ͋ΔࣄྫͱಉΫϥεͷࣄྫ
 ͱͷίετ (Intra-cluster) ͋ΔࣄྫͱผΫϥεͷࣄྫ
 ͱͷ࠷খίετ (Inter-cluster) ∈ [−1,1]
21. •ΫϥελϦϯάʹ͓͚Δྑ͞ΛଌΔͨΊͷࢦඪ [7] • ಉΫϥε͕ۙ͘ɺผΫϥε͕ԕ͍͔ʁ [7] Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics 1987. Silhouette score: γϧΤοτείΞ 21 ͋Δࣄྫɾ͋Δू߹ͷ
 ฏۉϢʔΫϦουϊϧϜ ͋ΔࣄྫͱಉΫϥεͷࣄྫ
 ͱͷίετ (Intra-cluster) ͋ΔࣄྫͱผΫϥεͷࣄྫ
 ͱͷ࠷খίετ (Inter-cluster) େ͖͍΄Ͳ͍͍஋ ∈ [−1,1]
22. •Silhouette scoreͱIsoScoreΛ໨తؔ਺ͱͯ͠ఆࣜԽ •݁Ռ: Silhouette scoreͱIsoScoreͷ໨తؔ਺͸૬൓͍ͯ͠Δ Silhouette scoreͱIsoScoreͷؔ܎ 22 Silhouette IsoScore ϥϕϧґଘͰڑ཭ͷ
 ࠷େԽ or ࠷খԽ ϥϕϧඇґଘͰڑ཭ͷ
 ࠷খԽ
23. •Silhouette scoreͱIsoScoreΛ໨తؔ਺ͱͯ͠ఆࣜԽ •݁Ռ: Silhouette scoreͱIsoScoreͷ໨తؔ਺͸૬൓͍ͯ͠Δ Silhouette scoreͱIsoScoreͷؔ܎ 23 Silhouette IsoScore ϥϕϧґଘͰڑ཭ͷ
 ࠷େԽ or ࠷খԽ ϥϕϧඇґଘͰڑ཭ͷ
 ࠷খԽ
24. •Silhouette scoreͱIsoScoreΛ໨తؔ਺ͱͯ͠ఆࣜԽ •݁Ռ: Silhouette scoreͱIsoScoreͷ໨తؔ਺͸૬൓͍ͯ͠Δ Silhouette scoreͱIsoScoreͷؔ܎ 24 Silhouette IsoScore ϥϕϧґଘͰڑ཭ͷ
 ࠷େԽ or ࠷খԽ ϥϕϧඇґଘͰڑ཭ͷ
 ࠷খԽ
25. •Silhouette scoreͱIsoScoreΛ໨తؔ਺ͱͯ͠ఆࣜԽ •݁Ռ: Silhouette scoreͱIsoScoreͷ໨తؔ਺͸૬൓͍ͯ͠Δ Silhouette scoreͱIsoScoreͷؔ܎ 25 Silhouette IsoScore ϥϕϧґଘͰڑ཭ͷ
 ࠷େԽ or ࠷খԽ ϥϕϧඇґଘͰڑ཭ͷ
 ࠷খԽ
26. •Silhouette scoreͱIsoScoreΛ໨తؔ਺ͱͯ͠ఆࣜԽ •݁Ռ: Silhouette scoreͱIsoScoreͷ໨తؔ਺͸૬൓͍ͯ͠Δ Silhouette scoreͱIsoScoreͷؔ܎ 26 Silhouette IsoScore ϥϕϧґଘͰڑ཭ͷ
 ࠷େԽ or ࠷খԽ ϥϕϧඇґଘͰڑ཭ͷ
 ࠷খԽ
27. •IsotropyͱԼྲྀλεΫͷੑೳʹ͸ͲΜͳؔ܎͕͋Δ͔ʁ • ຒΊࠐΈදݱΛઢܗ෼ྨ͢Δ৔߹Λߟ͑Δ •ઢܗ෼ྨͷ໨తؔ਺Λมܗ͍ͯ͘͠ͱҎԼͷ৔߹ʹ࠷ળ • ಉ͡ΫϥεʹׂΓ౰ͯΒΕͨࣄྫ͸͢΂ͯ୯Ұͷ఺ʹऩଋ͢Δ • ҟͳΔΫϥεʹׂΓ౰ͯΒΕͨࣄྫ͸Ͱ͖Δ͚ͩ཭ΕΔ •্ه͸Silhouette scoreͷ໨తؔ਺ͱҰக ઢܗ෼ྨثͱͷؔ܎ 27
28. •IsotropyͱԼྲྀλεΫͷੑೳʹ͸ͲΜͳؔ܎͕͋Δ͔ʁ • ຒΊࠐΈදݱΛઢܗ෼ྨ͢Δ৔߹Λߟ͑Δ •ઢܗ෼ྨͷ໨తؔ਺Λมܗ͍ͯ͘͠ͱҎԼͷ৔߹ʹ࠷ળ • ಉ͡ΫϥεʹׂΓ౰ͯΒΕͨࣄྫ͸͢΂ͯ୯Ұͷ఺ʹऩଋ͢Δ • ҟͳΔΫϥεʹׂΓ౰ͯΒΕͨࣄྫ͸Ͱ͖Δ͚ͩ཭ΕΔ •্ه͸Silhouette scoreͷ໨తؔ਺ͱҰக ઢܗ෼ྨثͱͷؔ܎ 28
29. •IsotropyͱԼྲྀλεΫͷੑೳʹ͸ͲΜͳؔ܎͕͋Δ͔ʁ • ຒΊࠐΈදݱΛઢܗ෼ྨ͢Δ৔߹Λߟ͑Δ •ઢܗ෼ྨͷ໨తؔ਺Λมܗ͍ͯ͘͠ͱҎԼͷ৔߹ʹ࠷ળ • ಉ͡ΫϥεʹׂΓ౰ͯΒΕͨࣄྫ͸͢΂ͯ୯Ұͷ఺ʹऩଋ͢Δ • ҟͳΔΫϥεʹׂΓ౰ͯΒΕͨࣄྫ͸Ͱ͖Δ͚ͩ཭ΕΔ •্ه͸Silhouette scoreͷ໨తؔ਺ͱҰக ઢܗ෼ྨثͱͷؔ܎ 29
30. •ઢܗ෼ྨثΛ܇࿅ͨ͠ࡍͷIsoScore, Silhouette scoreͷมԽΛ؍࡯ •λεΫ • SBERTͷຒΊࠐΈ͔Βۃੑ෼ྨ • SBERTͷຒΊࠐΈ͔Βࣗવݴޠਪ࿦ (2จؒͷҙຯؔ܎༧ଌ) • Word2VecͷຒΊࠐΈ͔ΒPOS-tagging • Word2VecͷຒΊࠐΈ͔ΒWordNetͷsupersense༧ଌ ࣮ݧ 30 SBERTΛ࢖͏λεΫʹ͍ͭͯ͸ຊจதʹ “we directly optimize the output embeddings of the SBERT model rather
 than update the parameters of the SBERT model” ͱ͋Δ͕ɺ͜Ε͸͔ͳΓมͳઃఆͱ͍͏ؾ͕͢Δ…? SBERTͷग़ྗຒΊࠐΈΛnn.Parameterʹͯ͠࠷దԽ͍ͯ͠ΔΑ͏ (SBERTͷग़ྗจຒΊࠐΈͷू߹ΛWord2VecͷΑ͏ʹѻ͍ͬͯΔ?) SBERTͷύϥϝʔλ͸ݻఆ
31. •ֶशͷաఔͰ • Silhouette score (ΫϥελϦϯάʹ͓͍ͯͷྑ͞) ͸վળ • IsoScore (౳ํੑʹ͓͍ͯͷྑ͞) ͸ѱԽ •ֶश͕ਐΉʹͭΕͯຒΊࠐΈ͕ҟํతʹ෼෍͢ΔΑ͏มԽ ݁Ռ: ֶशதͷSilhouetteείΞɾIsoScoreͷભҠ 31 Silhouette score IsoScore
32. •SilhouetteείΞͱIsoScoreͷ
 ૬ؔਤΛϓϩοτ •΄΅׬ᘳͳٯ૬ؔΛ؍ଌ • εϐΞϚϯͷॱҐ૬ؔ܎਺: 
 -0.998ఔ౓ SilhouetteείΞͱIsoScoreͷؔ܎ 32
33. •ຒΊࠐΈදݱͷ౳ํੑ޲্͸ • ҙຯతྨࣅ౓Λଌఆ͢ΔλεΫ (e.g. STS) ͷੑೳʹͱͬͯ༗ӹ • ΫϥελϦϯάλεΫͷੑೳʹͱͬͯෆརӹ •ຒΊࠐΈͷઢܗ෼ྨͷ໨తؔ਺͸ΫϥελϦϯάͷྑ͞ͱ΄΅౳Ձ ॴײ •શମతʹͤ΍Μͳͱ͍͏ײ͡ͷ࿦จ • ຒΊࠐΈ΍ͬͯΔਓ͸ͳΜͱͳ͘ײͯͨ͡໰୊ҙࣝΛͪΌΜͱ໌ݴͯ͠Έ ·ͨ͠ͱ͍͏งғؾ •୯ҰϞσϧͰཱ྆Ͱ͖ͳ͍͔ʁʹ͍ͭͯ͸ະ஌ ·ͱΊ 33
34. •࠷ۙͷςΩετຒΊࠐΈ͸ଟ༷ͳλεΫͰͷฏۉੑೳͰଌΒΕΔ͜ͱ͕ଟ͍ • Clustering, ෼ྨ, จॻݕࡧ, ҙຯతྨࣅ౓൑ఆ… •λεΫͷੑ࣭͝ͱʹʮڧݻͳΫϥελΛܗ੒͢ΔʯຒΊࠐΈ͔ʮ౳ํతʹ෼ ෍͢ΔʯຒΊࠐΈΛར༻͢Δ͔Λߟ͑Δඞཁ͕͋Δ •ۙ೥ςΩετຒΊࠐΈ෼໺ʹ͓͍ͯʮࢦࣔʹै͍ςΩετͷຒΊࠐΈํΛม Խͤ͞Δख๏ʯ͕୆಄͖͍ͯͯ͠Δ • e.g. InstructOR, E5-Mistral, LLM2Vec, NV-Embed, … •͜ΕΒͷϞσϧ͸ࢦࣔʹैͬͯʮҟํతͳຒΊࠐΈʯʮ౳ํతͳຒΊࠐΈʯ Λग़͠෼͚Δ͜ͱ͕Ͱ͖Δ͔ʁ ༨ஊ: ࢦࣔʹै͏ຒΊࠐΈϞσϧ 34
35. •ۙ೥ͷ൚༻ςΩετຒΊࠐΈϞσϧ(E5΍GTE)͸ରরֶशʹ͓͚ΔԹ౓ύϥ ϝʔλΛখ͍ͯ͘͞͠Δ (τ = 0.01) • ςΩετຒΊࠐΈ+ରরֶशͷ૲෼͚తଘࡏͰ͋ΔSimCSE͸0.05 •ରরֶशͷԹ౓ύϥϝʔλ͸্͛Δ΄ͲڧݻͳΫϥελΛ੒͢ [8] • େن໛ֶशͷࡍ͸ϊΠζ༝དྷͷṖΫϥελ͕Ͱ͖ͳ͍Α͏Թ౓ΛԼ͛Δʁ [8] Wang et al., Understanding the Behaviour of Contrastive Loss, CVPR 2021 ༨ஊ: ରরֶशͷԹ౓ύϥϝʔλ 35
36. •ຊ࿦จʹΑΕ͹ΫϥελϦϯά΍෼ྨλεΫ͸ҟํతͰ͋Δ΄ͲΑ͍ • ௨ৗͷin-batch negativesΛར༻͢Δରরֶशଛࣦ͸͜Εʹ൓͢Δ •ςΩετຒΊࠐΈϞσϧͷֶशʹ͓͍ͯɺ෼ྨɾΫϥελϦϯάλεΫͷ fi ne-tuningΛ͢Δ৔߹ʹɺରরֶशΛ෦෼తʹ΍ΊΔ͜ͱ͕ੑೳʹد༩͢Δ ͱ͍͏ใࠂ͕࠷ۙग़͖ͯͨ [9] •໡໨తʹରরֶशଛࣦΛ࢖͏࣌୅͸ͦΖͦΖऴΘΓʁ Salesforce, SFR-Embedding-Mistral: Enhance Text Retrieval with Transfer Learning, 2024 ༨ஊ: ΫϥελϦϯάλεΫʹ͓͚Δରরֶशଛࣦͷมભ 36