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Do LLMs Judge Distantly Supervised Named Entity Labels Well? Constructing the JudgeWEL Dataset

Do LLMs Judge Distantly Supervised Named Entity Labels Well? Constructing the JudgeWEL Dataset

judgeWEL ๋…ผ๋ฌธ์€ ์ €์ž์› ์–ธ์–ด์ธ ๋ฃฉ์…ˆ๋ถ€๋ฅดํฌ์–ด์— ๋Œ€ํ•œ NER ๋ฐ์ดํ„ฐ ๊ตฌ์ถ•์ด๋ผ๋Š” ์‹ค์งˆ์ ์ธ ๋ฌธ์ œ์— ๋Œ€ํ•ด ์ฐฝ์˜์ ์ธ ํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•œ๋‹ค. ๊ฐ€์žฅ ํฐ ๊ฐ•์ ์€ ๋‘ ๊ฐ€์ง€ ์ธก๋ฉด์—์„œ ์•ฝํ•œ ๊ฐ๋…์„ ํ™œ์šฉํ•œ๋‹ค๋Š” ์ ์ด๋‹ค. ์ฒซ์งธ, ์œ„ํ‚คํ”ผ๋””์•„ ๋‚ด๋ถ€ ๋งํฌ์™€ ์œ„ํ‚ค๋ฐ์ดํ„ฐ์˜ ๊ตฌ์กฐํ™”๋œ ๋ฉ”ํƒ€๋ฐ์ดํ„ฐ๋ฅผ ์—ฐ๊ฒฐํ•จ์œผ๋กœ์จ ์—”ํ„ฐํ‹ฐ ์œ ํ˜•์„ ์ž๋™์œผ๋กœ ์ถ”๋ก ํ•œ๋‹ค๋Š” ์•„์ด๋””์–ด๋Š” ๊ธฐ์กด์˜ ๊ทœ์น™ ๊ธฐ๋ฐ˜ ํ˜น์€ ์‚ฌ์ „ ๋งคํ•‘ ๋ฐฉ์‹๋ณด๋‹ค ํ™•์žฅ์„ฑ์ด ๋›ฐ์–ด๋‚˜๋‹ค. ์œ„ํ‚คํ”ผ๋””์•„๋Š” ์ง€์†์ ์œผ๋กœ ์—…๋ฐ์ดํŠธ๋˜๋ฉฐ ๋‹ค์–‘ํ•œ ๋„๋ฉ”์ธ์„ ํฌ๊ด„ํ•˜๋ฏ€๋กœ, ์ด ์ ‘๊ทผ๋ฒ•์€ ์ƒˆ๋กœ์šด ์—”ํ„ฐํ‹ฐ๊ฐ€ ๋“ฑ์žฅํ•ด๋„ ๋น„๊ต์  ์‰ฝ๊ฒŒ ๋ฐ˜์˜๋  ์ˆ˜ ์žˆ๋‹ค. ๋‘˜์งธ, ์ž๋™ ๋ผ๋ฒจ๋ง ๋‹จ

Computer Science NLP Data
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FlashInfer-Bench: Building the Virtuous Cycle for AI-driven LLM Systems

FlashInferโ€‘Bench ๋…ผ๋ฌธ์€ โ€œAIโ€‘generated GPU kernelโ€์ด๋ผ๋Š” ์ตœ์‹  ์—ฐ๊ตฌ ํ๋ฆ„์„ ์‹ค์ œ ์„œ๋น„์Šค ํ™˜๊ฒฝ์— ์ ์šฉํ•˜๊ธฐ ์œ„ํ•œ ์ธํ”„๋ผ์ŠคํŠธ๋Ÿญ์ฒ˜ ์„ค๊ณ„๋ผ๋Š” ๊ด€์ ์—์„œ ๋งค์šฐ ์˜๋ฏธ ์žˆ๋Š” ๊ธฐ์—ฌ๋ฅผ ํ•˜๊ณ  ์žˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ•ต์‹ฌ์€ FlashInfer Trace ๋ผ๋Š” ๋ฉ”ํƒ€๋ฐ์ดํ„ฐ ์Šคํ‚ค๋งˆ์ด๋‹ค. ๊ธฐ์กด์— LLM์ด ์ƒ์„ฑํ•œ ์ฝ”๋“œ๋ฅผ ๋‹จ์ˆœํžˆ ํ…์ŠคํŠธ๋กœ ์ €์žฅํ•˜๊ณ  ์ธ๊ฐ„์ด ์ˆ˜๋™์œผ๋กœ ๊ฒ€์ฆํ•˜๋Š” ๋ฐฉ์‹์€ ํ™•์žฅ์„ฑ์ด ๋–จ์–ด์ง„๋‹ค. Trace๋Š” ์ปค๋„ ์ธํ„ฐํŽ˜์ด์Šค(์ž…์ถœ๋ ฅ ํ…์„œ ํ˜•ํƒœ, ๋ฉ”๋ชจ๋ฆฌ ์š”๊ตฌ๋Ÿ‰), ์›Œํฌ๋กœ๋“œ ํŠน์„ฑ(๋ฐฐ์น˜ ํฌ๊ธฐ, ์‹œํ€€์Šค ๊ธธ์ด), ๊ตฌํ˜„ ์„ธ๋ถ€์‚ฌํ•ญ(์–ธ์–ด, ์ปดํŒŒ์ผ ์˜ต์…˜)

Computer Science Artificial Intelligence System
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Geometric Regularization in Mixture-of-Experts: The Disconnect Between Weights and Activations

Mixtureโ€‘ofโ€‘Experts(MoE) ๊ตฌ์กฐ๋Š” ์ˆ˜๋ฐฑ์—์„œ ์ˆ˜์ฒœ ๊ฐœ์˜ ์ „๋ฌธ๊ฐ€ ์ค‘ ์ผ๋ถ€๋งŒ์„ ์„ ํƒ์ ์œผ๋กœ ํ™œ์„ฑํ™”ํ•จ์œผ๋กœ์จ ๊ณ„์‚ฐ ๋น„์šฉ์„ ํฌ๊ฒŒ ์ ˆ๊ฐํ•œ๋‹ค๋Š” ์žฅ์ ์ด ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ „๋ฌธ๊ฐ€๋“ค์ด ์‹ค์ œ๋กœ ์„œ๋กœ ๋‹ค๋ฅธ ๊ธฐ๋Šฅ์„ ์ˆ˜ํ–‰ํ•˜๋„๋ก ๋งŒ๋“ค๊ธฐ ์œ„ํ•ด์„œ๋Š” โ€œ์ „๋ฌธ๊ฐ€ ๋‹ค์–‘์„ฑโ€์ด ํ•„์ˆ˜์ ์ด๋ฉฐ, ์ด๋ฅผ ๋‹ฌ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ์ •๊ทœํ™” ๊ธฐ๋ฒ•์ด ์ œ์•ˆ๋˜์–ด ์™”๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๊ฐ€์žฅ ์ง๊ด€์ ์ธ ์ ‘๊ทผ๋ฒ• ์ค‘ ํ•˜๋‚˜์ธ ๊ฐ€์ค‘์น˜ ์ง๊ต ์†์‹ค์„ ์ ์šฉํ•˜์—ฌ ์ „๋ฌธ๊ฐ€ ๊ฐ„์˜ ๊ธฐํ•˜ํ•™์  ์ฐจ์ด๋ฅผ ๊ฐ•์ œํ•˜๊ณ , ๊ทธ ํšจ๊ณผ๋ฅผ ๋‹ค๊ฐ๋„๋กœ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ฒซ ๋ฒˆ์งธ ์‹คํ—˜์—์„œ๋Š” ๊ฐ€์ค‘์น˜ ๊ณต๊ฐ„ ์ค‘๋ณต๋„(MSO, Mean Subspace

Machine Learning Computer Science
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Language as Mathematical Structure: Examining Semantic Field Theory Against Language Games

์ด ๋…ผ๋ฌธ์€ ์ตœ๊ทผ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ(Large Language Models, LLM)์˜ ๊ธ‰๊ฒฉํ•œ ์„ฑ๋Šฅ ํ–ฅ์ƒ์ด ์˜๋ฏธ๋ก  ์—ฐ๊ตฌ์— ๋ฏธ์น˜๋Š” ํ•จ์˜๋ฅผ ๋‘ ์ถ•์œผ๋กœ ๋‚˜๋ˆ„์–ด ๊ณ ์ฐฐํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์ถ•์€ ๋ฃจํŠธ๋น„ํžˆ ๋น„ํŠธ๊ฒ์Šˆํƒ€์ธ์˜ ํ›„๊ธฐ ์ฒ ํ•™์— ๊ธฐ๋ฐ˜ํ•œ ์‚ฌํšŒ๊ตฌ์„ฑ์ฃผ์˜์  โ€˜์–ธ์–ด๊ฒŒ์ž„โ€™ ์ ‘๊ทผ์ด๋‹ค. ์—ฌ๊ธฐ์„œ๋Š” ์˜๋ฏธ๊ฐ€ ํ™”์ž ๊ฐ„์˜ ๊ด€์Šต์  ์ƒํ˜ธ์ž‘์šฉ๊ณผ ์‚ฌ์šฉ ์ƒํ™ฉ์— ์˜ํ•ด ํ˜•์„ฑ๋œ๋‹ค๊ณ  ๋ณด๋ฉฐ, ์–ด๋– ํ•œ ํ˜•์‹์  ๊ทœ์น™๋„ ์˜๋ฏธ๋ฅผ ์™„์ „ํžˆ ์„ค๋ช…ํ•  ์ˆ˜ ์—†๋‹ค๊ณ  ์ฃผ์žฅํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ์ถ•์€ ์ €์ž๊ฐ€ ์ œ์•ˆํ•œ โ€˜์˜๋ฏธ์žฅ ์ด๋ก (Semantic Field Theory)โ€™์œผ๋กœ, ์–ธ์–ด๋ฅผ ์—ฐ์†์ ์ธ ์˜๋ฏธ ๊ณต๊ฐ„ ์•ˆ์—์„œ ์„œ๋กœ ์–ฝํžŒ

Computer Science NLP
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Latent Flow Matching for Expressive Singing Voice Synthesis

๋ณธ ๋…ผ๋ฌธ์€ ์ตœ๊ทผ ๊ฐ€์ฐฝ ์Œ์„ฑ ํ•ฉ์„ฑ ๋ถ„์•ผ์—์„œ ๊ฐ๊ด‘๋ฐ›๊ณ  ์žˆ๋Š” ์กฐ๊ฑด๋ถ€ ๋ณ€๋ถ„ ์˜คํ† ์ธ์ฝ”๋”(cVAE) ๊ตฌ์กฐ์˜ ๊ทผ๋ณธ์ ์ธ ํ•œ๊ณ„๋ฅผ ์งš๊ณ  ์žˆ๋‹ค. cVAE๋Š” ํ•™์Šต ์‹œ์— ์‹ค์ œ ๋…น์Œ์œผ๋กœ๋ถ€ํ„ฐ ์ถ”์ •๋œ ํ›„๋ฐฉ(latent posterior) ๋ถ„ํฌ์™€, ํ•ฉ์„ฑ ์‹œ์— ์‚ฌ์ „(latent prior) ๋ถ„ํฌ๋ฅผ ๊ฐ๊ฐ ์ด์šฉํ•œ๋‹ค. ์ด ๋‘ ๋ถ„ํฌ๋Š” ์ด๋ก ์ ์œผ๋กœ๋Š” KL ๋ฐœ์‚ฐ์„ ์ตœ์†Œํ™”ํ•˜๋„๋ก ํ•™์Šต๋˜์ง€๋งŒ, ์‹ค์ œ ๋ฐ์ดํ„ฐ์˜ ๋ณต์žก์„ฑ, ํŠนํžˆ ๊ฐ€์ฐฝ ์Œ์„ฑ์˜ ๋ฏธ์„ธํ•œ ์ง„๋™(๋น„๋ธŒ๋ผํ† )์ด๋‚˜ ์–ต์–‘ ๋ณ€๋™(๋งˆ์ดํฌ๋กœ ํ”„๋กœ์†Œ๋””) ๊ฐ™์€ ๊ณ ์ฃผํŒŒ ๋ณ€๋™์„ ์™„์ „ํžˆ ํฌ์ฐฉํ•˜๊ธฐ๋Š” ์–ด๋ ต๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ ์‚ฌ์ „ ์ƒ˜ํ”Œ์„ ๊ทธ๋Œ€๋กœ ์‚ฌ์šฉํ•˜๋ฉด ํ›„๋ฐฉ

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Sparse Probabilistic Coalition Structure Generation: Bayesian Greedy Pursuit and $ell_1$ Relaxations

์ด ๋…ผ๋ฌธ์€ ์—ฐํ•ฉ ๊ตฌ์กฐ ์ƒ์„ฑ(CSG) ๋ฌธ์ œ์— โ€˜๊ฐ€์น˜๊ฐ€ ๊ด€์ธก์„ ํ†ตํ•ด ํ•™์Šต๋ผ์•ผ ํ•œ๋‹คโ€™๋Š” ์ƒˆ๋กœ์šด ์ „์ œ๋ฅผ ๋„์ž…ํ•จ์œผ๋กœ์จ ๊ธฐ์กด ์—ฐ๊ตฌ์™€ ์ฐจ๋ณ„ํ™”ํ•œ๋‹ค. ์ „ํ†ต์ ์ธ CSG๋Š” ๋ชจ๋“  ๊ฐ€๋Šฅํ•œ ์—ฐํ•ฉ์— ๋Œ€ํ•œ ์ •ํ™•ํ•œ ๊ฐ€์น˜ ํ•จ์ˆ˜๊ฐ€ ์ฃผ์–ด์กŒ๋‹ค๊ณ  ๊ฐ€์ •ํ•˜๊ณ , ๊ทธ ์œ„์—์„œ ์ตœ์ ์˜ ์—ฐํ•ฉ ๋ถ„ํ• ์„ ์ฐพ๋Š”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์‹ค์ œ ์‚ฌํšŒยท๊ฒฝ์ œ ์‹œ์Šคํ…œ์—์„œ๋Š” ๊ฐœ๋ณ„ ์—ฐํ•ฉ์˜ ๊ฐ€์น˜๋ฅผ ์ง์ ‘ ์ธก์ •ํ•˜๊ธฐ ์–ด๋ ต๊ณ , ๋Œ€์‹  ์—ฌ๋Ÿฌ ์—ํ”ผ์†Œ๋“œ(์˜ˆ: ํ˜‘์ƒ ๋ผ์šด๋“œ, ํ”„๋กœ์ ํŠธ ์ˆ˜ํ–‰ ๊ฒฐ๊ณผ)์—์„œ ์–ป์€ ์ด ๋ณด์ƒ๋งŒ ๊ด€์ฐฐ๋œ๋‹ค. ์ €์ž๋“ค์€ ์ด๋Ÿฌํ•œ ์ƒํ™ฉ์„ โ€˜ํฌ์†Œ ์„ ํ˜• ํšŒ๊ท€โ€™ ๋ชจ๋ธ๋กœ ์ •ํ˜•ํ™”ํ•œ๋‹ค. ์ฆ‰, ํ•œ ์—ํ”ผ์†Œ๋“œ์˜ ์ด ๋ณด์ƒ Yโ‚œ๋Š” ์†Œ์ˆ˜(K)๊ฐœ

Computer Science Game Theory
VisNet: Efficient Person Re-Identification via Alpha-Divergence Loss, Feature Fusion and Dynamic Multi-Task Learning

VisNet: Efficient Person Re-Identification via Alpha-Divergence Loss, Feature Fusion and Dynamic Multi-Task Learning

VisNet์€ ํ˜„์žฌ ์‚ฌ๋žŒ ์žฌ์‹๋ณ„ ๋ถ„์•ผ์—์„œ ๊ฐ€์žฅ ํฐ ๊ณผ์ œ ์ค‘ ํ•˜๋‚˜์ธ โ€œ์ •ํ™•๋„์™€ ์—ฐ์‚ฐ ํšจ์œจ์„ฑ ์‚ฌ์ด์˜ ํŠธ๋ ˆ์ด๋“œ์˜คํ”„โ€๋ฅผ ํ•ด๊ฒฐํ•˜๋ ค๋Š” ์‹œ๋„๋กœ ๋ˆˆ์— ๋ˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ ํ•ต์‹ฌ ๊ธฐ์—ฌ๋Š” ResNetโ€‘50์˜ ๋„ค ๋‹จ๊ณ„(feature map)๋“ค์„ ์ˆœ์ฐจ์ ์œผ๋กœ ๊ฒฐํ•ฉํ•˜๋ฉด์„œ๋„ ๋ณ„๋„์˜ ๋ณ‘๋ ฌ ๋ธŒ๋žœ์น˜๋ฅผ ๋„์ž…ํ•˜์ง€ ์•Š์€ ์ ์ด๋‹ค. ์ด๋Š” ๊ธฐ์กด์˜ ๋ฉ€ํ‹ฐโ€‘์Šค์ผ€์ผ ์ ‘๊ทผ๋ฒ•์ด ํ”ํžˆ ๊ฒช๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ ํญ์ฆ๊ณผ ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰ ์ฆ๊ฐ€ ๋ฌธ์ œ๋ฅผ ํฌ๊ฒŒ ์™„ํ™”ํ•œ๋‹ค. ์ž๋™ ์ฃผ์˜(attention) ๋ชจ๋“ˆ์ด ๊ฐ ์Šค์ผ€์ผ๋ณ„ ํŠน์ง•์— ๊ฐ€์ค‘์น˜๋ฅผ ๋ถ€์—ฌํ•จ์œผ๋กœ์จ, ์ €ํ•ด์ƒ๋„์—์„œ ์ถ”์ถœ๋œ ์ „์—ญ์ ์ธ ํ˜•ํƒœ ์ •๋ณด์™€ ๊ณ ํ•ด์ƒ๋„์—์„œ ์–ป์–ด์ง€๋Š” ์„ธ

Computer Vision Computer Science Learning

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