Context Rot: How Increasing Input Tokens Impacts LLM Performance
Large Language Models (LLMs) are typically presumed to process context uniformly—that is, the model should handle the 10,000th token just as reliably as the 100th. However, in practice, this assumption does not hold. We observe that model performance varies significantly as input length changes, even on simple tasks. In this report, we evaluate 18 LLMs, including the state-of-the-art GPT-4.1, Clau
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言語モデルã®ç‰©ç†å¦ (Physics of Language Models) ã¨ã¯ã€FAIR (Meta) ã® Zeyuan Allen-Zhu ãŒæå”±ã—ãŸã€è¨€èªžãƒ¢ãƒ‡ãƒ«ã®ç ”究を進ã‚ã‚‹ãŸã‚ã®ã‚³ãƒ³ã‚»ãƒ—トã§ã™ã€‚ã–ã£ãり言ã†ã¨ã€ã€Œã‚ã®ãƒ¢ãƒ‡ãƒ«ã¯ã“ã†ã€ã¨ã‹ã€Œãã®ãƒ¢ãƒ‡ãƒ«ã¯ã“ã®ãƒ¢ãƒ‡ãƒ«ã‚ˆã‚Šã‚‚ã“ã†ã€ã¨ã„ã†ã‚ˆã†ãªåšç‰©å¦çš„ãªçŸ¥è˜ã‚’æ·±ã‚ã‚‹ã®ã§ã¯ãªãã€17世紀ã«ã‚±ãƒ—ラーやニュートンãŒç‰©ç†å¦ã«ãŠã„ã¦è¡Œã£ãŸã‚ˆã†ãªåŽŸç†ã«åŸºã¥ã„ãŸç ”究を進ã‚ã€ã€Œè¨€èªžãƒ¢ãƒ‡ãƒ«ã¯ãªãœã“ã®ã‚ˆã†ãªæŒ¯ã‚‹èˆžã„ã‚’ã™ã‚‹ã®ã‹ã€ã¨ã„ã†å•ã„ã«ç”ãˆã‚‰ã‚Œã‚‹ã‚ˆã†ã«ãªã‚‹ã¹ãã¨ã„ã†è€ƒãˆæ–¹ã§ã™ã€‚ 言語モデルã®ç‰©ç†å¦ã®ç‰¹å¾´ã¯å¤§ãã2ã¤ã‚ã‚Šã¾ã™ã€‚ 第一ã¯ã€ã‚¦ã‚§ãƒ–ã‹ã‚‰åŽé›†ã—ãŸã‚³ãƒ¼ãƒ‘スを使ã‚ãšã€ãã£ã¡ã‚Šã‚³ãƒ³ãƒˆãƒãƒ¼ãƒ«ã•ã‚ŒãŸãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã‚’使ã£ã¦è¨€èªžãƒ¢ãƒ‡ãƒ«ã‚’訓練ã™ã‚‹ã¨ã„ã†ã“ã¨ã€‚ウェブã¯èª°ã‚‚全体åƒã‚’ç†è§£ã§ããªã„ã»ã©è¤‡é›‘ã§ã€ãƒŽã‚¤ã‚ºã«ã¾ã¿ã‚Œã¦ã„ã¾ã™ã€‚本物ã®ç‰©ç†å¦ã§ã‚‚空気抵抗や摩擦ãŒã‚ã‚‹ã¨ã€ã€Œé‰„çƒã¯
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ã¯ã˜ã‚ã«ï¼šDeep Researchã®è¡æ’ƒ openai.com 先日ã€ChatGPTã®ã€ŒDeep Researchã€ã¨ã„ã†æ©Ÿèƒ½ãŒProユーザーå‘ã‘ã«æä¾›ã•ã‚Œã¾ã—ãŸã€‚ Deep Researchã¯ã€ã‚ªãƒ³ãƒ©ã‚¤ãƒ³ä¸Šã®æƒ…å ±ã‚½ãƒ¼ã‚¹ã‹ã‚‰ãƒ‡ãƒ¼ã‚¿ã‚’検索ã—ã€è©³ç´°ãªãƒ¬ãƒãƒ¼ãƒˆã‚’作æˆã—ã¦ãれる「リサーãƒã‚¨ãƒ¼ã‚¸ã‚§ãƒ³ãƒˆï¼ˆã‚¢ã‚·ã‚¹ã‚¿ãƒ³ãƒˆï¼‰ã€ã¨å‘¼ã°ã‚Œã‚‹ã‚‚ã®ã§ã™ã€‚ 実ã¯ã€ChatGPTã«å…ˆé§†ã‘ã¦Googleã®Geminiã«ã‚‚åŒåã®æ©Ÿèƒ½ãŒå˜åœ¨ã—ã¦ã„ãŸã‚Š*1ã€Perplexity AIã«ã‚‚検索çµæžœã¨å¿œç”ã‹ã‚‰è©³ç´°ãªãƒ¬ãƒãƒ¼ãƒˆã‚’生æˆã™ã‚‹æ©Ÿèƒ½ãŒã™ã§ã«æä¾›ã•ã‚Œã¦ã„ã¾ã—ãŸ*2。 ã•ã‚‰ã«ã•ã‹ã®ã¼ã‚‹ã¨ã€LangChainユーザーã®é–“ã§ã¯GPT Researcherã¨ã„ã†é¡žä¼¼ã®ãƒ„ールã¨ã—ã¦çŸ¥ã‚‰ã‚Œã¦ã„ã‚‹ã‹ã‚‚ã—ã‚Œã¾ã›ã‚“。 ç§è‡ªèº«ã¯ã€Žãã®ä»•äº‹ã€AIエージェントãŒã‚„ã£ã¦ãŠãã¾ã—ãŸã€‚ã€ã¨ã„ã†æœ¬ã‚’通ã˜ã¦ã€ã“ã®ã‚ˆã†ãªãƒªã‚µãƒ¼ãƒã‚¨ãƒ¼ã‚¸ã‚§ãƒ³ãƒˆã®å˜åœ¨ã‚’知りã¾ã—ãŸã€‚
A Survey on LLM-as-a-Judge
Accurate and consistent evaluation is crucial for decision-making across numerous fields, yet it remains a challenging task due to inherent subjectivity, variability, and scale. Large Language Models (LLMs) have achieved remarkable success across diverse domains, leading to the emergence of "LLM-as-a-Judge," where LLMs are employed as evaluators for complex tasks. With their ability to process div
生æˆAIを活用ã—ãŸã‚·ã‚¹ãƒ†ãƒ 開発ã®ç¾çŠ¶ã¨å±•æœ›
Copyright (c) The Japan Research Institute, Limited 生æˆAIを活用ã—ãŸã‚·ã‚¹ãƒ†ãƒ 開発 ã®ç¾çŠ¶ã¨å±•æœ› - 生æˆAI時代を見æ®ãˆãŸã‚·ã‚¹ãƒ†ãƒ 開発ã«å‘ã‘㦠- æ ªå¼ä¼šç¤¾æ—¥æœ¬ç·åˆç ”究所 先端技術ラボ 2024å¹´09月30æ—¥ <本資料ã«é–¢ã™ã‚‹ãŠå•ã„åˆã‚ã›> 伊藤蓮(ito.ren@jri.co.jp) 近藤浩å²(kondo.hirofumi@jri.co.jp) 本資料ã¯ã€ä½œæˆæ—¥æ™‚点ã§å¼Šç¤¾ãŒä¸€èˆ¬ã«ä¿¡é ¼ã§ãã‚‹ã¨æ€ã‚れる資料ã«åŸºã¥ã„ã¦ä½œæˆã•ã‚ŒãŸã‚‚ã®ã§ã™ãŒã€æƒ…å ±ã®æ£ç¢ºæ€§ãƒ»å®Œå…¨æ€§ã‚’弊社ã§ä¿è¨¼ã™ã‚‹ã‚‚ã®ã§ ã¯ã‚ã‚Šã¾ã›ã‚“。ã¾ãŸã€æœ¬è³‡æ–™ã®æƒ…å ±ã®å†…容ã¯ã€çµŒæ¸ˆæƒ…å‹¢ç‰ã®å¤‰åŒ–ã«ã‚ˆã‚Šå¤‰æ›´ã•ã‚Œã‚‹ã“ã¨ãŒã‚ã‚Šã¾ã™ã®ã§ã€ã”了承ãã ã•ã„。本資料ã®æƒ…å ±ã«èµ·å› ã—ã¦é–²è¦§è€… åŠã³ç¬¬ä¸‰è€…ã«æ害ãŒç™ºç”Ÿã—ãŸå ´åˆã§ã‚‚ã€åŸ·ç†è€…ã€åŸ·ç†å–æå…ˆåŠã³å¼Šç¤¾ã¯ä¸€åˆ‡è²¬ä»»ã‚’è² ã‚ãªã„ã‚‚ã®ã¨ã—ã¾ã™ã€‚本資料ã®è‘—作権ã¯æ ªå¼ä¼šç¤¾æ—¥
From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future
From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, Bo Li, Huaming Chen Haolin Jin, Linghan Huang and Huaming Chen are with the School of Electrical and Computer Engineering, The University of Sydney, Sydney, 2006, Australia. (email: [email protected])Haipeng Cai is with the School of Electrica
The Effects of Generative AI on High Skilled Work: Evidence from Three Field Experiments with Software Developers
Abstract This study evaluates the impact of generative AI on software developer productivity by analyzing data from three randomized controlled trials conducted at Microsoft, Accenture, and an anonymous Fortune 100 electronics manufacturing company. These field experiments, which were run by the companies as part of their ordinary course of business, provided a randomly selected subset of develope
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GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization
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Challenges and Paths Towards AI for Software Engineering
Theories of the generation effect and the impact of generation constraint: A meta-analytic review - Psychonomic Bulletin & Review
A Study of LLMs' Preferences for Libraries and Programming Languages
https://arxiv.org/pdf/2305.06156
GitHub - FSoft-AI4Code/TheVault: [EMNLP 2023] The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
人類ã¯å‹ã¦ãªã„? ChatGPTã§æœ¬æ ¼èª¿æŸ»ãƒ¬ãƒãƒ¼ãƒˆã‚’作æˆã€Œdeep researchã€ã‚’試ã—ãŸ
https://arxiv.org/pdf/2410.10934
LLMã®è«–ç†çš„推論を強化ã™ã‚‹æ–°æŠ€è¡“「Logic-of-Thought（LoT）ã€ã€€å¾“æ¥ã®Chain-of-Thought（æ€è€ƒã®é€£éŽ–）ã®ã€Œä¸å¿ 実ãªæŽ¨è«–ã€å•é¡Œè§£æ±ºã‚’目指㙠| Ledge.ai
https://www.semanticscholar.org/paper/An-Empirical-Evaluation-of-GitHub-Copilot's-Code-Nguyen-Nadi/cdfe9580f63070f311151444f9df32818cc858bf
An Empirical Evaluation of GitHub Copilot's Code Suggestions
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