activity
20242026
most citedKimi K2: Open Agentic Intelligence

14 citations · 28 across the 15 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CL2024

ExecRepoBench: Multi-level Executable Code Completion Evaluation

Jian Yang, Jiajun Zhang, Jiaxi Yang +9

Code completion has become an essential tool for daily software development. Existing evaluation benchmarks often employ static methods that do not fully capture the dynamic nature…

cs.CL2024

Evaluating and Aligning CodeLLMs on Human Preference

Jian Yang, Jiaxi Yang, Ke Jin +7

Code large language models (codeLLMs) have made significant strides in code generation. Most previous code-related benchmarks, which consist of various programming exercises along…

cs.CL2024★ 1 cited

Towards a Unified View of Preference Learning for Large Language Models: A Survey

Bofei Gao, Feifan Song, Yibo Miao +22

Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial factors to achieve success is aligning the LLM's output with human preferences. This align…

cs.AI2024★ 1 cited

Aligning CodeLLMs with Direct Preference Optimization

Yibo Miao, Bofei Gao, Shanghaoran Quan +6

The last year has witnessed the rapid progress of large language models (LLMs) across diverse domains. Among them, CodeLLMs have garnered particular attention because they can not…

cs.CL2024★ 3 cited

Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models

Bofei Gao, Feifan Song, Zhe Yang +17

Recent advancements in large language models (LLMs) have led to significant breakthroughs in mathematical reasoning capabilities. However, existing benchmarks like GSM8K or MATH ar…

cs.CL2024★ 2 cited

Measuring Bargaining Abilities of LLMs: A Benchmark and A Buyer-Enhancement Method

Tian Xia, Zhiwei He, Tong Ren +4

Bargaining is an important and unique part of negotiation between humans. As LLM-driven agents learn to negotiate and act like real humans, how to evaluate agents' bargaining abili…