15 citations · 26 across the 17 of their papers we have counts for
7 papers · 1 filter
Language Models can Self-Lengthen to Generate Long Texts
Shanghaoran Quan, Tianyi Tang, Bowen Yu +7
Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to process long contexts, yet a notable gap remains in generating long, aligned output…
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…
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…
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…
Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
An Yang, Beichen Zhang, Binyuan Hui +13
In this report, we present a series of math-specific large language models: Qwen2.5-Math and Qwen2.5-Math-Instruct-1.5B/7B/72B. The core innovation of the Qwen2.5 series lies in in…
LLM Critics Help Catch Bugs in Mathematics: Towards a Better Mathematical Verifier with Natural Language Feedback
Bofei Gao, Zefan Cai, Runxin Xu +10
In recent progress, mathematical verifiers have achieved success in mathematical reasoning tasks by validating the correctness of solutions generated by policy models. However, exi…
PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling
Zefan Cai, Yichi Zhang, Bofei Gao +8
In this study, we investigate whether attention-based information flow inside large language models (LLMs) is aggregated through noticeable patterns for long context processing. Ou…