4 citations · 4 across the 11 of their papers we have counts for
9 papers · 1 filter
BridgeAlign: Bridging Preference Alignment for Humanities and Social Sciences
Ru Peng, Haokai Xu, Xijun Gu +11
While data synthesis for large language models (LLMs) is prevalent, it primarily targets domains with verifiable answers, overlooking open-ended humanities and social sciences (HSS…
HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs
Ru Peng, Tianyu Zhao, Xijun Gu +9
High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. Data synthesis is a viable alternative and succeeds on closed tasks, yet the hum…
On Predicting the Post-training Potential of Pre-trained LLMs
Xiaoyuan Li, Yubo Ma, Kexin Yang +5
The performance of Large Language Models (LLMs) on downstream tasks is fundamentally constrained by the capabilities acquired during pre-training. However, traditional benchmarks l…
ClinConsensus: A Physician-Calibrated Benchmark for Evaluating Clinical Rubric Coverage in Chinese Medical LLMs
Xiang Zheng, Han Li, Wenjie Luo +15
Open-ended medical LLM evaluation remains weakly grounded in physician-calibrated coverage of clinically relevant response criteria, especially in localized clinical settings. We i…
OPUS: Towards Efficient and Principled Data Selection in Large Language Model Pre-training in Every Iteration
Shaobo Wang, Xuan Ouyang, Tianyi Xu +9
As high-quality public text approaches exhaustion, a phenomenon known as the Data Wall, pre-training is shifting from more tokens to better tokens. However, existing methods either…
PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice
Yuzhen Shi, Huanghai Liu, Yiran Hu +27
As large language models (LLMs) are increasingly applied to legal domain-specific tasks, evaluating their ability to perform legal work in real-world settings has become essential.…