52 citations · 76 across the 69 of their papers we have counts for
14 papers · 1 filter
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models
Yingshui Tan, Boren Zheng, Baihui Zheng +9
With the rapid advancement of Large Language Models (LLMs), significant safety concerns have emerged. Fundamentally, the safety of large language models is closely linked to the ac…
WiS Platform: Enhancing Evaluation of LLM-Based Multi-Agent Systems Through Game-Based Analysis
Chengwei Hu, Jianhui Zheng, Yancheng He +7
Recent advancements in autonomous multi-agent systems (MAS) based on large language models (LLMs) have enhanced the application scenarios and improved the capability of LLMs to han…
Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models
Yancheng He, Shilong Li, Jiaheng Liu +15
New LLM evaluation benchmarks are important to align with the rapid development of Large Language Models (LLMs). In this work, we present Chinese SimpleQA, the first comprehensive…
Adaptive Segment-level Reward: Bridging the Gap Between Action and Reward Space in Alignment
Yanshi Li, Shaopan Xiong, Gengru Chen +6
Reinforcement Learning (RL) has proven highly effective in aligning Large Language Models (LLMs) with human preferences. Typical RL methods optimize under an overall sequence rewar…
M2rc-Eval: Massively Multilingual Repository-level Code Completion Evaluation
Jiaheng Liu, Ken Deng, Congnan Liu +13
Repository-level code completion has drawn great attention in software engineering, and several benchmark datasets have been introduced. However, existing repository-level code com…
2D-DPO: Scaling Direct Preference Optimization with 2-Dimensional Supervision
Shilong Li, Yancheng He, Hui Huang +7
Recent advancements in Direct Preference Optimization (DPO) have significantly enhanced the alignment of Large Language Models (LLMs) with human preferences, owing to its simplicit…
MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Models
Pei Wang, Yanan Wu, Zekun Wang +12
Large Language Models (LLMs) have displayed massive improvements in reasoning and decision-making skills and can hold natural conversations with users. Recently, many tool-use benc…
DDK: Distilling Domain Knowledge for Efficient Large Language Models
Jiaheng Liu, Chenchen Zhang, Jinyang Guo +13
Despite the advanced intelligence abilities of large language models (LLMs) in various applications, they still face significant computational and storage demands. Knowledge Distil…
D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models
Haoran Que, Jiaheng Liu, Ge Zhang +13
Continual Pre-Training (CPT) on Large Language Models (LLMs) has been widely used to expand the model's fundamental understanding of specific downstream domains (e.g., math and cod…
GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models
Shilong Li, Yancheng He, Hangyu Guo +9
Long-context capabilities are essential for large language models (LLMs) to tackle complex and long-input tasks. Despite numerous efforts made to optimize LLMs for long contexts, c…
R2C2-Coder: Enhancing and Benchmarking Real-world Repository-level Code Completion Abilities of Code Large Language Models
Ken Deng, Jiaheng Liu, He Zhu +13
Code completion models have made significant progress in recent years. Recently, repository-level code completion has drawn more attention in modern software development, and sever…
ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models
Yanan Wu, Jie Liu, Xingyuan Bu +10
This paper introduces ConceptMath, a bilingual (English and Chinese), fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models (LLMs). Unl…
E^2-LLM: Efficient and Extreme Length Extension of Large Language Models
Jiaheng Liu, Zhiqi Bai, Yuanxing Zhang +11
Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources. Existing long-context extension methods usually…
MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues
Ge Bai, Jie Liu, Xingyuan Bu +8
The advent of Large Language Models (LLMs) has drastically enhanced dialogue systems. However, comprehensively evaluating the dialogue abilities of LLMs remains a challenge. Previo…