5 papers
The Table Says Otherwise: Testing LLMs with Counterfactual Relational Data
Xinzhi Wang, Chunwei Liu
Large language models (LLMs) are increasingly used to answer natural-language questions over structured data. However, when a table contains familiar real-world facts, it is unclea…
SAGE: Selective Attention-Guided Extraction for Token-Efficient Document Indexing
Xinzhi Wang, Peter Baile Chen, Gerardo Vitagliano +5
Large language models with long context windows can answer complex questions directly from full-length academic, technical, and policy documents, but passing entire documents is of…
ACEBench: Who Wins the Match Point in Tool Usage?
Chen Chen, Xinlong Hao, Weiwen Liu +13
Large Language Models (LLMs) have demonstrated significant potential in decision-making and reasoning, particularly when integrated with various tools to effectively solve complex…
VideoRewardBench: Comprehensive Evaluation of Multimodal Reward Models for Video Understanding
Zhihong Zhang, Xiaojian Huang, Jin Xu +4
Multimodal reward models (MRMs) play a crucial role in the training, inference, and evaluation of Large Vision Language Models (LVLMs) by assessing response quality. However, exist…
ToolACE: Winning the Points of LLM Function Calling
Weiwen Liu, Xu Huang, Xingshan Zeng +24
Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability. Ho…