2 citations · 4 across the 13 of their papers we have counts for
4 papers · 1 filter
MoEGen: Mixture-of-Experts for Instance-Adaptive LoRA Generation
Yiming Zeng, Lei Lu, Zexin Li +10
Parameter-efficient fine-tuning (PEFT) enables efficient adaptation of large language models, but existing MoE-based PEFT methods typically improve capacity by storing multiple ful…
CRAFT: A Unified Counterfactual Reasoning Framework for Tabular Question Answering and Fact Verification
Chenshuo Pan, Yu Zhao, Jie Zhang +7
Table reasoning remains challenging for large language models (LLMs), particularly in tasks that require multi-step inference over long and structured tables. Existing approaches p…
Pairwise Preference Reward and Group-Based Diversity Enhancement for Superior Open-Ended Generation
Guining Cao, Jiaxin Peng, Chu Zeng +3
Current reinforcement learning(RL) methods are broadly applicable and powerful in verifiable settings where scalar rewards can be provided. However, in open-ended generation tasks,…
ReasonTabQA: A Comprehensive Benchmark for Table Question Answering from Real World Industrial Scenarios
Changzai Pan, Jie Zhang, Kaiwen Wei +15
Recent advancements in Large Language Models (LLMs) have significantly catalyzed table-based question answering (TableQA). However, existing TableQA benchmarks often overlook the i…