2 citations · 4 across the 13 of their papers we have counts for
12 papers · 1 filter
MoEGen: Mixture-of-Experts for Instance-Adaptive LoRA Generation
Yiming Zeng, Lei Lu, Zexin Li +9
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…
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…
T2R-bench: A Benchmark for Generating Article-Level Reports from Real World Industrial Tables
Jie Zhang, Changzai Pan, Kaiwen Wei +12
Extensive research has been conducted to explore the capabilities of large language models (LLMs) in table reasoning. However, the essential task of transforming tables information…
TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering
Sishi Xiong, Ziyang He, Zhongjiang He +6
While large language models (LLMs) have shown promise in the table question answering (TQA) task through prompt engineering, they face challenges in industrial applications, includ…
Technical Report of TeleChat2, TeleChat2.5 and T1
Zihan Wang, Xinzhang Liu, Yitong Yao +35
We introduce the latest series of TeleChat models: \textbf{TeleChat2}, \textbf{TeleChat2.5}, and \textbf{T1}, offering a significant upgrade over their predecessor, TeleChat. Despi…