1 citations · 1 across the 7 of their papers we have counts for
8 papers
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…
From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence
Jian Yang, Xianglong Liu, Weifeng Lv +68
Large language models (LLMs) have fundamentally transformed automated software development by enabling direct translation of natural language descriptions into functional code, dri…
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…
MR-UIE: Multi-Perspective Reasoning with Reinforcement Learning for Universal Information Extraction
Zhongqiu Li, Shiquan Wang, Ruiyu Fang +5
Large language models (LLMs) demonstrate robust capabilities across diverse research domains. However, their performance in universal information extraction (UIE) remains insuffici…
Mitigating Spurious Correlations Between Question and Answer via Chain-of-Thought Correctness Perception Distillation
Hongyan Xie, Yitong Yao, Yikun Ban +6
Large language models (LLMs) excel at reasoning tasks but are expensive to deploy. Thus small language models (SLMs) are fine-tuned on CoT data generated by LLMs to copy LLMs' abil…
M3TQA: Massively Multilingual Multitask Table Question Answering
Daixin Shu, Jian Yang, Zhenhe Wu +11
Tabular data is a fundamental component of real-world information systems, yet most research in table understanding remains confined to English, leaving multilingual comprehension…