1 citations · 3 across the 8 of their papers we have counts for
10 papers
Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering
Wuzhenghong Wen, Bowen Zhou, Jinwen Huang +5
Question Answering over Temporal Knowledge Graphs (TKGQA) has attracted growing interest for handling time-sensitive queries. However, existing methods still struggle with: 1) weak…
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…
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…
Multilingual Multimodal Software Developer for Code Generation
Linzheng Chai, Jian Yang, Shukai Liu +12
The rapid advancement of Large Language Models (LLMs) has significantly improved code generation, yet most models remain text-only, neglecting crucial visual aids like diagrams and…
Table-R1: Region-based Reinforcement Learning for Table Understanding
Zhenhe Wu, Jian Yang, Zhongjiang He +9
Tables present unique challenges for language models due to their structured row-column interactions, necessitating specialized approaches for effective comprehension. While large…
SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models
Xianfu Cheng, Wei Zhang, Shiwei Zhang +16
The increasing application of multi-modal large language models (MLLMs) across various sectors have spotlighted the essence of their output reliability and accuracy, particularly t…
TableBench: A Comprehensive and Complex Benchmark for Table Question Answering
Xianjie Wu, Jian Yang, Linzheng Chai +10
Recent advancements in Large Language Models (LLMs) have markedly enhanced the interpretation and processing of tabular data, introducing previously unimaginable capabilities. Desp…
Raw Text is All you Need: Knowledge-intensive Multi-turn Instruction Tuning for Large Language Model
Xia Hou, Qifeng Li, Jian Yang +8
Instruction tuning as an effective technique aligns the outputs of large language models (LLMs) with human preference. But how to generate the seasonal multi-turn dialogues from ra…
McEval: Massively Multilingual Code Evaluation
Linzheng Chai, Shukai Liu, Jian Yang +15
Code large language models (LLMs) have shown remarkable advances in code understanding, completion, and generation tasks. Programming benchmarks, comprised of a selection of code c…
Unleashing Potential of Evidence in Knowledge-Intensive Dialogue Generation
Xianjie Wu, Jian Yang, Tongliang Li +4
Incorporating external knowledge into dialogue generation (KIDG) is crucial for improving the correctness of response, where evidence fragments serve as knowledgeable snippets supp…