2 citations · 3 across the 16 of their papers we have counts for
8 papers · 1 filter
I-WebGenBench : Evaluating Interactivity in LLM-Generated Scientific Web Applications
Dasen Dai, Biao Wu, Meng Fang +2
Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding. However, existing document agents mainly trans…
PaperVoyager : Building Interactive Web with Visual Language Models
Dasen Dai, Biao Wu, Meng Fang +1
Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding. However, existing document agents mainly trans…
Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing
Baode Wang, Biao Wu, Weizhen Li +8
Document parsing from scanned images into structured formats remains a significant challenge due to its complexly intertwined elements such as text paragraphs, figures, formulas, a…
Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering
Wanqi Yang, Yanda Li, Meng Fang +1
Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts, encompassing multiple time-evolving facts, to address time-sensitive quest…
RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering
Zihan Zhang, Meng Fang, Ling Chen
Adaptive retrieval-augmented generation (ARAG) aims to dynamically determine the necessity of retrieval for queries instead of retrieving indiscriminately to enhance the efficiency…
Turn-Level Active Learning for Dialogue State Tracking
Zihan Zhang, Meng Fang, Fanghua Ye +2
Dialogue state tracking (DST) plays an important role in task-oriented dialogue systems. However, collecting a large amount of turn-by-turn annotated dialogue data is costly and in…
CITB: A Benchmark for Continual Instruction Tuning
Zihan Zhang, Meng Fang, Ling Chen +1
Continual learning (CL) is a paradigm that aims to replicate the human ability to learn and accumulate knowledge continually without forgetting previous knowledge and transferring…
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances
Zihan Zhang, Meng Fang, Ling Chen +2
Although large language models (LLMs) are impressive in solving various tasks, they can quickly be outdated after deployment. Maintaining their up-to-date status is a pressing conc…