6 citations · 11 across the 7 of their papers we have counts for
7 papers
ANCHOR: An External LLM-Driven Supervisory Module Facilitating Healthy Evolution in Self-Evolving Systems
Dianxing Shi, Bowen Wang, Junqi He +2
Self-evolving agents improve through continual self-play and self-generated learning signals, but their internally generated tasks and verifier signals provide limited coverage of…
KnowMT-Bench: Benchmarking Knowledge-Intensive Long-Form Question Answering in Multi-Turn Dialogues
Junhao Chen, Yu Huang, Siyuan Li +7
Multi-Turn Long-Form Question Answering (MT-LFQA) is a key application paradigm of Large Language Models (LLMs) in knowledge-intensive domains. However, existing benchmarks are lim…
Interpreting Fedspeak with Confidence: A LLM-Based Uncertainty-Aware Framework Guided by Monetary Policy Transmission Paths
Rui Yao, Qi Chai, Jinhai Yao +4
"Fedspeak", the stylized and often nuanced language used by the U.S. Federal Reserve, encodes implicit policy signals and strategic stances. The Federal Open Market Committee strat…
ReLayout: Towards Real-World Document Understanding via Layout-enhanced Pre-training
Zhouqiang Jiang, Bowen Wang, Junhao Chen +1
Recent approaches for visually-rich document understanding (VrDU) uses manually annotated semantic groups, where a semantic group encompasses all semantically relevant but not obvi…
Putting People in LLMs' Shoes: Generating Better Answers via Question Rewriter
Junhao Chen, Bowen Wang, Zhouqiang Jiang +1
Large Language Models (LLMs) have demonstrated significant capabilities, particularly in the domain of question answering (QA). However, their effectiveness in QA is often undermin…
DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models
Bowen Wang, Jiuyang Chang, Yiming Qian +6
Large language models (LLMs) have recently showcased remarkable capabilities, spanning a wide range of tasks and applications, including those in the medical domain. Models like GP…
IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuning
Feiyu Zhang, Liangzhi Li, Junhao Chen +3
With the increasing size of pre-trained language models (PLMs), fine-tuning all the parameters in the model is not efficient, especially when there are a large number of downstream…