72 citations · 181 across the 44 of their papers we have counts for
25 papers · 1 filter
AceSearcher: Bootstrapping Reasoning and Search for LLMs via Reinforced Self-Play
Ran Xu, Yuchen Zhuang, Zihan Dong +7
Search-augmented LLMs often struggle with complex reasoning tasks due to ineffective multi-hop retrieval and limited reasoning ability. We propose AceSearcher, a cooperative self-p…
RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation
Ran Xu, Yuchen Zhuang, Yue Yu +3
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge retrieved at inference time. While RAG demonstrates strong performance…
WorkForceAgent-R1: Incentivizing Reasoning Capability in LLM-based Web Agents via Reinforcement Learning
Yuchen Zhuang, Di Jin, Jiaao Chen +3
Large language models (LLMs)-empowered web agents enables automating complex, real-time web navigation tasks in enterprise environments. However, existing web agents relying on sup…
MedAgentGym: A Scalable Agentic Training Environment for Code-Centric Reasoning in Biomedical Data Science
Ran Xu, Yuchen Zhuang, Yishan Zhong +13
We introduce MedAgentGym, a scalable and interactive training environment designed to enhance coding-based biomedical reasoning capabilities in large language model (LLM) agents. M…
Towards Better Instruction Following Retrieval Models
Yuchen Zhuang, Aaron Trinh, Rushi Qiang +4
Modern information retrieval (IR) models, trained exclusively on standard <query, passage> pairs, struggle to effectively interpret and follow explicit user instructions. We introd…
Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration
Ran Xu, Wenqi Shi, Yuchen Zhuang +4
Retrieval-Augmented Generation (RAG) systems often struggle to handle multi-hop question-answering tasks accurately due to irrelevant context retrieval and limited complex reasonin…
Hephaestus: Improving Fundamental Agent Capabilities of Large Language Models through Continual Pre-Training
Yuchen Zhuang, Jingfeng Yang, Haoming Jiang +16
Due to the scarcity of agent-oriented pre-training data, LLM-based autonomous agents typically rely on complex prompting or extensive fine-tuning, which often fails to introduce ne…
HYDRA: Model Factorization Framework for Black-Box LLM Personalization
Yuchen Zhuang, Haotian Sun, Yue Yu +4
Personalization has emerged as a critical research area in modern intelligent systems, focusing on mining users' behavioral history and adapting to their preferences for delivering…
MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning
Wenqi Shi, Ran Xu, Yuchen Zhuang +5
Despite their improved capabilities in generation and reasoning, adapting large language models (LLMs) to the biomedical domain remains challenging due to their immense size and co…
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers
Ran Xu, Wenqi Shi, Yue Yu +6
Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedical tasks but still challenging due to the deficiency of sufficient public…
RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records
Ran Xu, Wenqi Shi, Yue Yu +5
We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources, conver…
HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification
Vidit Jain, Mukund Rungta, Yuchen Zhuang +5
Hierarchical text classification (HTC) is a complex subtask under multi-label text classification, characterized by a hierarchical label taxonomy and data imbalance. The best-perfo…
BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models
Haotian Sun, Yuchen Zhuang, Wei Wei +2
Adapting state-of-the-art Large Language Models (LLMs) like GPT-4 and Gemini for specific tasks is challenging. Due to the opacity in their parameters, embeddings, and even output…
TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidance
Haorui Wang, Rongzhi Zhang, Yinghao Li +4
Large Language Models (LLMs) have recently showcased remarkable reasoning abilities. However, larger models often surpass their smaller counterparts in reasoning tasks, posing the…
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records
Wenqi Shi, Ran Xu, Yuchen Zhuang +7
Large language models (LLMs) have demonstrated exceptional capabilities in planning and tool utilization as autonomous agents, but few have been developed for medical problem-solvi…
PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature
Jerry Junyang Cheung, Yuchen Zhuang, Yinghao Li +5
Scientific information extraction (SciIE), which aims to automatically extract information from scientific literature, is becoming more important than ever. However, there are no e…
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models
Ran Xu, Hejie Cui, Yue Yu +6
Clinical natural language processing requires methods that can address domain-specific challenges, such as complex medical terminology and clinical contexts. Recently, large langua…
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search
Yuchen Zhuang, Xiang Chen, Tong Yu +5
Large language models (LLMs) have demonstrated powerful decision-making and planning capabilities in solving complicated real-world problems. LLM-based autonomous agents can intera…
ToolQA: A Dataset for LLM Question Answering with External Tools
Yuchen Zhuang, Yue Yu, Kuan Wang +2
Large Language Models (LLMs) have demonstrated impressive performance in various NLP tasks, but they still suffer from challenges such as hallucination and weak numerical reasoning…
DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative Modeling
Yuchen Zhuang, Yue Yu, Lingkai Kong +2
Learning from noisy labels is a challenge that arises in many real-world applications where training data can contain incorrect or corrupted labels. When fine-tuning language model…
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
Yue Yu, Yuchen Zhuang, Jieyu Zhang +5
Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks. While previous research has explored diff…
AdaPlanner: Adaptive Planning from Feedback with Language Models
Haotian Sun, Yuchen Zhuang, Lingkai Kong +2
Large language models (LLMs) have recently demonstrated the potential in acting as autonomous agents for sequential decision-making tasks. However, most existing methods either tak…
ReGen: Zero-Shot Text Classification via Training Data Generation with Progressive Dense Retrieval
Yue Yu, Yuchen Zhuang, Rongzhi Zhang +3
With the development of large language models (LLMs), zero-shot learning has attracted much attention for various NLP tasks. Different from prior works that generate training data…
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select
Yuchen Zhuang, Yinghao Li, Jerry Junyang Cheung +5
We study the problem of extracting N-ary relation tuples from scientific articles. This task is challenging because the target knowledge tuples can reside in multiple parts and mod…