36 citations · 69 across the 36 of their papers we have counts for
10 papers · 1 filter
DiSRouter: Distributed Self-Routing for LLM Selections
Hang Zheng, Hongshen Xu, Yongkai Lin +3
The proliferation of Large Language Models (LLMs) has created a diverse ecosystem of models with highly varying performance and costs, necessitating effective query routing to bala…
AirQA: A Comprehensive QA Dataset for AI Research with Instance-Level Evaluation
Tiancheng Huang, Ruisheng Cao, Yuxin Zhang +8
The growing volume of academic papers has made it increasingly difficult for researchers to efficiently extract key information. While large language models (LLMs) based agents are…
ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge
Zihan Zhao, Ziping Wan, Lu Chen +12
Atomized chemical knowledge, such as functional group information of molecules and reactions, plays a pivotal intermediate role in the reasoning process that connects molecular str…
RetroDFM-R: Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning
Situo Zhang, Hanqi Li, Lu Chen +7
Retrosynthetic planning is a cornerstone of organic synthesis and drug discovery. Yet existing AI methods often rely on pattern matching rather than transferable chemical reasoning…
NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering
Ruisheng Cao, Hanchong Zhang, Tiancheng Huang +8
The increasing number of academic papers poses significant challenges for researchers to efficiently acquire key details. While retrieval augmented generation (RAG) shows great pro…
ProgRM: Build Better GUI Agents with Progress Rewards
Danyang Zhang, Situo Zhang, Ziyue Yang +5
LLM-based (Large Language Model) GUI (Graphical User Interface) agents can potentially reshape our daily lives significantly. However, current LLM-based GUI agents suffer from the…
Delusions of Large Language Models
Hongshen Xu, Zixv yang, Zichen Zhu +7
Large Language Models often generate factually incorrect but plausible outputs, known as hallucinations. We identify a more insidious phenomenon, LLM delusion, defined as high beli…
Alignment for Efficient Tool Calling of Large Language Models
Hongshen Xu, Zihan Wang, Zichen Zhu +4
Recent advancements in tool learning have enabled large language models (LLMs) to integrate external tools, enhancing their task performance by expanding their knowledge boundaries…
Task-Specific Data Selection for Instruction Tuning via Monosemantic Neuronal Activations
Da Ma, Gonghu Shang, Zhi Chen +6
Instruction tuning improves the ability of large language models (LLMs) to follow diverse human instructions, but achieving strong performance on specific target tasks remains chal…
Enhancing LLM Reliability via Explicit Knowledge Boundary Modeling
Hang Zheng, Hongshen Xu, Yuncong Liu +3
Large language models (LLMs) are prone to hallucination stemming from misaligned self-awareness, particularly when processing queries exceeding their knowledge boundaries. While ex…