1 citations · 2 across the 11 of their papers we have counts for
12 papers
Concordia: Self-Improving Synthetic Tables for Federated LLMs
Jimin Huang, Duanyu Feng, Nuo Chen +8
Federated learning (FL) enables training large language models (LLMs) without sharing raw data, but adapting LLMs under strict data isolation and non-IID client distributions remai…
Unlocking Biological Workflows for Robust Protein-Text Question Answering: A Dual-Dimensional RAG Framework
Li Ding, Duanyu Feng, Chen Huang +4
Protein-Text Question Answering (QA) is crucial for interpreting biological sequences through natural language. The integration of Large Language Models (LLMs) with Retrieval-Augme…
Advancing Edge Classification through High-Dimensional Causal Modeling of Node-Edge Interplay
Duanyu Feng, Li Ding, Hongru Liang +1
Edge classification, a crucial task for graph applications, remains relatively under-explored compared to link prediction. Current methods often overlook the potential causal influ…
STAR: Semantic-Tuned and Tail-Adaptive Retriever for Graph-Augmented Generation
Shuai Li, Chen Huang, Duanyu Feng +2
To augment Large Language Models (LLMs) for multi-hop question answering, a mainstream solution within Graph Retrieval Augmented Generation (GraphRAG) leverages lightweight retriev…
AMaPO: Adaptive Margin-attached Preference Optimization for Language Model Alignment
Ruibo Deng, Duanyu Feng, Wenqiang Lei
Offline preference optimization offers a simpler and more stable alternative to RLHF for aligning language models. However, their effectiveness is critically dependent on ranking a…
Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information
Youcheng Huang, Bowen Qin, Chen Huang +3
Large Reasoning Models (LRMs) have demonstrated remarkable problem-solving abilities in mathematics, as evaluated by existing benchmarks exclusively on well-defined problems. Howev…
Cross-model Transferability among Large Language Models on the Platonic Representations of Concepts
Youcheng Huang, Chen Huang, Duanyu Feng +2
Understanding the inner workings of Large Language Models (LLMs) is a critical research frontier. Prior research has shown that a single LLM's concept representations can be captur…
Dishonesty in Helpful and Harmless Alignment
Youcheng Huang, Jingkun Tang, Duanyu Feng +4
People tell lies when seeking rewards. Large language models (LLMs) are aligned to human values with reinforcement learning where they get rewards if they satisfy human preference.…
Legend: Leveraging Representation Engineering to Annotate Safety Margin for Preference Datasets
Duanyu Feng, Bowen Qin, Chen Huang +3
The success of the reward model in distinguishing between responses with subtle safety differences depends critically on the high-quality preference dataset, which should capture t…
Towards Understanding the Influence of Reward Margin on Preference Model Performance
Bowen Qin, Duanyu Feng, Xi Yang
Reinforcement Learning from Human Feedback (RLHF) is a widely used framework for the training of language models. However, the process of using RLHF to develop a language model tha…
Towards Analyzing and Understanding the Limitations of DPO: A Theoretical Perspective
Duanyu Feng, Bowen Qin, Chen Huang +2
Direct Preference Optimization (DPO), which derives reward signals directly from pairwise preference data, has shown its effectiveness on aligning Large Language Models (LLMs) with…
DREditor: An Time-efficient Approach for Building a Domain-specific Dense Retrieval Model
Chen Huang, Duanyu Feng, Wenqiang Lei +1
Deploying dense retrieval models efficiently is becoming increasingly important across various industries. This is especially true for enterprise search services, where customizing…