4 papers
OpenGenAlign: A Preference Dataset and Benchmark for Trustworthy Reward Modeling in Open-Ended, Long-Context Generation
Hanning Zhang, Juntong Song, Juno Zhu +3
Reward Modeling is critical in evaluating and improving the generation of Large Language Models (LLMs). While numerous recent works have shown its feasibility in improving safety,…
Contextual Relevance and Adaptive Sampling for LLM-Based Document Reranking
Jerry Huang, Siddarth Madala, Cheng Niu +2
Reranking algorithms have made progress in improving document retrieval quality by efficiently aggregating relevance judgments generated by large language models (LLMs). However, i…
DuaShepherd: Integrating Stepwise Correctness and Potential Rewards for Mathematical Reasoning
Yuanhao Wu, Juntong Song, Hanning Zhang +2
In this paper, we propose DuaShepherd, a novel reward modeling framework that integrates two complementary reward signals, correctness and potential, to enhance the mathematical re…
RAG-RL: Advancing Retrieval-Augmented Generation via RL and Curriculum Learning
Jerry Huang, Siddarth Madala, Risham Sidhu +4
Retrieval-augmented generation (RAG) systems rely on retrieval models for identifying relevant contexts and answer generation models for utilizing those contexts. However, retrieve…