5 papers
RankEvolve: Automating the Discovery of Retrieval Algorithms via LLM-Driven Evolution
Jinming Nian, Fangchen Li, Dae Hoon Park +1
Retrieval algorithms like BM25 and query likelihood with Dirichlet smoothing remain strong and efficient first-stage rankers, yet improvements have mostly relied on parameter tunin…
Submodular Evaluation Subset Selection in Automatic Prompt Optimization
Jinming Nian, Zhiyuan Peng, Hongwei Shang +2
Automatic prompt optimization reduces manual prompt engineering, but relies on task performance measured on a small, often randomly sampled evaluation subset as its main source of…
Does Reasoning Introduce Bias? A Study of Social Bias Evaluation and Mitigation in LLM Reasoning
Xuyang Wu, Jinming Nian, Ting-Ruen Wei +3
Recent advances in large language models (LLMs) have enabled automatic generation of chain-of-thought (CoT) reasoning, leading to strong performance on tasks such as math and code.…
ELOQ: Resources for Enhancing LLM Detection of Out-of-Scope Questions
Zhiyuan Peng, Jinming Nian, Alexandre Evfimievski +1
Retrieval-augmented generation (RAG) has become integral to large language models (LLMs), particularly for conversational AI systems where user questions may reference knowledge be…
W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering
Jinming Nian, Zhiyuan Peng, Qifan Wang +1
In knowledge-intensive tasks such as open-domain question answering (OpenQA), large language models (LLMs) often struggle to generate factual answers, relying solely on their inter…