2 citations · 2 across the 3 of their papers we have counts for
4 papers
How Fine-Tuning Objectives Shape Layer-Wise Information in LLM Hallucination Detection
Kaiyang Wan, Forrest Sheng Bao, Amin Ahmad +1
Under matched conditions, classifier-head fine-tuning (\textsc{cls}) paradigm consistently outperforms the LLM-as-Judge sequence-to-sequence (\textsc{s2s}) on binary hallucination…
Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards
Manveer Singh Tamber, Forrest Sheng Bao, Chenyu Xu +7
Retrieval-augmented generation (RAG) aims to reduce hallucinations by grounding responses in external context, yet large language models (LLMs) still frequently introduce unsupport…
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs
Forrest Sheng Bao, Miaoran Li, Renyi Qu +13
Summarization is one of the most common tasks performed by large language models (LLMs), especially in applications like Retrieval-Augmented Generation (RAG). However, existing eva…
Is Semantic Chunking Worth the Computational Cost?
Renyi Qu, Ruixuan Tu, Forrest Bao
Recent advances in Retrieval-Augmented Generation (RAG) systems have popularized semantic chunking, which aims to improve retrieval performance by dividing documents into semantica…