Papers by Forrest Sheng Bao
Is Semantic Chunking Worth the Computational Cost? (2025.findings-naacl)
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| Challenge: | Recent advances in Retrieval-Augmented Generation (RAG) systems have popularized semantic chunking. |
| Approach: | They evaluate the effectiveness of semantic chunking using three common retrieval tasks . they find that the computational costs associated with semantic chunks are not justified by consistent performance gains. |
| Outcome: | The proposed semantic chunking approach is not able to deliver consistent performance gains in three retrieval-related tasks. |
Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards (2025.emnlp-industry)
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Manveer Singh Tamber, Forrest Sheng Bao, Chenyu Xu, Ge Luo, Suleman Kazi, Minseok Bae, Miaoran Li, Ofer Mendelevitch, Renyi Qu, Jimmy Lin
| Challenge: | Large language models (LLMs) excel in various tasks, but often produce hallucinations . retrieved contexts, misrepresent information, or generate outright contradictions . |
| Approach: | They propose a framework that measures hallucination faithfulness of large language models . they introduce a leaderboard that leverages diverse human-annotated hallucinian examples . |
| Outcome: | The proposed framework improves hallucination evaluations by leveraging human-annotated examples. |
PrefScore: Pairwise Preference Learning for Reference-free Summarization Quality Assessment (2022.coling-1)
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| Challenge: | Existing studies on summarization evaluation without a human-written reference summary have shown high correlations with human ratings. |
| Approach: | They propose to judge summary quality by learning preference rank from corrupted summaries . they use Bradley-Terry power ranking model to learn preference rank . |
| Outcome: | Experiments on several datasets show that the proposed model can produce scores highly correlated with human ratings. |
Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators (N18-2)
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| Challenge: | Recent studies on review helpfulness prediction require labeled samples for each domain/category of interest. |
| Approach: | They propose a convolutional neural network based model which leverages word-level and character-based representations to transfer knowledge between domains. |
| Outcome: | The proposed model outperforms the state-of-the-art on the Amazon product review dataset. |
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs (2025.naacl-short)
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Forrest Sheng Bao, Miaoran Li, Renyi Qu, Ge Luo, Erana Wan, Yujia Tang, Weisi Fan, Manveer Singh Tamber, Suleman Kazi, Vivek Sourabh, Mike Qi, Ruixuan Tu, Chenyu Xu, Matthew Gonzales, Ofer Mendelevitch, Amin Ahmad
| Challenge: | Existing evaluations of hallucinations in large language models suffer from a lack of diversity and recency in the LLM and LLM families considered. |
| Approach: | They propose a summarization hallucination benchmark that challenges models to disagree on hallucines . they use models to generate answers or summaries from textual input . |
| Outcome: | The proposed model combines the best of 10 modern LLMs with ground truth annotations. |