Papers by Manveer Singh Tamber
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. |
Can’t Hide Behind the API: Stealing Black-Box Commercial Embedding Models (2025.findings-naacl)
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| Challenge: | a new study examines the retrieval effectiveness of commercial embedding models . robert mcgahey: can commercial embeds be "stolen" using distillation techniques? he says stealing models can offer benefits to different actors, including reduced costs and security . |
| Approach: | They propose to "steal" embedding models by training thief models on text–embedding pairs . they replicate retrieval effectiveness of commercial embeddable models with a cost of under $300 . |
| Outcome: | The proposed methods replicate retrieval effectiveness of commercial embedding models with under $300 . authors suggest measures to mitigate risk of model theft. |
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. |