Papers by Suleman Kazi
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. |
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. |
MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems (2025.naacl-long)
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| Challenge: | Traditional retrieval-augmented generation benchmarks use heuristics as the ground truth for evaluation, but require an expensive large language model (LLM) as a judge for a reliable evaluation. |
| Approach: | They propose to use large language models as a judge for retrieval-augmented generation benchmarks . they use heuristic metrics as input and a large language model as heuriistic input . |
| Outcome: | The proposed method couples heuristic features with large language models as judge for evaluation. |