Papers by Sanmi Koyejo
SCENEBench: An Audio Understanding Benchmark Grounded in Assistive and Industrial Use Cases (2026.eacl-long)
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| Challenge: | Existing models that measure audio comprehension beyond automatic speech recognition lack performance and latency. |
| Approach: | They propose a benchmark suite that measures audio comprehension beyond automatic speech recognition . the benchmark suite includes a small human-recorded evaluation split per category . |
| Outcome: | The proposed suite measures audio comprehension beyond speech recognition . it includes a small human-recorded evaluation split per category . |
The Sound of Syntax: Finetuning and Comprehensive Evaluation of Language Models for Speech Pathology (2025.emnlp-main)
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| Challenge: | State-of-the-art multimodal language models (MLMs) show promise for supporting SLPs, but their use remains underexplored due to a limited understanding of their performance in high-stakes clinical settings. |
| Approach: | They propose a taxonomy of real-world use cases of multimodal language models in speech-language pathologies to address this gap. |
| Outcome: | The proposed model outperforms 15 state-of-the-art models in speech-language pathologies across five use cases and achieves improvements of over 30% on domain-specific data. |
Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models (2024.findings-naacl)
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| Challenge: | Existing open-source LLMs exhibit limited effectiveness in processing Vietnamese . lack of systematic benchmark datasets and metrics tailored for Vietnamese LLM evaluation exacerbates these issues. |
| Approach: | They propose to fine tune LLMs specifically for Vietnamese and develop a framework for evaluation . they find that larger models introduce more biases and uncalibrated outputs . |
| Outcome: | The proposed framework finetunes LLMs specifically for Vietnamese and provides a framework for evaluation . |
SATBench: Benchmarking LLMs’ Logical Reasoning via Automated Puzzle Generation from SAT Formulas (2025.emnlp-main)
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Anjiang Wei, Yuheng Wu, Yingjia Wan, Tarun Suresh, Huanmi Tan, Zhanke Zhou, Sanmi Koyejo, Ke Wang, Alex Aiken
| Challenge: | SATBench is a benchmark for evaluating the logical reasoning capabilities of large language models (LLMs) through logical puzzles derived from Boolean satisfiability (SAT) problems. |
| Approach: | They propose a benchmark to evaluate logical reasoning capabilities of large language models (LLMs) using logical puzzles derived from Boolean satisfiability problems. |
| Outcome: | The proposed model achieves 65.0% accuracy on hard UNSAT problems, close to the baseline of 50%. |
Fairness through Difference Awareness: Measuring Desired Group Discrimination in LLMs (2025.acl-long)
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| Challenge: | Algorithmic fairness has traditionally adopted the mathematically convenient perspective of racial color-blindness. |
| Approach: | They propose a benchmark suite of eight different scenarios to assess group difference awareness. |
| Outcome: | The proposed model demonstrates that group difference awareness is a distinct dimension to fairness where existing bias mitigation strategies may backfire. |