Papers by Seyedali Mohammadi
Experiments or Outcomes? Probing Scientific Feasibility in Large Language Models (2026.acl-short)
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| Challenge: | Scientific feasibility assessment asks whether a claim aligns with established knowledge and whether experimental evidence could support or refute it. |
| Approach: | They frame scientific feasibility assessment as a diagnostic reasoning task . given a hypothesis, a model predicts feasible or infeasible and justifies its decision . they evaluate large language models under controlled knowledge conditions . |
| Outcome: | The results show that providing outcome evidence is more reliable than providing experiment descriptions. |
Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions (2025.emnlp-main)
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Seyedali Mohammadi, Bhaskara Hanuma Vedula, Hemank Lamba, Edward Raff, Ponnurangam Kumaraguru, Francis Ferraro, Manas Gaur
| Challenge: | Exact label definitions are considered as clues to disambiguate unclear labels, helping models perform their tasks more effectively. |
| Approach: | They conducted controlled experiments on multiple explanation benchmark datasets and label definition conditions using expert-curated, LLM-generated, perturbed, and swapped definitions. |
| Outcome: | The results suggest that models often default to internal representations, particularly in general tasks, while domain-specific tasks benefit more from explicit definitions. |
LingVarBench: Benchmarking LLMs on Entity Recognitions and Linguistic Verbalization Patterns in Phone-Call Transcripts (2026.eacl-industry)
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| Challenge: | Existing methods degrade under disfluencies, interruptions, and speaker overlap, yet large real-call corpora are rarely shareable. |
| Approach: | They propose a benchmark and semantic synthetic data generation pipeline that generates linguistically varied training data via (1) LLM-sampled entity values, (2) curated linguistic verbalization patterns covering diverse disfluencies and entity-specific readout styles, and (3) a value–transcript consistency filter. |
| Outcome: | The proposed pipeline outperforms zero-shot baselines and matches or closely approaches human-tuned prompts on real customer transcripts. |