Papers by J. Ben Tamo
Tree-of-Evidence: Efficient "System 2" Search for Faithful Multimodal Grounding (2026.findings-acl)
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| Challenge: | Attention-based methods fail to faithfully represent the model’s decision process when integrating heterogeneous modalities. |
| Approach: | They propose an inference-time search algorithm that frames interpretability as a discrete optimization problem. |
| Outcome: | The proposed algorithm retains over 98% of full-model AUROC with as few as five evidence units and achieves higher decision agreement and lower error than LIME, SHAP, saliency, and concept-bottleneck baselines under sparse budgets. |
MetaBench: A Multi-task Benchmark for Assessing LLMs in Metabolomics (2026.acl-long)
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Yuxing Lu, Xukai Zhao, J. Ben Tamo, Micky C. Nnamdi, Rui Peng, Shuang Zeng, Xingyu Hu, Jinzhuo Wang, May Dongmei Wang
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities on general text, but their proficiency in specialized scientific domains remains uncharacterized. |
| Approach: | They evaluate the capabilities of large language models in metabolomics research using MetaBench . they found that models perform well on text generation tasks, but cross-database identifier grounding remains challenging . |
| Outcome: | The evaluation of 25 open- and closed-source LLMs reveals distinct performance patterns across metabolomics tasks. |