Papers by Seohee Yoon
Auditing LLM Responses to Harmful Stereotypes Targeting Mental Health Groups (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) can exhibit imbalanced biases against vulnerable groups, but how they rationalize stereotypes and rights restrictions targeting mental health entities remains underexplored. |
| Approach: | They audit a suite of open-weight LLMs on stereotype-justification prompts tied to mental health identities. |
| Outcome: | The proposed models endorse harmful stereotypes when explicitly asked to justify them, with endorsement varying across model families, versions, and mental health conditions. |
COMPEL: Compensated Mixture-of-Experts Pruning with Expert-Layer distribution (2026.findings-acl)
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| Challenge: | Mixture-of-Experts (MoE) architectures are effective for scaling Large Language Models (LLMs) however, existing pruning methods adopt uniform pruning across layers, which fails to capture layer-wise variations in expert importance and redundancy. |
| Approach: | They propose a Mixture-of-Experts pruning method that activates only a subset of experts during inference by estimating expert importance using Fisher information. |
| Outcome: | The proposed pruning method outperforms existing pruning methods while reducing inference latency and peak GPU memory usage. |