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cs.CL2025

Circuit Distillation

Somin Wadhwa, Silvio Amir, Byron C. Wallace

Model distillation typically focuses on behavioral mimicry, where a student model is trained to replicate a teacher's output while treating its internal computations as a black box…

cs.CL2025

Elucidating Mechanisms of Demographic Bias in LLMs for Healthcare

Hiba Ahsan, Arnab Sen Sharma, Silvio Amir +2

We know from prior work that LLMs encode social biases, and that this manifests in clinical tasks. In this work we adopt tools from mechanistic interpretability to unveil sociodemo…

cs.CL2025

Who Taught You That? Tracing Teachers in Model Distillation

Somin Wadhwa, Chantal Shaib, Silvio Amir +1

Model distillation -- using outputs from a large teacher model to teach a small student model -- is a practical means of creating efficient models for a particular task. We ask: Ca…

cs.CL2024

Investigating Mysteries of CoT-Augmented Distillation

Somin Wadhwa, Silvio Amir, Byron C. Wallace

Eliciting "chain of thought" (CoT) rationales -- sequences of token that convey a "reasoning" process -- has been shown to consistently improve LLM performance on tasks like questi…

cs.CL2024

Open (Clinical) LLMs are Sensitive to Instruction Phrasings

Alberto Mario Ceballos Arroyo, Monica Munnangi, Jiuding Sun +4

Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are p…

cs.CL2024

On-the-fly Definition Augmentation of LLMs for Biomedical NER

Monica Munnangi, Sergey Feldman, Byron C Wallace +3

Despite their general capabilities, LLMs still struggle on biomedical NER tasks, which are difficult due to the presence of specialized terminology and lack of training data. In th…