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Prompt PerturbAItions: Mechanisms of LLM Response Inconsistencies
Soorya Ram Shimgekar, Agam Goyal, Amruta Parulekar +6
Large language models (LLMs) are increasingly deployed in conversational settings where user phrasing ranges from polite to casual to toxic, yet less is known about whether such le…
CausalDetox: Causal Head Selection and Intervention for Language Model Detoxification
Yian Wang, Yuen Chen, Agam Goyal +1
Large language models (LLMs) frequently generate toxic content, posing significant risks for safe deployment. Current mitigation strategies often degrade generation quality or requ…
From Plausible to Causal: Counterfactual Semantics for Policy Evaluation in Simulated Online Communities
Agam Goyal, Yian Wang, Eshwar Chandrasekharan +1
LLM-based social simulations can generate believable community interactions, enabling ``policy wind tunnels'' where governance interventions are tested before deployment. But belie…
Breaking Bad Tokens: Detoxification of LLMs Using Sparse Autoencoders
Agam Goyal, Vedant Rathi, William Yeh +3
Large language models (LLMs) are now ubiquitous in user-facing applications, yet they still generate undesirable toxic outputs, including profanity, vulgarity, and derogatory remar…