3 papers
cs.CL2026
Distill to Detect: Exposing Stealth Biases in LLMs through Cartridge Distillation
Shayan Talaei, Abhinav Chinta, Devvrit Khatri +3
Language models deployed in high-stakes roles can potentially favor certain entities, brands, or viewpoints, steering user decisions at scale. Such preferential biases can be intro…
cs.CL2025
Premise-Augmented Reasoning Chains Improve Error Identification in Math reasoning with LLMs
Sagnik Mukherjee, Abhinav Chinta, Takyoung Kim +2
Chain-of-Thought (CoT) prompting enhances mathematical reasoning in large language models (LLMs) by enabling detailed step-by-step solutions. However, due to the verbosity of LLMs,…
cs.CL2024
Unsupervised Human Preference Learning
Sumuk Shashidhar, Abhinav Chinta, Vaibhav Sahai +1
Large language models demonstrate impressive reasoning abilities but struggle to provide personalized content due to their lack of individual user preference information. Existing…