9 citations · 10 across the 4 of their papers we have counts for
4 papers
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…
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,…
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…
Democratizing LLMs: An Exploration of Cost-Performance Trade-offs in Self-Refined Open-Source Models
Sumuk Shashidhar, Abhinav Chinta, Vaibhav Sahai +2
The dominance of proprietary LLMs has led to restricted access and raised information privacy concerns. High-performing open-source alternatives are crucial for information-sensiti…