5 citations · 21 across the 17 of their papers we have counts for
7 papers · 1 filter
Targeting Pivotal Decisions for Credit Assignment in Agentic Reinforcement Learning
Dongwon Jung, Hemanth Neelgund Ramesh, Yifan Wang +7
Group Relative Policy Optimization (GRPO) has become a promising approach for training large language model agents. However, its uniform assignment of trajectory-level advantages t…
Attention Speaks Volumes: Localizing and Mitigating Bias in Language Models
Rishabh Adiga, Besmira Nushi, Varun Chandrasekaran
We explore the internal mechanisms of how bias emerges in large language models (LLMs) when provided with ambiguous comparative prompts: inputs that compare or enforce choosing bet…
Generative Monoculture in Large Language Models
Fan Wu, Emily Black, Varun Chandrasekaran
We introduce {\em generative monoculture}, a behavior observed in large language models (LLMs) characterized by a significant narrowing of model output diversity relative to availa…
Designing Informative Metrics for Few-Shot Example Selection
Rishabh Adiga, Lakshminarayanan Subramanian, Varun Chandrasekaran
Pretrained language models (PLMs) have shown remarkable few-shot learning capabilities when provided with properly formatted examples. However, selecting the "best" examples remain…
Teaching Language Models to Hallucinate Less with Synthetic Tasks
Erik Jones, Hamid Palangi, Clarisse Simões +5
Large language models (LLMs) frequently hallucinate on abstractive summarization tasks such as document-based question-answering, meeting summarization, and clinical report generat…
Diversity of Thought Improves Reasoning Abilities of LLMs
Ranjita Naik, Varun Chandrasekaran, Mert Yuksekgonul +2
Large language models (LLMs) are documented to struggle in settings that require complex reasoning. Nevertheless, instructing the model to break down the problem into smaller reaso…
Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models
Mert Yuksekgonul, Varun Chandrasekaran, Erik Jones +5
We investigate the internal behavior of Transformer-based Large Language Models (LLMs) when they generate factually incorrect text. We propose modeling factual queries as constrain…