47 citations · 77 across the 30 of their papers we have counts for
8 papers · 1 filter
Cognitive bias in LLM reasoning compromises interpretation of clinical oncology notes
Matthew W. Kenaston, Umair Ayub, Mihir Parmar +14
Despite high performance on clinical benchmarks, large language models may reach correct conclusions through faulty reasoning, a failure mode with safety implications for oncology…
PHANTOM RECALL: When Familiar Puzzles Fool Smart Models
Souradeep Mukhopadhyay, Rishabh Baral, Nimeesh Mahajan +5
Large language models (LLMs) such as GPT, Gemini, and Claude often appear adept at solving classic logic puzzles--but how much genuine reasoning underlies their answers? Recent evi…
GuidedSampling: Steering LLMs Towards Diverse Candidate Solutions at Inference-Time
Divij Handa, Mihir Parmar, Aswin RRV +3
Repeated Sampling (RS) is a simple inference-time algorithm that has been shown to improve model performance on complex tasks. Although it is an effective way of scaling inference…
LLM-Based Multi-Agent Blackboard System for Information Discovery in Data Science
Alireza Salemi, Mihir Parmar, Palash Goyal +5
Advances in large language models (LLMs) have created new opportunities in data science, but their deployment is often limited by the challenge of finding relevant data in large da…
ThinkTuning: Instilling Cognitive Reflections without Distillation
Aswin RRV, Jacob Dineen, Divij Handa +4
Recent advances in test-time scaling have led to the emergence of thinking LLMs that exhibit self-reflective behaviors and multi-step reasoning. While RL drives this self-improveme…
PLAN-TUNING: Post-Training Language Models to Learn Step-by-Step Planning for Complex Problem Solving
Mihir Parmar, Palash Goyal, Xin Liu +5
Recently, decomposing complex problems into simple subtasks--a crucial part of human-like natural planning--to solve the given problem has significantly boosted the performance of…
PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving
Mihir Parmar, Xin Liu, Palash Goyal +11
Recent agent frameworks and inference-time algorithms often struggle with complex planning problems due to limitations in verifying generated plans or reasoning and varying complex…
Investigating the Shortcomings of LLMs in Step-by-Step Legal Reasoning
Venkatesh Mishra, Bimsara Pathiraja, Mihir Parmar +5
Reasoning abilities of LLMs have been a key focus in recent years. One challenging reasoning domain with interesting nuances is legal reasoning, which requires careful application…