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Mohit Bansal — most-cited papers & profile · Large Language Models
← authors
·
overview
Mohit Bansal
64
papers ·
511
citations ·
61
h-index
University of North Carolina at Chapel Hill · University of North Carolina Health Care · King's College London · King's College Hospital NHS Foundation Trust · ABES Engineering College
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Most-cited papers
TrustLLM: Trustworthiness in Large Language Models
2024 · 53 citations
LACIE: Listener-aware Finetuning For Confidence Calibration In Large Language Models
2024 · 15 citations
REAL Sampling: Boosting Factuality And Diversity Of Open-ended Generation Via Asymptotic Entropy
2024 · 9 citations
LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints
2024 · 1 citations
Stabilizing Efficient Reasoning with Step-Level Advantage Selection
2026
MINTEval: Evaluating Memory under Multi-Target Interference in Long-Horizon Agent Systems
2026
MuseBench: Benchmarking Intent-Level Audiovisual Arts Understanding in MLLMs
2026
Cog-DRIFT: Exploration on Adaptively Reformulated Instances Enables Learning from Hard Reasoning Problems
2026
GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs
2025
Instruction Tuning with and without Context: Behavioral Shifts and Downstream Impact
2025
Learning to Generate Unit Tests for Automated Debugging
2025
UPCORE: Utility-Preserving Coreset Selection for Balanced Unlearning
2025
MutaGReP: Execution-Free Repository-Grounded Plan Search for Code-Use
2025
RSQ: Learning from Important Tokens Leads to Better Quantized LLMs
2025
Symbolic Mixture-of-Experts: Adaptive Skill-based Routing for Heterogeneous Reasoning
2025
Top co-authors
Elias Stengel-Eskin
· 16
Archiki Prasad
· 7
Justin Chih-Yao Chen
· 7
Jaemin Cho
· 6
Zaid Khan
· 6
Tianlong Chen
· 5
Han Lin
· 4
Vaidehi Patil
· 4
Hyunji Lee
· 3
Jaehong Yoon
· 3
Jialu Li
· 3
Joykirat Singh
· 3
Topics
Training Techniques
Evaluation
Efficiency
Fine-Tuning
Vision-Language
Safety & Alignment
Model Architecture
RAG
Reinforcement Learning
In-Context Learning