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Mohit Bansal

summarization

compositional generalization

in-context learning

language generation

large language models

alignment

benchmark

dataset

data augmentation

continual learning

conversation

interpretability

commonsense reasoning

code generation

llms

79

presentations

96

number of views

2

citations

SHORT BIO

Dr. Mohit Bansal is the John R. & Louise S. Parker Professor and the Director of the MURGe-Lab (UNC-NLP Group) in the Computer Science department at UNC Chapel Hill. He received his PhD from UC Berkeley in 2013 and his BTech from IIT Kanpur in 2008. His research expertise is in natural language processing and multimodal machine learning, with a particular focus on multimodal generative models, grounded and embodied semantics, language generation and Q&A/dialogue, and interpretable and generalizable deep learning. He is a recipient of IIT Kanpur Young Alumnus Award, DARPA Director's Fellowship, NSF CAREER Award, Google Focused Research Award, Microsoft Investigator Fellowship, Army Young Investigator Award (YIP), DARPA Young Faculty Award (YFA), and outstanding paper awards at ACL, CVPR, EACL, COLING, and CoNLL. He has been a keynote speaker for the AACL 2023 and INLG 2022 conferences. His service includes ACL Executive Committee, ACM Doctoral Dissertation Award Committee, CoNLL Program Co-Chair, ACL Americas Sponsorship Co-Chair, and Associate/Action Editor for TACL, CL, IEEE/ACM TASLP, and CSL journals. Webpage: https://www.cs.unc.edu/~mbansal/

Presentations

The Unreasonable Effectiveness of Easy Training Data for Hard Tasks

Peter Hase and 3 other authors

Soft Self-Consistency Improves Language Models Agents

Han Wang and 3 other authors

Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings

Yichen Jiang and 2 other authors

The Power of Summary-Source Alignments

Ori Ernst and 7 other authors

ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

Justin Chen and 2 other authors

Evaluating Very Long-Term Conversational Memory of LLM Agents

Adyasha Maharana and 5 other authors

REFINESUMM: Self-Refining MLLM for Generating a Multimodal Summarization Dataset

Vaidehi Ramesh Patil and 4 other authors

Prompting Vision-Language Models For Aspect-Controlled Generation of Referring Expressions

Danfeng Guo and 7 other authors

ADaPT: As-Needed Decomposition and Planning with Language Models

Archiki Prasad and 6 other authors

Branch-Solve-Merge Improves Large Language Model Evaluation and Generation

Swarnadeep Saha and 5 other authors

VLN-Video: Utilizing Driving Videos for Outdoor Vision-and-Language Navigation

Jialu Li and 3 other authors

HistAlign: Improving Context Dependency in Language Generation by Aligning with History

David Wan and 2 other authors

Data Factors for Better Compositional Generalization

Xiang Zhou and 2 other authors

Generating Summaries with Controllable Readability Levels | VIDEO

Leonardo F. R. Ribeiro and 2 other authors

ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness

Archiki Prasad and 3 other authors

Debiasing Multimodal Models via Causal Information Minimization

Mohit Bansal and 2 other authors

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