3 papers
cs.CL2026
PROMPT2BOX: Uncovering Entailment Structure among LLM Prompts
Neeladri Bhuiya, Shib Sankar Dasgupta, Andrew McCallum +1
To discover the weaknesses of LLMs, researchers often embed prompts into a vector space and cluster them to extract insightful patterns. However, vector embeddings primarily captur…
cs.IR2025
A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings
Shib Dasgupta, Michael Boratko, Andrew McCallum
Personalized item recommendation typically suffers from data sparsity, which is most often addressed by learning vector representations of users and items via low-rank matrix facto…
cs.CL2024
HITgram: A Platform for Experimenting with n-gram Language Models
Shibaranjani Dasgupta, Chandan Maity, Somdip Mukherjee +3
Large language models (LLMs) are powerful but resource intensive, limiting accessibility. HITgram addresses this gap by offering a lightweight platform for n-gram model experimenta…