8 citations · 19 across the 9 of their papers we have counts for
9 papers
Breaking the Safety-Capability Tradeoff: Reinforcement Learning with Verifiable Rewards Maintains Safety Guardrails in LLMs
Dongkyu Derek Cho, Huan Song, Arijit Ghosh Chowdhury +6
Fine-tuning large language models (LLMs) for downstream tasks typically exhibit a fundamental safety-capability tradeoff, where improving task performance degrades safety alignment…
Learning from Generalization Patterns: An Evaluation-Driven Approach to Enhanced Data Augmentation for Fine-Tuning Small Language Models
Huan Song, Deeksha Razdan, Yiyue Qian +6
Small Language Models (SLMs) offer compelling advantages in deployment cost and latency, but their accuracy often lags behind larger models, particularly for complex domain-specifi…
LeMAJ (Legal LLM-as-a-Judge): Bridging Legal Reasoning and LLM Evaluation
Joseph Enguehard, Morgane Van Ermengem, Kate Atkinson +8
Evaluating large language model (LLM) outputs in the legal domain presents unique challenges due to the complex and nuanced nature of legal analysis. Current evaluation approaches…
Breaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models
Arijit Ghosh Chowdhury, Md Mofijul Islam, Vaibhav Kumar +3
Large Language Models (LLMs) have become a cornerstone in the field of Natural Language Processing (NLP), offering transformative capabilities in understanding and generating human…
From Prejudice to Parity: A New Approach to Debiasing Large Language Model Word Embeddings
Aishik Rakshit, Smriti Singh, Shuvam Keshari +3
Embeddings play a pivotal role in the efficacy of Large Language Models. They are the bedrock on which these models grasp contextual relationships and foster a more nuanced underst…
Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges
Vinay Samuel, Houda Aynaou, Arijit Ghosh Chowdhury +2
Large Language Models (LLMs) have demonstrated impressive zero shot performance on a wide range of NLP tasks, demonstrating the ability to reason and apply commonsense. A relevant…
Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering
Arijit Ghosh Chowdhury, Aman Chadha
Robustness in Natural Language Processing continues to be a pertinent issue, where state of the art models under-perform under naturally shifted distributions. In the context of Qu…
An Interpretable Deep Learning System for Automatically Scoring Request for Proposals
Subhadip Maji, Anudeep Srivatsav Appe, Raghav Bali +3
The Managed Care system within Medicaid (US Healthcare) uses Request For Proposals (RFP) to award contracts for various healthcare and related services. RFP responses are very deta…
Exclusion and Inclusion -- A model agnostic approach to feature importance in DNNs
Subhadip Maji, Arijit Ghosh Chowdhury, Raghav Bali +1
Deep Neural Networks in NLP have enabled systems to learn complex non-linear relationships. One of the major bottlenecks towards being able to use DNNs for real world applications…