1 citations · 2 across the 32 of their papers we have counts for
14 papers · 1 filter
NagaNLP: Bootstrapping NLP for Low-Resource Nagamese Creole with Human-in-the-Loop Synthetic Data
Agniva Maiti, Manya Pandey, Murari Mandal
The vast majority of the world's languages, particularly creoles like Nagamese, remain severely under-resourced in Natural Language Processing (NLP), creating a significant barrier…
How to Trick Your AI TA: A Systematic Study of Academic Jailbreaking in LLM Code Evaluation
Devanshu Sahoo, Vasudev Majhi, Arjun Neekhra +3
The use of Large Language Models (LLMs) as automatic judges for code evaluation is becoming increasingly prevalent in academic environments. But their reliability can be compromise…
The Realignment Problem: When Right becomes Wrong in LLMs
Aakash Sen Sharma, Debdeep Sanyal, Manodeep Ray +3
Post-training alignment of large language models (LLMs) relies on large-scale human annotations guided by policy specifications that change over time. Cultural shifts, value reinte…
Confidence is Not Competence
Debdeep Sanyal, Manya Pandey, Dhruv Kumar +2
Large language models (LLMs) often exhibit a puzzling disconnect between their asserted confidence and actual problem-solving competence. We offer a mechanistic account of this dec…
Policy Optimization Prefers The Path of Least Resistance
Debdeep Sanyal, Aakash Sen Sharma, Dhruv Kumar +2
Policy optimization (PO) algorithms are used to refine Large Language Models for complex, multi-step reasoning. Current state-of-the-art pipelines enforce a strict think-then-answe…
time2time: Causal Intervention in Hidden States to Simulate Rare Events in Time Series Foundation Models
Debdeep Sanyal, Aaryan Nagpal, Dhruv Kumar +2
While transformer-based foundation models excel at forecasting routine patterns, two questions remain: do they internalize semantic concepts such as market regimes, or merely fit c…
AntiDote: Bi-level Adversarial Training for Tamper-Resistant LLMs
Debdeep Sanyal, Manodeep Ray, Murari Mandal
The release of open-weight large language models (LLMs) creates a tension between advancing accessible research and preventing misuse, such as malicious fine-tuning to elicit harmf…
Step-by-Step Reasoning Attack: Revealing 'Erased' Knowledge in Large Language Models
Yash Sinha, Manit Baser, Murari Mandal +2
Knowledge erasure in large language models (LLMs) is important for ensuring compliance with data and AI regulations, safeguarding user privacy, mitigating bias, and misinformation.…
Nine Ways to Break Copyright Law and Why Our LLM Won't: A Fair Use Aligned Generation Framework
Aakash Sen Sharma, Debdeep Sanyal, Priyansh Srivastava +4
Large language models (LLMs) commonly risk copyright infringement by reproducing protected content verbatim or with insufficient transformative modifications, posing significant et…
Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style
Debdeep Sanyal, Agniva Maiti, Umakanta Maharana +4
Effective teaching requires adapting instructional strategies to accommodate the diverse cognitive and behavioral profiles of students, a persistent challenge in education and teac…
Right Prediction, Wrong Reasoning: Uncovering LLM Misalignment in RA Disease Diagnosis
Umakanta Maharana, Sarthak Verma, Avarna Agarwal +4
Large language models (LLMs) offer a promising pre-screening tool, improving early disease detection and providing enhanced healthcare access for underprivileged communities. The e…
Guardians of Generation: Dynamic Inference-Time Copyright Shielding with Adaptive Guidance for AI Image Generation
Soham Roy, Abhishek Mishra, Shirish Karande +1
Modern text-to-image generative models can inadvertently reproduce copyrighted content memorized in their training data, raising serious concerns about potential copyright infringe…
ReviewEval: An Evaluation Framework for AI-Generated Reviews
Madhav Krishan Garg, Tejash Prasad, Tanmay Singhal +3
The escalating volume of academic research, coupled with a shortage of qualified reviewers, necessitates innovative approaches to peer review. In this work, we propose: 1. ReviewEv…
Agents Are All You Need for LLM Unlearning
Debdeep Sanyal, Murari Mandal
Information removal or suppression in large language models (LLMs) is a desired functionality, useful in AI regulation, legal compliance, safety, and privacy. LLM unlearning method…