Papers by Murari Mandal
Nine Ways to Break Copyright Law and Why Our LLM Won’t: A Fair Use Aligned Generation Framework (2025.findings-emnlp)
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Aakash Sen Sharma, Debdeep Sanyal, Priyansh Srivastava, Sundar Athreya H, Shirish Karande, Mohan Kankanhalli, Murari Mandal
| Challenge: | Large language models (LLMs) often risk copyright infringement by reproducing protected content verbatim or with insufficient transformative modifications. |
| Approach: | They propose a legally-grounded framework to align LLM outputs with fair-use doctrine . LAW-LM uses a dataset containing 18,000 expert-validated examples . |
| Outcome: | The proposed framework aligns outputs with fair-use doctrine and is validated by 18,000 experts. |
Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval-Augmented Generation Across Learning Styles (2025.emnlp-main)
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Debdeep Sanyal, Agniva Maiti, Umakanta Maharana, Dhruv Kumar, Ankur Mali, C. Lee Giles, Murari Mandal
| Challenge: | Existing models for large language models neglect comprehensive student modeling beyond basic knowledge states and lack mechanisms for teachers to dynamically adapt their approach based on student feedback and collective performance. |
| Approach: | They propose a framework that integrates LLM-based diverse student agents with a self-evolving teacher agent to optimize teacher's pedagogical parameters based on simulated student performance. |
| Outcome: | The proposed framework integrates diverse student agents with a self-evolving teacher agent to optimize teacher pedagogical parameters based on simulated student performance. |
ReviewEval: An Evaluation Framework for AI-Generated Reviews (2025.findings-emnlp)
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| Challenge: | escalating volume of academic research necessitates innovative approaches to peer review . authors propose reviewEval, ReviewAgent and ReviewEval to improve on existing reviews . |
| Approach: | They propose a framework for AI-generated reviews that measures alignment with human assessments . they propose 'reviewAgent' that iteratively optimizes its intermediate outputs and external improvement loops . |
| Outcome: | The proposed framework improves actionable insights and analytical depth by 6.78% and 47.62% over baselines and expert reviews. |