Kumar Vikas


2023

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Citation-Based Summarization of Landmark Judgments
Bindal Purnima | Kumar Vikas | Bhatnagar Vasudha | Sirohi Parikshet | Siwal Ashwini
Proceedings of the 20th International Conference on Natural Language Processing (ICON)

Landmark judgments are of prime importance in the Common Law System because of their exceptional jurisprudence and frequent references in other judgments. In this work, we leverage contextual references available in citing judgments to create an extractive summary of the target judgment. We evaluate the proposed algorithm on two datasets curated from the judgments of Indian Courts and find the results promising.

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Infusing Knowledge into Large Language Models with Contextual Prompts
Vasisht Kinshuk | Ganesan Balaji | Kumar Vikas | Bhatnagar Vasudha
Proceedings of the 20th International Conference on Natural Language Processing (ICON)

Knowledge infusion is a promising method for enhancing Large Language Models for domainspecific NLP tasks rather than pre-training models over large data from scratch. These augmented LLMs typically depend on additional pre-training or knowledge prompts from an existing knowledge graph, which is impractical in many applications. In contrast, knowledge infusion directly from relevant documents is more generalisable and alleviates the need for structured knowledge graphs while also being useful for entities that are usually not found in any knowledge graph. With this motivation, we propose a simple yet generalisable approach for knowledge infusion by generating prompts from the context in the input text. Our experiments show the effectiveness of our approach which we evaluate by probing the fine-tuned LLMs.