Automated Argument Generation from Legal Facts
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Abstract (arXiv)
The count of pending cases has shown an exponential rise across nations (e.g., with more than 10 million pending cases in India alone). The main issue lies in the fact that the number of cases submitted to the law system is far greater than the available number of legal professionals present in a country. Given this worldwide context, the utilization of AI technology has gained paramount importance to enhance the efficiency and speed of legal procedures. In this study we partcularly focus on helping legal professionals in the process of analyzing a legal case. Our specific investigation delves into harnessing the generative capabilities of open-sourced large language models to create arguments derived from the facts present in legal cases. Experimental results show that the generated arguments from the best performing method have on average 63% overlap with the benchmark set gold standard annotations.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 29 references that are in the map. First posted on arXiv 9 Oct 2023.
Builds on
11 of 29 references.
References · 11
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
- 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
- 2023Prompting Large Language Model for Machine Translation: A Case Studycited by 247
- 2021Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuningcited by 145
- 2023On Learning to Summarize with Large Language Models as Referencescited by 80
- 2019Identification of Rhetorical Roles of Sentences in Indian Legal Judgmentscited by 39
- 2022Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluationcited by 23
- 2019Long-length Legal Document Classificationcited by 10
- 2018Automatic Catchphrase Extraction from Legal Case Documents via Scoring using Deep Neural Networkscited by 3
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