mlsanityData to 7 Oct 2026

Aalap: AI Assistant for Legal & Paralegal Functions in India

arXiv 2402.01758 ↗First posted 30 Jan 2024cs.CY cs.AI cs.CLcited by 4

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Abstract (arXiv)

Using proprietary Large Language Models on legal tasks poses challenges due to data privacy issues, domain data heterogeneity, domain knowledge sophistication, and domain objectives uniqueness. We created Aalalp, a fine-tuned Mistral 7B model on instructions data related to specific Indian legal tasks. The performance of Aalap is better than gpt-3.5-turbo in 31% of our test data and obtains an equivalent score in 34% of the test data as evaluated by GPT4. Training Aalap mainly focuses on teaching legal reasoning rather than legal recall. Aalap is definitely helpful for the day-to-day activities of lawyers, judges, or anyone working in legal systems.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 30 references that are in the map. First posted on arXiv 30 Jan 2024.

Builds on

23 of 30 references.

References · 23

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11 more
  1. 2023Orca: Progressive Learning from Complex Explanation Traces of GPT-4cited by 369
  2. 2023LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Modelscited by 328
  3. 2023Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Surveycited by 134
  4. 2023Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecturecited by 119
  5. 2023LawBench: Benchmarking Legal Knowledge of Large Language Modelscited by 111
  6. 2022Legal Prompt Engineering for Multilingual Legal Judgement Predictioncited by 54
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  8. 2022Two-stage LLM Fine-tuning with Less Specialization and More Generalizationcited by 39
  9. 2023ChatGPT may Pass the Bar Exam soon, but has a Long Way to Go for the LexGLUE benchmarkcited by 33
  10. 2022Named Entity Recognition in Indian court judgmentscited by 26
  11. 2022Corpus for Automatic Structuring of Legal Documentscited by 25

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.