Pith. sign in

REVIEW 1 cited by

Aalap: AI Assistant for Legal & Paralegal Functions in India

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2402.01758 v1 pith:WCLXCRQ4 submitted 2024-01-30 cs.CY cs.AIcs.CL

Aalap: AI Assistant for Legal & Paralegal Functions in India

Aman Tiwari , Prathamesh Kalamkar , Atreyo Banerjee , Saurabh Karn , Varun Hemachandran , Smita Gupta This is my paper
classification cs.CY cs.AIcs.CL
keywords legaldataaalapdomaintaskstestaalalpactivities
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Lightweight Domain Adaptation of a Large Language Model for Legal Assistance in the Indian Context

    cs.CL 2025-05 unverdicted novelty 5.0

    An 8B Llama model with RAG and prompt engineering scores 60.08% on the All-India Bar Examination, slightly above GPT-3.5 Turbo while claiming 22 times greater parameter efficiency via a new PEI metric.