Hybrid Retrieval Systems Enhancing Robustness in Legal Domain Question Answering

Abstract

Abstract: This report synthesises findings from 3 peer-reviewed papers addressing the following research question: Do hybrid retrieval systems combining manifold-aware dense retrieval with sparse retrieval (e.g., BM25) improve robustness against adversarial query perturbations in legal domain QA benchmarks like. Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating. 9 claims were extracted from source literature; 9 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

Do hybrid retrieval systems combining manifold-aware dense retrieval with sparse retrieval (e.g., BM25) improve robustness against adversarial query perturbations in legal domain QA benchmarks like JURIS-AQA while maintaining high Recall@1000?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance