Reasoning-Focused Legal Retrieval-Augmented Question Answering under Low Lexical Overlap: Query Expansion, Evidence Citation, and Selective Abstention
DOI:
https://doi.org/10.61424/ygk7h578Keywords:
Legal information retrieval, retrieval-augmented question answering, bar examination, BM25, latent semantic indexing, query expansion, citation evaluation, confidence calibration, selective abstentionAbstract
Legal retrieval-augmented question answering is difficult when a fact pattern and its controlling rule use different language. This study evaluates all 1,195 Bar Exam QA retrieval questions, 1,194 complete four-choice items, and the full 856,835-passage corpus. It compares BM25, 96-dimensional latent semantic indexing (LSI), reciprocal-rank fusion, pseudo-relevance feedback, answer-choice expansion, and cross-fitted rule-space expansion. LSI derives from TF–IDF and truncated singular-value decomposition; it is not a neural dense retriever. BM25 used a 40,000-term unigram/bigram vocabulary with k₁=1.2 and b=0.75. Mean query–gold TF–IDF cosine similarity was 0.0509, and 182 questions had zero overlap. Plain BM25 achieved 1.339% Recall@10; answer-choice expansion raised it to 5.105%, while rule-space BM25 reached 1.841%. The best retrieval-conditioned answer selector achieved 26.382% accuracy, versus 24.372% without evidence and 49.581% with gold evidence. Its 2.010-point gain over no evidence was nonsignificant (p=.265). Temperature scaling reduced its expected calibration error from 0.1486 to 0.0240, but risk–coverage ordering remained weak. Only 3.183% of depth-five contexts contained the gold or exact-equivalent passage, and 71.776% of questions combined missed gold evidence with a wrong answer. Stronger retrieval is therefore necessary before calibrated confidence can support dependable selective answering.
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