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Understanding Errors in LLM-Based Question Answering over Imperfect Tables

Baowen Zhang, Wei Fan, Ruman Wang, Hangting Ye · Oct 3, 2026

Citations: 0

Match reason: Matched by broad semantic/index fallback.

Score: 42% Moderate protocol signal Freshness: Hot Status: Ready
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  • Using human-reviewed instances from RADAR-T, we conduct controlled studies across three LLMs by varying row order and comparing original, error-marked, and repaired tables.
  • On RADAR-T, GBDI improves QA accuracy by 3.8-18.5 percentage points over a code-agent baseline across five LLMs (paired 95% confidence intervals exclude zero for four), at the cost of additional inference.
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