Corpus Quality Improves, Retrieval Utility Does Not: An Ablation of Preprocessing and Refinement in a Speech-to-QA Pipeline
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The paper introduces three retrieval methods for Polish statutory law that use language‑model annotations attached to articles as surrogates. The methods—ASCR, ASCR‑H, and DTF—vary in cost and quality, with ASCR‑H achieving the highest rank‑one accuracy on bar exam questions, while DTF offers competitive performance with lower latency and cost. Extensive evaluation against 14 baselines on 300 exam questions demonstrates significant improvements in head‑rank accuracy and discusses limitations such as coverage asymmetry and negative results for lemmatisation, pseudo‑relevance feedback, and query rewriting.
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The paper introduces Corpus Task Complexity (CTC), a metric that captures how a task’s difficulty scales with corpus size. It distinguishes low‑CTC tasks, whose difficulty grows linearly, from high‑CTC tasks, whose difficulty grows quadratically or more, and presents ten new high‑CTC tasks. Experiments show that models performing well on low‑CTC tasks often fail on high‑CTC tasks, highlighting the need for new approaches to large‑corpus reasoning.