arXiv Computation and Language By Sachin Gupta, Divya Godara

TRACE: Target-Aware Retrieval, Attributed Evidence, and Contract-Constrained Extraction for LitTraceQA

Read the original on arXiv Computation and Language →

TRACE is a system designed to bridge the grounding contract gap in LitTraceQA by combining target-aware retrieval, independent typed evidence localization, multimodal table extraction, and schema-driven table construction. It indexes 27,487 papers using multiple representations while preserving question targets, predicts observation units for tables, and assembles rows with evaluator-compatible key normalization. On the official test set, TRACE achieves a 0.760613 overall score, with high paper F1, evidence F1, and multiple-choice accuracy, though table-row and macro cell performance remain lower.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv AI
Sep 7

Does the Selected Object Reach the Reader? Auditing Identity Handoffs in Grounded Language-Model Pipelines

The paper investigates whether the object selected in a grounded language‑model pipeline actually reaches the reader, a failure that can break the handoff between stages. By auditing 600 HybridQA questions across three selector families, the authors find that exact key lookup and title matching recover the selected object in all 1,463 resolvable records, but body‑only BM25 omits it in 26.6% of cases at cutoff five, while hybrid retrieval with reranking omits it only 1.0%. The study also shows that misalignment between selected and retrieved objects can reduce exact match scores by up to 31 points, and introduces the Returned‑Object Profile (ROP) as a tool for reproducible auditing.

By Siddharth Vohra, Runmin Jiang, Xiaomo Li, Min Xu