TraceBack: Multi-Agent Decomposition for Fine-Grained Table Attribution

• URL: http://arxiv.org/abs/2602.13059v1
• Date: Fri, 13 Feb 2026 16:13:36 GMT
• Title: TraceBack: Multi-Agent Decomposition for Fine-Grained Table Attribution
• Authors: Tejas Anvekar, Junha Park, Rajat Jha, Devanshu Gupta, Poojah Ganesan, Puneeth Mathur, Vivek Gupta,
• Abstract summary: TraceBack is a framework for scalable, cell-level attribution in single-table QA.<n>We release CITEBench, a benchmark with phrase-to-cell annotations drawn from ToTTo, FetaQA, and AITQA.<n>We also propose FairScore, a reference-less metric that compares atomic facts derived from predicted cells and answers to estimate attribution precision and recall without human cell labels.
• Score: 11.133753556671392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Question answering (QA) over structured tables requires not only accurate answers but also transparency about which cells support them. Existing table QA systems rarely provide fine-grained attribution, so even correct answers often lack verifiable grounding, limiting trust in high-stakes settings. We address this with TraceBack, a modular multi-agent framework for scalable, cell-level attribution in single-table QA. TraceBack prunes tables to relevant rows and columns, decomposes questions into semantically coherent sub-questions, and aligns each answer span with its supporting cells, capturing both explicit and implicit evidence used in intermediate reasoning steps. To enable systematic evaluation, we release CITEBench, a benchmark with phrase-to-cell annotations drawn from ToTTo, FetaQA, and AITQA. We further propose FairScore, a reference-less metric that compares atomic facts derived from predicted cells and answers to estimate attribution precision and recall without human cell labels. Experiments show that TraceBack substantially outperforms strong baselines across datasets and granularities, while FairScore closely tracks human judgments and preserves relative method rankings, supporting interpretable and scalable evaluation of table-based QA.

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