Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting
The paper presents a novel approach to Table Question-Answering (TQA) using two frameworks: TableGrid Navigation (TGN) and Progressive Inference Prompting (PIP). These methods aim to enhance the performance of Large Language Models (LLMs) on tabular data without the need for extensive training. The results indicate significant improvements in TQA tasks, making it a promising solution for resource-constrained environments.
- ▪The proposed TGN framework navigates tables iteratively to refine answers.
- ▪PIP enforces column identification for better row selection based on queries.
- ▪The evaluation shows TGN improves performance by 3.8 points on the TableBench dataset.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20254 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | LSwvXKHkl61Z |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Information Retrieval arXiv:2605.20254 (cs) [Submitted on 18 May 2026] Title:Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting Authors:Amritansh Maurya, Navjot Singh, Mohammed Javed, Omar Moured View a PDF of the paper titled Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting, by Amritansh Maurya and 2 other authors View PDF HTML (experimental) Abstract:Large Language Models (LLMs) have shown promising results on NLP tasks, however, their performance on tabular data still needs research attention, because Table Question-Answering (TQA) requires precise cell retrieval and multi-step structured reasoning.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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