Setting The Table with Intent: Intent-aware Schema Generation and Editing for Literature Review Tables

• URL: http://arxiv.org/abs/2507.19521v1
• Date: Fri, 18 Jul 2025 22:01:27 GMT
• Title: Setting The Table with Intent: Intent-aware Schema Generation and Editing for Literature Review Tables
• Authors: Vishakh Padmakumar, Joseph Chee Chang, Kyle Lo, Doug Downey, Aakanksha Naik,
• Abstract summary: We present an approach for augmenting unannotated table corpora with synthesized intents and apply it to create a dataset for studying schema generation conditioned on a given information need.<n>Next, we propose several LLM-based schema editing techniques.
• Score: 37.55154887661534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing volume of academic literature makes it essential for researchers to organize, compare, and contrast collections of documents. Large language models (LLMs) can support this process by generating schemas defining shared aspects along which to compare papers. However, progress on schema generation has been slow due to: (i) ambiguity in reference-based evaluations, and (ii) lack of editing/refinement methods. Our work is the first to address both issues. First, we present an approach for augmenting unannotated table corpora with synthesized intents and apply it to create a dataset for studying schema generation conditioned on a given information need, thus reducing ambiguity. With this dataset, we show how incorporating table intents significantly improves baseline performance in reconstructing reference schemas. Next, we propose several LLM-based schema editing techniques. We start by comprehensively benchmarking several single-shot schema generation methods, including prompted LLM workflows and fine-tuned models, showing that smaller, open-weight models can be fine-tuned to be competitive with state-of-the-art prompted LLMs. Then we demonstrate that our editing techniques can further improve schemas generated by these methods.

Related papers

• Beyond Isolated Dots: Benchmarking Structured Table Construction as Deep Knowledge Extraction [28.47810405584841]
  Arranged and Organized Extraction Benchmark designed to evaluate ability of large language models to comprehend fragmented documents.<n>AOE includes 11 carefully crafted tasks across three diverse domains, requiring models to generate context-specific schema tailored to varied input queries.<n>Results show that even the most advanced models struggled significantly.
  arXiv  Detail & Related papers  (2025-07-22T06:37:51Z)
• Large Language Models are Good Relational Learners [55.40941576497973]
  We introduce Rel-LLM, a novel architecture that utilizes a graph neural network (GNN)- based encoder to generate structured relational prompts for large language models (LLMs)<n>Unlike traditional text-based serialization approaches, our method preserves the inherent relational structure of databases while enabling LLMs to process and reason over complex entity relationships.
  arXiv  Detail & Related papers  (2025-06-06T04:07:55Z)
• SchemaGraphSQL: Efficient Schema Linking with Pathfinding Graph Algorithms for Text-to-SQL on Large-Scale Databases [1.6544167074080365]
  We present a zero-shot, training-free schema linking approach that first constructs a schema graph based on foreign key relations.<n>We apply classical path-finding algorithms and post-processing to identify the optimal sequence of tables and columns that should be joined.<n>Our method achieves state-of-the-art results on the BIRD benchmark, outperforming previous specialized, fine-tuned, and complex multi-step LLM-based approaches.
  arXiv  Detail & Related papers  (2025-05-23T20:42:36Z)
• Matchmaker: Self-Improving Large Language Model Programs for Schema Matching [60.23571456538149]
  We propose a compositional language model program for schema matching, comprised of candidate generation, refinement and confidence scoring. Matchmaker self-improves in a zero-shot manner without the need for labeled demonstrations. Empirically, we demonstrate on real-world medical schema matching benchmarks that Matchmaker outperforms previous ML-based approaches.
  arXiv  Detail & Related papers  (2024-10-31T16:34:03Z)
• ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models [58.34560740973768]
  We introduce a framework that leverages language models (LMs) to generate literature review tables. A new dataset of 2,228 literature review tables extracted from ArXiv papers synthesize a total of 7,542 research papers. We evaluate LMs' abilities to reconstruct reference tables, finding this task benefits from additional context.
  arXiv  Detail & Related papers  (2024-10-25T18:31:50Z)
• DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System [83.34921966305804]
  Large language models (LLMs) have demonstrated remarkable performance in recommender systems.<n>We propose a novel plug-and-play alignment framework for LLMs and collaborative models.<n>Our method is superior to existing state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2024-08-15T15:56:23Z)
• Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction [12.455647753787442]
  We propose a three-phase framework named Extract-Define-Canonicalize (EDC) EDC is flexible in that it can be applied to settings where a pre-defined target schema is available and when it is not. We demonstrate EDC is able to extract high-quality triplets without any parameter tuning and with significantly larger schemas compared to prior works.
  arXiv  Detail & Related papers  (2024-04-05T02:53:51Z)
• HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generation [7.69801337810352]
  We conduct parameter-efficient fine-tuning on the LLaMA2 model. Our approach involves injecting reasoning information into the input by emphasizing table-specific row data. On both the FetaQA and QTSumm datasets, our approach achieved state-of-the-art results.
  arXiv  Detail & Related papers  (2023-11-15T12:02:52Z)
• Schema-adaptable Knowledge Graph Construction [47.772335354080795]
  Conventional Knowledge Graph Construction (KGC) approaches typically follow the static information extraction paradigm with a closed set of pre-defined schema. We propose a new task called schema-adaptable KGC, which aims to continually extract entity, relation, and event based on a dynamically changing schema graph without re-training.
  arXiv  Detail & Related papers  (2023-05-15T15:06:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.