# Intent-Aware Schema Generation And Refinement For Literature Review Tables

arXiv: https://arxiv.org/abs/2507.19521 · HTML: https://mlsanity.com/paper/2507.19521/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 51 references that are in the map.
- First posted on arXiv 18 Jul 2025.

## Builds on (most cited first, 28 of 28)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://mlsanity.com/paper/2210.02406/) (2022)
- [LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset](https://mlsanity.com/paper/2309.11998/) (2023)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (2020)
- [PaperQA: Retrieval-Augmented Generative Agent for Scientific Research](https://mlsanity.com/paper/2312.07559/) (2023)
- [Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooM](https://mlsanity.com/paper/2404.12259/) (2024)
- [Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases](https://mlsanity.com/paper/2012.07675/) (2020)
- [BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation](https://mlsanity.com/paper/2210.07626/) (2022)
- [Text-to-Table: A New Way of Information Extraction](https://mlsanity.com/paper/2109.02707/) (2021)
- [Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell](https://mlsanity.com/paper/2406.14673/) (2024)
- [CHIME: LLM-Assisted Hierarchical Organization of Scientific Studies for Literature Review Support](https://mlsanity.com/paper/2407.16148/) (2024)
- [Schema-Driven Information Extraction from Heterogeneous Tables](https://mlsanity.com/paper/2305.14336/) (2023)
- [Ai2 Scholar QA: Organized Literature Synthesis with Attribution](https://mlsanity.com/paper/2504.10861/) (2025)
- [DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles](https://mlsanity.com/paper/2207.01079/) (2022)
- [Struc-Bench: Are Large Language Models Really Good at Generating Complex Structured Data?](https://mlsanity.com/paper/2309.08963/) (2023)
- [gTBLS: Generating Tables from Text by Conditional Question Answering](https://mlsanity.com/paper/2403.14457/) (2024)
- [Hierarchical Catalogue Generation for Literature Review: A Benchmark](https://mlsanity.com/paper/2304.03512/) (2023)
- [Reproducibility Issues for BERT-based Evaluation Metrics](https://mlsanity.com/paper/2204.00004/) (2022)
- [arXiv2Table: Toward Realistic Benchmarking and Evaluation for LLM-Based Literature-Review Table Generation](https://mlsanity.com/paper/2504.10284/) (2025)
- [On-the-fly Table Generation](https://mlsanity.com/paper/1805.04875/) (2018)
- [Facets, Taxonomies, and Syntheses: Navigating Structured Representations in LLM-Assisted Literature Review](https://mlsanity.com/paper/2504.18496/) (2025)

## Built on by (most cited first, 5 of 5)

- [LOGOS: LLM-driven End-to-End Grounded Theory Development and Schema Induction for Qualitative Research](https://mlsanity.com/paper/2509.24294/) (2025)
- [ScheMatiQ: From Research Question to Structured Data through Interactive Schema Discovery](https://mlsanity.com/paper/2604.09237/) (2026)
- [Building Agent Harnesses for Scientific Curation from Multimodal Sources](https://mlsanity.com/paper/2606.21005/) (2026)
- [Bridging Human Interpretation and Machine Representation: A Landscape of Qualitative Data Analysis in the LLM Era](https://mlsanity.com/paper/2601.11739/) (2026)
- [Schematize: An Agentic System for Generating and Refining Information-Extraction Schemas for Legal Research](https://mlsanity.com/paper/2609.22209/) (2026)

## Abstract (arXiv)

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. 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. Next, we propose several LLM-based schema refinement techniques and show that these can further improve schemas generated by these methods.