# Can LLMs Narrate Tabular Data? An Evaluation Framework for Natural Language Representations of Text-to-SQL System Outputs

arXiv: https://arxiv.org/abs/2510.23854 · HTML: https://mlsanity.com/paper/2510.23854/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 63 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://mlsanity.com/paper/2304.11015/) (2023)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (2020)
- [CodeS: Towards Building Open-source Language Models for Text-to-SQL](https://mlsanity.com/paper/2402.16347/) (2024)
- [FeTaQA: Free-form Table Question Answering](https://mlsanity.com/paper/2104.00369/) (2021)
- [CHESS: Contextual Harnessing for Efficient SQL Synthesis](https://mlsanity.com/paper/2405.16755/) (2024)
- [C3: Zero-shot Text-to-SQL with ChatGPT](https://mlsanity.com/paper/2307.07306/) (2023)
- [Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment](https://mlsanity.com/paper/2402.14016/) (2024)
- [Synthesizing Text-to-SQL Data from Weak and Strong LLMs](https://mlsanity.com/paper/2408.03256/) (2024)
- [BotChat: Evaluating LLMs' Capabilities of Having Multi-Turn Dialogues](https://mlsanity.com/paper/2310.13650/) (2023)
- [Understanding the Effects of Noise in Text-to-SQL: An Examination of the BIRD-Bench Benchmark](https://mlsanity.com/paper/2402.12243/) (2024)
- [Text2SQL is Not Enough: Unifying AI and Databases with TAG](https://mlsanity.com/paper/2408.14717/) (2024)
- [Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval](https://mlsanity.com/paper/2404.09889/) (2024)
- [A Survey on Recent Advances in Conversational Data Generation](https://mlsanity.com/paper/2405.13003/) (2024)
- [Text-to-SQL Error Correction with Language Models of Code](https://mlsanity.com/paper/2305.13073/) (2023)
- [Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations](https://mlsanity.com/paper/2305.07372/) (2023)
- [Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy](https://mlsanity.com/paper/2412.17759/) (2024)
- [SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data](https://mlsanity.com/paper/2311.02883/) (2023)
- [SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use](https://mlsanity.com/paper/2505.17332/) (2025)
- [Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems](https://mlsanity.com/paper/2505.18366/) (2025)
- [Error Detection for Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2305.13683/) (2023)
- [LLM for Barcodes: Generating Diverse Synthetic Data for Identity Documents](https://mlsanity.com/paper/2411.14962/) (2024)
- [Tuning LLM Judge Design Decisions for 1/1000 of the Cost](https://mlsanity.com/paper/2501.17178/) (2025)
- [Aligning LLMs for Multilingual Consistency in Enterprise Applications](https://mlsanity.com/paper/2509.23659/) (2025)
- [DBCopilot: Natural Language Querying over Massive Databases via Schema Routing](https://mlsanity.com/paper/2312.03463/) (2023)
- [FS-DAG: Few Shot Domain Adapting Graph Networks for Visually Rich Document Understanding](https://mlsanity.com/paper/2505.17330/) (2025)
- [DAIQ: Auditing Demographic Attribute Inference from Question in LLMs](https://mlsanity.com/paper/2508.15830/) (2025)

## Built on by (most cited first, 7 of 7)

- [JTPRO: A Joint Tool-Prompt Reflective Optimization Framework for Language Agents](https://mlsanity.com/paper/2604.19821/) (2026)
- [Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and Bandwidth](https://mlsanity.com/paper/2602.11175/) (2026)
- [MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation](https://mlsanity.com/paper/2604.08782/) (2026)
- [GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations](https://mlsanity.com/paper/2605.07053/) (2026)
- [Do Image-Text Metrics Respect Semantic Invariances?](https://mlsanity.com/paper/2605.24702/) (2026)
- [DiffuMask: Diffusion Language Model for Token-level Prompt Pruning](https://mlsanity.com/paper/2604.06627/) (2026)
- [SPENCE: A Syntactic Probe for Detecting Contamination in NL2SQL Benchmarks](https://mlsanity.com/paper/2604.17771/) (2026)

## Abstract (arXiv)

In modern industry systems like multi-turn chat agents, Text-to-SQL technology bridges natural language (NL) questions and database (DB) querying. The conversion of tabular DB results into NL representations (NLRs) enables the chat-based interaction. Currently, NLR generation is typically handled by large language models (LLMs), but information loss or errors in presenting tabular results in NL remains largely unexplored. This paper introduces a novel evaluation method - Combo-Eval - for judgment of LLM-generated NLRs that combines the benefits of multiple existing methods, optimizing evaluation fidelity and achieving a significant reduction in LLM calls by 25-61%. Accompanying our method is NLR-BIRD, the first dedicated dataset for NLR benchmarking. Through human evaluations, we demonstrate the superior alignment of Combo-Eval with human judgments, applicable across scenarios with and without ground truth references.