mlsanityData to 14 Oct 2026

FreshTab: Sourcing Fresh Data for Table-to-Text Generation Evaluation

arXiv 2510.13598 ↗First posted 15 Oct 2025cs.CLcited by 2

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Abstract (arXiv)

Table-to-text generation (insight generation from tables) is a challenging task that requires precision in analyzing the data. In addition, the evaluation of existing benchmarks is affected by contamination of Large Language Model (LLM) training data as well as domain imbalance. We introduce FreshTab, an on-the-fly table-to-text benchmark generation from Wikipedia, to combat the LLM data contamination problem and enable domain-sensitive evaluation. While non-English table-to-text datasets are limited, FreshTab collects datasets in different languages on demand (we experiment with German, Russian and French in addition to English). We find that insights generated by LLMs from recent tables collected by our method appear clearly worse by automatic metrics, but this does not translate into LLM and human evaluations. Domain effects are visible in all evaluations, showing that a~domain-balanced benchmark is more challenging.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 28 of its 40 references that are in the map. First posted on arXiv 15 Oct 2025.

Builds on

28 of 40 references.

References · 28

  1. 2024The Llama 3 Herd of Modelscited by 14,715
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16 more
  1. 2022Chart-to-Text: A Large-Scale Benchmark for Chart Summarizationcited by 169
  2. 2023Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarkscited by 142
  3. 2023Task Contamination: Language Models May Not Be Few-Shot Anymorecited by 99
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  5. 2023Table-GPT: Table-tuned GPT for Diverse Table Taskscited by 69
  6. 2017Table-to-text Generation by Structure-aware Seq2seq Learningcited by 54
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  8. 2023Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphscited by 24
  9. 2022Table-To-Text generation and pre-training with TabT5cited by 20
  10. 2024Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generationcited by 16
  11. 2025GuideBench: Benchmarking Domain-Oriented Guideline Following for LLM Agentscited by 14
  12. 2025An LLM-Based Approach for Insight Generation in Data Analysiscited by 12
  13. 2024Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuningcited by 11
  14. 2022R2D2: Robust Data-to-Text with Replacement Detectioncited by 8
  15. 2023LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Controlcited by 8
  16. 2023HeLM: Highlighted Evidence augmented Language Model for Enhanced Table-to-Text Generationcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026From Tables to Quantified Statements: Evaluating LLM Inference Generation through Executable Verificationcited by 0
  2. 2026Structured Composition of Verifiable Atomic Insights for Table-to-Report Generationcited by 0