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Thesis Proposal: Intentional Inference for Insight Generation

Paper · 2026 · Kristýna Onderková

Summary

This doctoral proposal from Charles University (ÚFAL) addresses a persistent weakness of LLM-based data-to-text generation, mainly table-to-text. Model outputs read fluently but are often unfaithful, logically shallow, or uninteresting. The author attributes much of this to task underspecification. When premises are missing, models quietly fill the gaps with their own assumptions, and these assumptions also distort faithfulness evaluation.

The agenda does not try to eliminate assumptions. Instead, it treats them as something to surface explicitly, similar to reconstructing unstated premises in arguments. It is organized around three research questions:

  • how hidden assumptions harm the factual validity of insights;
  • how symbolic reasoning (SQL and code generation) and qualitative reasoning can produce deeper inferences;
  • how assumptions and inference depth shape perceived interestingness.

Work so far includes studies on presuppositions and the SQuirreL system. SQuirreL uses SQL queries over tables and is hampered by noisy real-world tables and text-to-SQL errors. Even so, preliminary human evaluation suggests it yields fewer trivial and more interesting summaries than a baseline. The aim is more reliable, interpretable, and user-controllable NLG.

Key points

  • Three research questions:
    • RQ1: implicit assumptions vs. factual validity.
    • RQ2: symbolic and qualitative reasoning for deeper inferences.
    • RQ3: how assumptions and inference depth affect perceived interestingness.
  • SQuirreL (SQL-based table summarization): performance drops notably when tables are truncated. Early human judgments nonetheless favor it over the baseline for producing non-trivial insights.
  • Critique of benchmarks: existing benchmarks such as WebNLG and LogicNLG mostly require surface-level information with minimal inference. Reference-based metrics (e.g., PARENT) and model-based ones (TAPAS, TAPEX, NLI scores) miss deeper insights.
  • Planned work:
    • "softer" CoT for quasi-symbolic abstractions;
    • qualitative reasoning;
    • multi-agent debate;
    • counting logical operations in insights (neural plus regex methods);
    • a human evaluation built on template-generated insights.
  • Setup and funding: experiments rely mainly on open models for reproducibility. The work is funded by ERC NG-NLG (101,039,303), CEDMO 2.0 NPO, and GAUK 506,526.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Charles UniversityPrague · Czechia133 works · 20,686 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

Cite

Cite as Kristýna Onderková. 2026. Thesis Proposal: Intentional Inference for Insight Generation. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-srw.109

@inproceedings{onderkova2026thesis,
  title = {{Thesis Proposal: Intentional Inference for Insight Generation}},
  author = {Kristýna Onderková},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-srw.109},
  url = {https://doi.org/10.18653/v1/2026.acl-srw.109}
}

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