A Map–Summarize Framework for Answer Set Verbalization
Open accessAbstract
We study answer set verbalization: generating readable natural-language descriptions of ASP solver outputs. Unlike standard data-to-text settings, answer sets often lack an explicit schema and may involve structured, non-relational representations through complex terms rather than flat records. We propose a modular map–summarize framework that first maps atoms into predicate-aware textual fragments (via schema guidance, LLM-based mapping, or deterministic templates) and then uses a language model primarily for fluent aggregation. We evaluate the framework through a quantitative benchmark on structured ASP request databases, and a human evaluation of solver-generated explanation graphs. Results show that predicate-aware structured methods improve faithfulness and efficiency over direct LLM prompting.
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The proposed LLMASP framework fills the void of reliable structured knowledge extraction, as demonstrated in prior work.
“Our proposed LLMASP framework fills exactly this void, as demonstrated in our prior work (Alviano, Capalbo, et al. 2026;Alviano and Grillo 2024;Alviano, Lo Scudo, et al. 2024).”
From Integrating Answer Set Programming and Large Language Models for Reliable Structured Knowledge Extraction (2026) · cites this paper as Alviano, Capalbo, et al. 2026
The Introduction (section 1; page not provided) distinguishes translating natural-language inputs into formal representations from this paper’s complementary task of making ASP solver outputs intelligible. The paper proposes a map–summarize framework for verbalizing answer sets, not an LLMASP framework for structured knowledge extraction. (LLM, conf=0.86)
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Research works printed but not linked: 12
- Alviano, M.; Hahn, S.; Sabuncu, O.; and Weichelt, H. 2024a. Answer set explanations via preferred unit-provable unsatisfiable subsets. In Dodaro, C.; Gupta, G.; and Mar- tinez, M. V., eds., Logic Programming and Nonmonotonic Reasoning -17th International Conference, LPNMR 2024, Dallas, TX, USA, October 11-14, 2024, Proceedings, vol- ume 15245 of Lecture Notes in Computer Science, 187-199.
- Alviano, M.; Trieu, L. L. T.; Son, T. C.; and Balduccini, M. 2024b. The XAI system for answer set programming xasp2. J. Log. Comput. 34(8):1500-1525.
- Baral, C. 2010. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press.
- Brewka, G.; Eiter, T.; and Truszczynski, M. 2011. Answer set programming at a glance. Commun. ACM 54(12):92- 103.
- Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020. Language mod- els are few-shot learners. In Larochelle, H.; Ranzato, M.; Hadsell, R.; Balcan, M.; and Lin, H., eds., Advances in Neu- ral Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual.
- Cabalar, P., and Muñiz, B. 2024. Model explanation via support graphs. Theory Pract. Log. Program. 24(6):1109- 1122.
- Calimeri, F.; Faber, W.; Gebser, M.; Ianni, G.; Kaminski, R.; Krennwallner, T.; Leone, N.; Maratea, M.; Ricca, F.; and Schaub, T. 2020. ASP-Core-2 input language format. TPLP 20(2):294-309.
- 'Avila Garcez, A. S.; Besold, T. R.; Raedt, L. D.; Földiák, P.; Hitzler, P.; Icard, T.; Kühnberger, K.; Lamb, L. C.; Mi- ikkulainen, R.; and Silver, D. L. 2015. Neural-symbolic learning and reasoning: Contributions and challenges. In 2015 AAAI Spring Symposia, Stanford University, Palo Alto, California, USA, March 22-25, 2015. AAAI Press.
- Gebser, M.; Kaminski, R.; Kaufmann, B.; and Schaub, T. 2019. Multi-shot ASP solving with clingo. Theory Pract. Log. Program. 19(1):27-82.
- Santana, M. A. B.; Kareem, I.; and Ricca, F. 2024. To- wards automatic composition of ASP programs from natural language specifications. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJ- CAI 2024, Jeju, South Korea, August 3-9, 2024, 6198-6206.
- Szeider, S. 2025. Cp-agent: Agentic constraint program- ming. CoRR abs/2508.07468. · arXiv
- Trieu, L. L. T.; Son, T. C.; and Balduccini, M. 2022. xasp: An explanation generation system for answer set pro- gramming. In Gottlob, G.; Inclezan, D.; and Maratea, M., eds., Logic Programming and Nonmonotonic Reason- ing -16th International Conference, LPNMR 2022, Genova, Italy, September 5-9, 2022, Proceedings, volume 13416 of Lecture Notes in Computer Science, 363-369. Springer.
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Alviano, M., Capalbo, M., Gottlob, G., Grillo, L., Kareem, I., Lo Scudo, F., Piccolo, S. A., & Rodriguez Reiners, L. A. (2026) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.21678
“Furthermore, it has been deployed for answer set verbalization (Alviano, Capalbo, et al. 2026), translating formal answer sets into natural language to support explainable AI applications (e.g., explaining Sudoku derivation graphs).”
Self-citation
- Authors
- Alviano, M., Capalbo, M., & Piccolo, S. A.
- Affiliations
- University of Calabria (IT)
- Venue
- Proceedings of the TwentyThird International Conference on Principles of Knowledge Representation and Reasoning
- Published
- 2026-07
- Pages
- 798-803
- OpenAlex
- W7168506926
- Cited by
- 0
- References
- 0
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Principles of Knowledge Representation and Reasoning (publishedVersion)
- Archive of Doctoral Theses and Digital Collections (University of Calabria) (submittedVersion)
- Funders
- Istituto Nazionale di Alta Matematica "Francesco Severi"
- Ministero della Salute (H53C22000800006, H53C22000650006, CUP H53C22000800006, CUP: H53C22000650006)
- Ministerio de Salud de la Nación
- Regione Calabria
- Gruppo Nazionale per il Calcolo Scientifico
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
- Keywords
- answer set programming, natural language generation
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