Papers with formal representation
PDDLEGO: Iterative Planning in Textual Environments (2024.starsem-1)
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| Challenge: | Existing methods to plan in textual environments rely on a fully-observed environment where all entity states are known, but are not interpretable. |
| Approach: | They propose to use LLMs to generate a formal representation of the environment that can be solved by a symbolic planner. |
| Outcome: | The proposed model outperforms existing methods in the Coin Collector simulation and Cooking World simulations. |
An Annotation Language for Semantic Search of Legal Sources (L18-1)
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| Challenge: | formalizing legal sources is an important challenge, but the generation of a formal representation from legal texts has been less considered and requires considerable expertise. |
| Approach: | They propose to experiment with annotations and the annotation process to improve uniformity and efficiency of legal annotation. |
| Outcome: | The proposed method improves the richness and efficiency of legal annotations. |
On the Limit of Language Models as Planning Formalizers (2025.acl-long)
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| Challenge: | Large Language Models can create plans that are neither executable nor verifiable in grounded environments. |
| Approach: | They use Large Language Models to generate a formal representation of the planning domain in some language, such as Planning Domain Definition Language (PDDL). |
| Outcome: | The proposed model outperforms the models directly generating plans while being robust to lexical perturbation. |
Mapping probability word problems to executable representations (2021.emnlp-main)
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Simon Suster, Pieter Fivez, Pietro Totis, Angelika Kimmig, Jesse Davis, Luc de Raedt, Walter Daelemans
| Challenge: | a recent paper addresses the problem of solving math word problems automatically . a number of approaches have been proposed for solving word problems . |
| Approach: | They employ a sequence-to-sequence model to generate intermediate representations for word problems . they then use a probabilistic programming system to provide the answer . their best performing model incorporates general-domain contextualised word representations . |
| Outcome: | The proposed model is the best performing on a declarative language and a probabilistic programming system. |
Unifying Inference-Time Planning Language Generation (2026.findings-acl)
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Prabhu Prakash Kagitha, Bo Sun, Ishan Desai, Andrew Zhu, Cassie Huang, Manling Li, Ziyang Li, Li Zhang
| Challenge: | Large language models (LLMs) are used to generate a formal representation of a plan in a planning language. |
| Approach: | They propose a unifying organizational framework based on intermediate representations to unify the inference-time LLM-as-formalizer methodology for classical planning. |
| Outcome: | The proposed framework subsumes most existing work and proposes new ones that involve syntactically similar but high-resource intermediate languages. |
GRhOOT: Ontology of Rhetorical Figures in German (2022.lrec-1)
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| Challenge: | GRhOOT is a domain ontology of rhetorical figures in the German language . the goal is to allow for easier detection of non-literal language based tasks . |
| Approach: | GRhOOT is a domain ontology of 110 rhetorical figures in the german language . the goal is to allow for easier detection and sentiment analysis . |
| Outcome: | The ontology of rhetorical figures in the German language is based on 110 rhetorical figure domains . the goal is to make the ontologies more accurate and to allow for easier detection . |
Mitigating Data Scarcity in Semantic Parsing across Languages with the Multilingual Semantic Layer and its Dataset (2024.findings-acl)
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Abelardo Carlos Martinez Lorenzo, Pere-Lluís Huguet Cabot, Karim Ghonim, Lu Xu, Hee-Soo Choi, Alberte Fernández-Castro, Roberto Navigli
| Challenge: | Large Language Models (LLMs) have advanced significantly in understanding human text, but semantic representations remain crucial for various applications. |
| Approach: | They introduce a multilingual semantic layer which decouples from disambiguation and external inventories and simplifies the task. |
| Outcome: | The proposed model reduces performance gap between languages and annotators by enabling them to understand semantic relations between concepts in any language. |