Challenge: Existing methods for building formal semantic representations of specification texts are laborious and error-prone.
Approach: They propose to use SpecIR to model sentences appearing in NFS specification documents as IF-THEN statements and introduce a representation language to parse them.
Outcome: The proposed models achieve an F1 score of only 60.5 and 33.3 when using a state-of-the-art language model.

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Challenge: Several diagnostics help to localize the benefits of our approach.
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Challenge: Existing models of language understanding are based on explicit representations of hierarchical structure, but there are good reasons to doubt that they can be said to understand language in any meaningful way.
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Challenge: Entity vectors improve scores on basic event, while gated architectures benefit most.
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Semantic-Eval : A Semantic Comprehension Evaluation Framework for Large Language Models Generation without Training (2025.acl-long)

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Challenge: Large language models (LLMs) have emerged as key drivers of progress in the field of natural language processing.
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Mitigating Data Scarcity in Semantic Parsing across Languages with the Multilingual Semantic Layer and its Dataset (2024.findings-acl)

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Challenge: Large Language Models (LLMs) have advanced significantly in understanding human text, but semantic representations remain crucial for various applications.
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SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics (2021.findings-acl)

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Challenge: Existing models have limitations to generalize to diverse semantic phenomena, and it is unclear whether they can capture compositional meanings.
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Investigating the (De)Composition Capabilities of Large Language Models in Natural-to-Formal Language Conversion (2025.naacl-long)

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Challenge: Existing frameworks for evaluating the decomposition and composition capabilities of large language models (LLMs) in N2F are inadequate, and there are errors that can be attributed to deficiencies in natural language understanding and the learning and use of symbolic systems.
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Neuro-Symbolic Natural Language Processing (2025.emnlp-tutorials)

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Challenge: Large Language Models (LLMs) have limitations in terms of safe and controlled reasoning, interpretability and adaptability . this tutorial aims to bridge the gap between the practical performance of LLMs and the principled modelling of language and inference of formal methods.
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Challenge: Recent advances in NLP research have focused on robustness and explainability issues of their evaluation strategies.
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