| 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. |
| Approach: | This tutorial aims to bridge the gap between the practical performance of Large Language Models and the principled modelling of language and inference of formal methods. |
| Outcome: | This tutorial aims to bridge the gap between the performance of LLMs and the principled modelling of language and inference of formal methods. |
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| Challenge: | Language models (LMs) are at the forefront of NLP research due to their versatility across diverse tasks. |
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Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language. |
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LLMs as a synthesis between symbolic and distributed approaches to language (2025.findings-emnlp)
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| Challenge: | Pre-trained language models have enabled deep neural networks to perform natural language understanding tasks, but their performance can drastically deteriorate when logical reasoning is needed. |
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Towards Intrinsic Interpretability of Large Language Models: A Survey of Design Principles and Architectures (2026.acl-long)
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| Challenge: | Existing studies on explainable AI focus on post-hoc explanation methods that interpret trained models through external approximations. |
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| Challenge: | Existing methods for assessing the validity of explanations for NLI are time-consuming and prone to logical errors. |
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Faithful and Robust LLM-Driven Theorem Proving for NLI Explanations (2025.acl-long)
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| Challenge: | Recent work has shown that the interaction of large language models (LLMs) with theorem provers (TPs) can help verify and improve the validity of NLI explanations. |
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