Papers with semantic entailment
Limits for learning with language models (2023.starsem-1)
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| Challenge: | Recent studies show that large language models fail to capture important aspects of linguistic meaning . authors argue that LLMs cannot learn fundamental semantic properties defined in formal semantics . |
| Approach: | They propose a theoretical explanation for some of the observed failings of large language models . they show that LLMs cannot learn certain fundamental semantic properties . |
| Outcome: | The proposed model fails to learn semantic entailment and consistency as defined in formal semantics, the authors argue . their model fails on tasks that require engorgements and deep linguistic understanding, they argue - but not on universal quantification. |
COGEN: Abductive Commonsense Language Generation (2023.acl-short)
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| Challenge: | Existing training methods for NLP models to perform on two main tasks are needed to introduce these capabilities into the field of reasoning. |
| Approach: | They propose a model that integrates commonsense reasoning with contextual filtering to improve the inference. |
| Outcome: | The proposed model outperforms existing models and sets new state-of-the-art in regards to alphaNLI and alphaNGG tasks. |
Transform Retrieval for Textual Entailment in RAG (2025.naacl-short)
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| Challenge: | Existing retrieval methods prioritize relevance without ensuring the retrieved documents semantically support answering the queries. |
| Approach: | They propose a novel approach to improve Textual Entailment Retrieval within the framework of Retri-Augmented Generation (RAG) they transform query embeddings to better align with semantic entailment without re-encoding the document corpus. |
| Outcome: | The proposed approach consistently approaches the skyline across multiple datasets, demonstrating its strength in many-to-many retrieval scenarios. |