Abstract
Natural language reasoning plays an increasingly important role in improving language models’ ability to solve complex language understanding tasks. An interesting use case for reasoning is the resolution of context-dependent ambiguity. But no resources exist to evaluate how well Large Language Models can use explicit reasoning to resolve ambiguity in language. We propose to use ambiguous definite descriptions for this purpose and create and publish the first benchmark dataset consisting of such phrases. Our method includes all information required to resolve the ambiguity in the prompt, which means a model does not require anything but reasoning to do well. We find this to be a challenging task for recent LLMs. Code and data available at: https://github.com/sfschouten/exploiting-ambiguity- Anthology ID:
- 2023.findings-emnlp.296
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4479–4484
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.296
- DOI:
- 10.18653/v1/2023.findings-emnlp.296
- Cite (ACL):
- Stefan Schouten, Peter Bloem, Ilia Markov, and Piek Vossen. 2023. Reasoning about Ambiguous Definite Descriptions. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4479–4484, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Reasoning about Ambiguous Definite Descriptions (Schouten et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.findings-emnlp.296.pdf