Abstract
Despite impressive advances in Natural Language Generation (NLG) and Large Language Models (LLMs), researchers are still unclear about important aspects of NLG evaluation. To substantiate this claim, I examine current classifications of hallucination and omission in data-text NLG, and I propose a logic-based synthesis of these classfications. I conclude by highlighting some remaining limitations of all current thinking about hallucination and by discussing implications for LLMs.- Anthology ID:
- 2024.cl-2.10
- Volume:
- Computational Linguistics, Volume 50, Issue 2 - June 2023
- Month:
- June
- Year:
- 2024
- Address:
- Cambridge, MA
- Venue:
- CL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 807–816
- Language:
- URL:
- https://aclanthology.org/2024.cl-2.10
- DOI:
- 10.1162/coli_a_00509
- Cite (ACL):
- Kees van Deemter. 2024. The Pitfalls of Defining Hallucination. Computational Linguistics, 50(2):807–816.
- Cite (Informal):
- The Pitfalls of Defining Hallucination (Deemter, CL 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.cl-2.10.pdf