The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering

Sabrina Chiesurin, Dimitris Dimakopoulos, Marco Antonio Sobrevilla Cabezudo, Arash Eshghi, Ioannis Papaioannou, Verena Rieser, Ioannis Konstas


Abstract
Large language models are known to produce output which sounds fluent and convincing, but is also often wrong, e.g. “unfaithful” with respect to a rationale as retrieved from a knowledge base. In this paper, we show that task-based systems which exhibit certain advanced linguistic dialog behaviors, such as lexical alignment (repeating what the user said), are in fact preferred and trusted more, whereas other phenomena, such as pronouns and ellipsis are dis-preferred. We use open-domain question answering systems as our test-bed for task based dialog generation and compare several open- and closed-book models. Our results highlight the danger of systems that appear to be trustworthy by parroting user input while providing an unfaithful response.
Anthology ID:
2023.findings-acl.60
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
947–959
Language:
URL:
https://aclanthology.org/2023.findings-acl.60
DOI:
10.18653/v1/2023.findings-acl.60
Bibkey:
Cite (ACL):
Sabrina Chiesurin, Dimitris Dimakopoulos, Marco Antonio Sobrevilla Cabezudo, Arash Eshghi, Ioannis Papaioannou, Verena Rieser, and Ioannis Konstas. 2023. The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering. In Findings of the Association for Computational Linguistics: ACL 2023, pages 947–959, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering (Chiesurin et al., Findings 2023)
Copy Citation:
PDF:
https://aclanthology.org/2023.findings-acl.60.pdf