How Decoding Strategies Affect the Verifiability of Generated Text

Luca Massarelli, Fabio Petroni, Aleksandra Piktus, Myle Ott, Tim Rocktäschel, Vassilis Plachouras, Fabrizio Silvestri, Sebastian Riedel


Abstract
Recent progress in pre-trained language models led to systems that are able to generate text of an increasingly high quality. While several works have investigated the fluency and grammatical correctness of such models, it is still unclear to which extent the generated text is consistent with factual world knowledge. Here, we go beyond fluency and also investigate the verifiability of text generated by state-of-the-art pre-trained language models. A generated sentence is verifiable if it can be corroborated or disproved by Wikipedia, and we find that the verifiability of generated text strongly depends on the decoding strategy. In particular, we discover a tradeoff between factuality (i.e., the ability of generating Wikipedia corroborated text) and repetitiveness. While decoding strategies such as top-k and nucleus sampling lead to less repetitive generations, they also produce less verifiable text. Based on these finding, we introduce a simple and effective decoding strategy which, in comparison to previously used decoding strategies, produces less repetitive and more verifiable text.
Anthology ID:
2020.findings-emnlp.22
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
223–235
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.22
DOI:
10.18653/v1/2020.findings-emnlp.22
Bibkey:
Cite (ACL):
Luca Massarelli, Fabio Petroni, Aleksandra Piktus, Myle Ott, Tim Rocktäschel, Vassilis Plachouras, Fabrizio Silvestri, and Sebastian Riedel. 2020. How Decoding Strategies Affect the Verifiability of Generated Text. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 223–235, Online. Association for Computational Linguistics.
Cite (Informal):
How Decoding Strategies Affect the Verifiability of Generated Text (Massarelli et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.22.pdf
Data
FEVER