Speechworthy Instruction-tuned Language Models

Hyundong Justin Cho, Nicolaas Paul Jedema, Leonardo F. R. Ribeiro, Karishma Sharma, Pedro Szekely, Alessandro Moschitti, Ruben Janssen, Jonathan May


Abstract
Current instruction-tuned language models are exclusively trained with textual preference data and thus may not be aligned to the unique requirements of other modalities, such as speech. To better align language models with the speech domain, we explore i) prompting strategies based on radio-industry best practices and ii) preference learning using a novel speech-based preference data of 20K samples collected by annotators who listen to response pairs. Both human and automatic evaluation show that both prompting and preference learning increase the speech-suitability of popular instruction tuned LLMs. More interestingly, we show that these methods are additive; combining them achieves the best win rates in head-to-head comparison, resulting in responses that are preferred or tied to the base model in 76.2% of comparisons on average. Lastly, we share lexical, syntactical, and qualitative analyses that elicit how our studied methods differ with baselines in generating more speech-suitable responses.
Anthology ID:
2024.emnlp-main.595
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10652–10670
Language:
URL:
https://aclanthology.org/2024.emnlp-main.595
DOI:
10.18653/v1/2024.emnlp-main.595
Bibkey:
Cite (ACL):
Hyundong Justin Cho, Nicolaas Paul Jedema, Leonardo F. R. Ribeiro, Karishma Sharma, Pedro Szekely, Alessandro Moschitti, Ruben Janssen, and Jonathan May. 2024. Speechworthy Instruction-tuned Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 10652–10670, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
Speechworthy Instruction-tuned Language Models (Cho et al., EMNLP 2024)
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PDF:
https://aclanthology.org/2024.emnlp-main.595.pdf