@inproceedings{cho-etal-2024-speechworthy,
title = "Speechworthy Instruction-tuned Language Models",
author = "Cho, Hyundong and
Jedema, Nicolaas and
Ribeiro, Leonardo and
Sharma, Karishma and
Szekely, Pedro and
Moschitti, Alessandro and
Janssen, Ruben and
May, Jonathan",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.595",
pages = "10652--10670",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Speechworthy Instruction-tuned Language Models
%A Cho, Hyundong
%A Jedema, Nicolaas
%A Ribeiro, Leonardo
%A Sharma, Karishma
%A Szekely, Pedro
%A Moschitti, Alessandro
%A Janssen, Ruben
%A May, Jonathan
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F cho-etal-2024-speechworthy
%X 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.
%U https://aclanthology.org/2024.emnlp-main.595
%P 10652-10670
Markdown (Informal)
[Speechworthy Instruction-tuned Language Models](https://aclanthology.org/2024.emnlp-main.595) (Cho et al., EMNLP 2024)
ACL
- Hyundong Cho, Nicolaas Jedema, Leonardo 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.