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        <title>Reward Engineering for Generating Semi-structured Explanation</title>
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    <abstract>Semi-structured explanation depicts the implicit process of a reasoner with an explicit representation. This explanation highlights how available information in a specific query is utilised and supplemented with information a reasoner produces from its internal weights towards generating an answer. Despite the recent improvements in generative capabilities of language models, producing structured explanations to verify a model’s true reasoning capabilities remains a challenge. This issue is particularly pronounced for not-so-large LMs (e.g., FLAN-T5-XXL). In this work, we first underscore the limitations of supervised fine-tuning (SFT) in tackling this challenge, and then introduce a carefully crafted reward engineering method in reinforcement learning (RL) to better address this problem. We investigate multiple reward aggregation methods and provide a detailed discussion which sheds light on the promising potential of RL for future research. Our proposed method on two semi-structured explanation generation benchmarks (ExplaGraph and COPA-SSE) achieves new state-of-the-art results.</abstract>
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%0 Conference Proceedings
%T Reward Engineering for Generating Semi-structured Explanation
%A Han, Jiuzhou
%A Buntine, Wray
%A Shareghi, Ehsan
%Y Graham, Yvette
%Y Purver, Matthew
%S Findings of the Association for Computational Linguistics: EACL 2024
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F han-etal-2024-reward
%X Semi-structured explanation depicts the implicit process of a reasoner with an explicit representation. This explanation highlights how available information in a specific query is utilised and supplemented with information a reasoner produces from its internal weights towards generating an answer. Despite the recent improvements in generative capabilities of language models, producing structured explanations to verify a model’s true reasoning capabilities remains a challenge. This issue is particularly pronounced for not-so-large LMs (e.g., FLAN-T5-XXL). In this work, we first underscore the limitations of supervised fine-tuning (SFT) in tackling this challenge, and then introduce a carefully crafted reward engineering method in reinforcement learning (RL) to better address this problem. We investigate multiple reward aggregation methods and provide a detailed discussion which sheds light on the promising potential of RL for future research. Our proposed method on two semi-structured explanation generation benchmarks (ExplaGraph and COPA-SSE) achieves new state-of-the-art results.
%U https://aclanthology.org/2024.findings-eacl.41
%P 589-602

Markdown (Informal)

[Reward Engineering for Generating Semi-structured Explanation](https://aclanthology.org/2024.findings-eacl.41) (Han et al., Findings 2024)

• Reward Engineering for Generating Semi-structured Explanation (Han et al., Findings 2024)

ACL

• Jiuzhou Han, Wray Buntine, and Ehsan Shareghi. 2024. Reward Engineering for Generating Semi-structured Explanation. In Findings of the Association for Computational Linguistics: EACL 2024, pages 589–602, St. Julian’s, Malta. Association for Computational Linguistics.