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        <title>From Sentences to Proof Trees: Leveraging Language Models for Structured Reasoning</title>
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        <namePart type="given">Aayushee</namePart>
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            <title>Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)</title>
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        <name type="personal">
            <namePart type="given">Selene</namePart>
            <namePart type="family">Baez Santamaria</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Sai</namePart>
            <namePart type="given">Ashish</namePart>
            <namePart type="family">Somayajula</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Atsuki</namePart>
            <namePart type="family">Yamaguchi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>The ability of AI systems to not only answer complex natural language questions, but also transparently justify their reasoning, is crucial for building trust and enabling effective human-AI collaboration. In domains requiring multi-hop reasoning, answers must often be constructed by combining multiple relevant sentences from a knowledge base to build an inferential path from the question toward the answer. We tackle this challenge by exploring a neuro-symbolic approach to reasoning through the generation of entailment trees – structured, step-by-step proof trees – using Large Language Models (LLMs). These trees provide interpretable justifications for the inference process. Using the EntailmentBank (CITATION) data set, we evaluated a diverse set of prompting strategies across multiple models, along with a proposal of an inference-guided prompting approach that performs well. We also fine-tuned LLMs trained specifically for proof generation by applying several data augmentation, curriculum learning, and reinforcement-guided optimization strategies. Our results show that the fine-tuned model outperforms all prompting strategies, achieving superior performance across multiple structural and semantic metrics. We also provide a detailed evaluation of which training strategies are helpful towards proof generation. Our findings highlight the importance of proof tree generation as a benchmark for evaluating structured reasoning in LLMs.</abstract>
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%0 Conference Proceedings
%T From Sentences to Proof Trees: Leveraging Language Models for Structured Reasoning
%A Gupta, Aayushee
%Y Baez Santamaria, Selene
%Y Somayajula, Sai Ashish
%Y Yamaguchi, Atsuki
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-383-8
%F gupta-2026-sentences
%X The ability of AI systems to not only answer complex natural language questions, but also transparently justify their reasoning, is crucial for building trust and enabling effective human-AI collaboration. In domains requiring multi-hop reasoning, answers must often be constructed by combining multiple relevant sentences from a knowledge base to build an inferential path from the question toward the answer. We tackle this challenge by exploring a neuro-symbolic approach to reasoning through the generation of entailment trees – structured, step-by-step proof trees – using Large Language Models (LLMs). These trees provide interpretable justifications for the inference process. Using the EntailmentBank (CITATION) data set, we evaluated a diverse set of prompting strategies across multiple models, along with a proposal of an inference-guided prompting approach that performs well. We also fine-tuned LLMs trained specifically for proof generation by applying several data augmentation, curriculum learning, and reinforcement-guided optimization strategies. Our results show that the fine-tuned model outperforms all prompting strategies, achieving superior performance across multiple structural and semantic metrics. We also provide a detailed evaluation of which training strategies are helpful towards proof generation. Our findings highlight the importance of proof tree generation as a benchmark for evaluating structured reasoning in LLMs.
%U https://aclanthology.org/2026.eacl-srw.71/
%P 967-981

Markdown (Informal)

[From Sentences to Proof Trees: Leveraging Language Models for Structured Reasoning](https://aclanthology.org/2026.eacl-srw.71/) (Gupta, EACL 2026)

• From Sentences to Proof Trees: Leveraging Language Models for Structured Reasoning (Gupta, EACL 2026)

ACL

• Aayushee Gupta. 2026. From Sentences to Proof Trees: Leveraging Language Models for Structured Reasoning. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 967–981, Rabat, Morocco. Association for Computational Linguistics.