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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.05138 |
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| _version_ | 1866914410783571968 |
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| author | Go, Gregory Hok Tjoan Ly, Khang Søgaard, Anders Tabatabaei, Amin de Rijke, Maarten Chen, Xinyi |
| author_facet | Go, Gregory Hok Tjoan Ly, Khang Søgaard, Anders Tabatabaei, Amin de Rijke, Maarten Chen, Xinyi |
| contents | The rapid growth of scientific publications has made it increasingly difficult to keep literature reviews comprehensive and up-to-date. Though prior work has focused on automating retrieval and screening, the writing phase of systematic reviews remains largely under-explored, especially with regard to readability and factual accuracy. To address this, we present LiRA (Literature Review Agents), a multi-agent collaborative workflow which emulates the human literature review process. LiRA utilizes specialized agents for content outlining, subsection writing, editing, and reviewing, producing cohesive and comprehensive review articles. Evaluated on SciReviewGen and a proprietary ScienceDirect dataset, LiRA outperforms current baselines such as AutoSurvey and MASS-Survey in writing and citation quality, while maintaining competitive similarity to human-written reviews. We further evaluate LiRA in real-world scenarios using document retrieval and assess its robustness to reviewer model variation. Our findings highlight the potential of agentic LLM workflows, even without domain-specific tuning, to improve the reliability and usability of automated scientific writing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05138 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LiRA: A Multi-Agent Framework for Reliable and Readable Literature Review Generation Go, Gregory Hok Tjoan Ly, Khang Søgaard, Anders Tabatabaei, Amin de Rijke, Maarten Chen, Xinyi Computation and Language The rapid growth of scientific publications has made it increasingly difficult to keep literature reviews comprehensive and up-to-date. Though prior work has focused on automating retrieval and screening, the writing phase of systematic reviews remains largely under-explored, especially with regard to readability and factual accuracy. To address this, we present LiRA (Literature Review Agents), a multi-agent collaborative workflow which emulates the human literature review process. LiRA utilizes specialized agents for content outlining, subsection writing, editing, and reviewing, producing cohesive and comprehensive review articles. Evaluated on SciReviewGen and a proprietary ScienceDirect dataset, LiRA outperforms current baselines such as AutoSurvey and MASS-Survey in writing and citation quality, while maintaining competitive similarity to human-written reviews. We further evaluate LiRA in real-world scenarios using document retrieval and assess its robustness to reviewer model variation. Our findings highlight the potential of agentic LLM workflows, even without domain-specific tuning, to improve the reliability and usability of automated scientific writing. |
| title | LiRA: A Multi-Agent Framework for Reliable and Readable Literature Review Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.05138 |