Automated citation recommendation tools encourage questionable citations

Horbach, Serge P. J. M.; Maatman, Freek J W Oude; Halffman, Willem; Hepkema, Wytske M

doi:10.1093/reseval/rvac016

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…In essence, these systems are necessarily conservative, favoring the status-quo and potentially skewed approaches already present in current and past discourse. This issue resembles concerns voiced regarding other modes of automation in scholarly work (e.g., citation recommendation tools [ 12 ] or those that aim to detect erroneous citations [ 13 ]).…”

Section: Roles Of Reviewers In Peer Reviewmentioning

confidence: 99%

Fighting reviewer fatigue or amplifying bias? Considerations and recommendations for use of ChatGPT and other large language models in scholarly peer review

Hosseini

Horbach

2023

Res Integr Peer Rev

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Background The emergence of systems based on large language models (LLMs) such as OpenAI’s ChatGPT has created a range of discussions in scholarly circles. Since LLMs generate grammatically correct and mostly relevant (yet sometimes outright wrong, irrelevant or biased) outputs in response to provided prompts, using them in various writing tasks including writing peer review reports could result in improved productivity. Given the significance of peer reviews in the existing scholarly publication landscape, exploring challenges and opportunities of using LLMs in peer review seems urgent. After the generation of the first scholarly outputs with LLMs, we anticipate that peer review reports too would be generated with the help of these systems. However, there are currently no guidelines on how these systems should be used in review tasks. Methods To investigate the potential impact of using LLMs on the peer review process, we used five core themes within discussions about peer review suggested by Tennant and Ross-Hellauer. These include 1) reviewers’ role, 2) editors’ role, 3) functions and quality of peer reviews, 4) reproducibility, and 5) the social and epistemic functions of peer reviews. We provide a small-scale exploration of ChatGPT’s performance regarding identified issues. Results LLMs have the potential to substantially alter the role of both peer reviewers and editors. Through supporting both actors in efficiently writing constructive reports or decision letters, LLMs can facilitate higher quality review and address issues of review shortage. However, the fundamental opacity of LLMs’ training data, inner workings, data handling, and development processes raise concerns about potential biases, confidentiality and the reproducibility of review reports. Additionally, as editorial work has a prominent function in defining and shaping epistemic communities, as well as negotiating normative frameworks within such communities, partly outsourcing this work to LLMs might have unforeseen consequences for social and epistemic relations within academia. Regarding performance, we identified major enhancements in a short period and expect LLMs to continue developing. Conclusions We believe that LLMs are likely to have a profound impact on academia and scholarly communication. While potentially beneficial to the scholarly communication system, many uncertainties remain and their use is not without risks. In particular, concerns about the amplification of existing biases and inequalities in access to appropriate infrastructure warrant further attention. For the moment, we recommend that if LLMs are used to write scholarly reviews and decision letters, reviewers and editors should disclose their use and accept full responsibility for data security and confidentiality, and their reports’ accuracy, tone, reasoning and originality.

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Section: Roles Of Reviewers In Peer Reviewmentioning

confidence: 99%

Fighting reviewer fatigue or amplifying bias? Considerations and recommendations for use of ChatGPT and other large language models in scholarly peer review

Hosseini

Horbach

2023

Res Integr Peer Rev

Self Cite

View full text Add to dashboard Cite

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“…Another future direction for further improving Relatedly is to analyze the importance of each reference in the context of the paragraph they were mentioned. For example, NLP techniques such as [70] could be used to estimate the level of influence of each references in a paragraph, so that Relatedly could mitigate the effects of bulk and passing citations [26,27,37].…”

Section: Limitations and Future Workmentioning

confidence: 99%

Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections

Palani,

Naik,

Downey

et al. 2023

Preprint

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Scholars who want to research a scientific topic must take time to read, extract meaning, and identify connections across many papers. As scientific literature grows, this becomes increasingly challenging. Meanwhile, authors summarize prior research in papers' related work sections, though this is scoped to support a single paper. A formative study found that while reading multiple related work paragraphs helps overview a topic, it is hard to navigate overlapping and diverging references and research foci. In this work, we design a system, Relatedly, that scaffolds exploring and reading multiple related work paragraphs on a topic, with features including dynamic re-ranking and highlighting to spotlight unexplored dissimilar information, auto-generated descriptive paragraph headings, and low-lighting of redundant information. From a within-subjects user study (n=15), we found that scholars generate more coherent, insightful, and comprehensive topic outlines using Relatedly compared to a baseline paper list. CCS CONCEPTS• Human-centered computing → Graphical user interfaces.

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