Several metadata attributes can introduce biases in LLM-based novelty assessment. These include:
Journal Impact Factor: Papers published in high-impact journals may be perceived as more novel due to the prestige associated with the journal, regardless of the actual novelty of the research. To mitigate this bias, the evaluation process could anonymize journal names or use a standardized scoring system that evaluates the novelty of the research independently of the journal's reputation.
Funding Sources: The presence of prestigious funding sources may influence perceptions of a paper's novelty. To address this, researchers could implement a double-blind review process where funding information is withheld during the assessment phase.
Geographical Location: The institution's geographical location may affect perceptions of novelty, with research from well-known institutions in developed countries being favored. Mitigating this bias could involve ensuring a diverse representation of institutions from various regions in the dataset.
Research Methodology: The methodology used in the research could also introduce bias, as certain methodologies may be viewed as more innovative than others. To counteract this, the evaluation criteria should focus on the novelty of the ideas presented rather than the methods employed.
Publication Language: Papers published in English may be more readily accessible and thus perceived as more novel compared to those in other languages. To mitigate this, the benchmark could include papers from non-English publications and ensure that LLMs are trained on multilingual datasets.
By recognizing these potential biases and implementing strategies to mitigate them, researchers can enhance the fairness and accuracy of LLM-based novelty assessments, leading to more equitable evaluations across diverse research outputs.