Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly Publications (2409.16605v1)

Abstract: Recent studies have evaluated the creativity/novelty of LLMs primarily from a semantic perspective, using benchmarks from cognitive science. However, accessing the novelty in scholarly publications is a largely unexplored area in evaluating LLMs. In this paper, we introduce a scholarly novelty benchmark (SchNovel) to evaluate LLMs' ability to assess novelty in scholarly papers. SchNovel consists of 15000 pairs of papers across six fields sampled from the arXiv dataset with publication dates spanning 2 to 10 years apart. In each pair, the more recently published paper is assumed to be more novel. Additionally, we propose RAG-Novelty, which simulates the review process taken by human reviewers by leveraging the retrieval of similar papers to assess novelty. Extensive experiments provide insights into the capabilities of different LLMs to assess novelty and demonstrate that RAG-Novelty outperforms recent baseline models.

• The paper introduces the RAG-Novelty method, significantly outperforming baselines in assessing scholarly novelty using title and abstract data.
• It leverages a benchmark of 15,000 paper pairs across six academic fields to simulate human novelty judgments via retrieval augmentation.
• Findings reveal that pairwise evaluation and advanced prompting techniques, including self-reflection, enhance LLM performance despite metadata biases.

Evaluating and Enhancing LLMs for Novelty Assessment in Scholarly Publications

The paper presents a focused exploration into the ability of LLMs to assess novelty in scholarly publications, a dimension of AI evaluation that has been largely neglected despite the prevalent use of LLMs in various creative tasks. The authors introduce the Scholarly Novelty Benchmark (SchNovel), deploying a dataset of 15,000 paper pairs sourced from arXiv, across six academic fields, and emphasize the implementation and performance of a novel method termed RAG-Novelty.

Benchmark Design and Assumptions

The novelty assessment hinges on a straightforward assumption: within each pair, the more recently published paper is assumed to be more novel. This premise facilitates the evaluation of LLMs in recognizing novelty without the necessity of full-text analysis—a pragmatic but challenging approach as it relies solely on titles and abstracts. The dataset spans a range of publication gaps from 2 to 10 years, allowing a nuanced view of how temporal proximity affects perceived novelty.

RAG-Novelty and Experimental Insights

The RAG-Novelty method diverges from conventional evaluation techniques by incorporating retrieval-augmented generation. It simulates human review processes by retrieving relevant papers and using their publication dates to enhance novelty predictions. Experimental results clearly favor RAG-Novelty over other baseline models, underscoring its efficacy in elevating performance metrics, particularly in fields like computer science and quantitative finance.

Comparative Analysis of Methods

The paper thoroughly investigates several comparison approaches, notably distinguishing between pointwise and pairwise methods, with pairwise evaluations proving superior. Furthermore, advanced prompting techniques such as self-reflection and LLM discussion demonstrate varying degrees of effectiveness, reflecting on the contextual understanding imperative for novelty assessment. Self-consistency yields notable improvements, highlighting the value of sampling diverse reasoning paths.

Influence of Metadata and LLM Models

In assessing the effects of metadata, the paper reveals intriguing biases, especially concerning affiliations. LLMs exhibit a predisposition toward papers from top research institutions, indicating a potential bias that could impact fair evaluation. Additionally, the paper finds that different LLMs, including ChatGPT-4o-mini and its counterparts, grapple with position bias, showcasing varied strengths and weaknesses across the spectrum of available models.

Theoretical and Practical Implications

The authors’ work has significant implications, both theoretically and practically. Theoretically, it provides a foundation for understanding the alignment between LLM capabilities and nuanced academic tasks such as novelty assessment. Practically, the findings advocate for specific methodological adjustments to improve LLM performance, notably through contextual and metadata incorporation.

Future Directions

The paper suggests further expansion of the SchNovel dataset, enhancement of LLM understanding through full-text analysis, and an exploration of systemic biases within LLMs. Future work might also investigate real-time updates to LLM knowledge bases, better aligning model predictions with evolving research landscapes.

Overall, this research provides valuable insights into the novel application of LLMs in the domain of scholarly publication analysis, offering methodologies and benchmarks that advance both the practical utility and theoretical understanding of these AI systems.