SciRepEval: A Multi-Format Benchmark for Scientific Document Representations (2211.13308v4)

Abstract: Learned representations of scientific documents can serve as valuable input features for downstream tasks without further fine-tuning. However, existing benchmarks for evaluating these representations fail to capture the diversity of relevant tasks. In response, we introduce SciRepEval, the first comprehensive benchmark for training and evaluating scientific document representations. It includes 24 challenging and realistic tasks, 8 of which are new, across four formats: classification, regression, ranking and search. We then use this benchmark to study and improve the generalization ability of scientific document representation models. We show how state-of-the-art models like SPECTER and SciNCL struggle to generalize across the task formats, and that simple multi-task training fails to improve them. However, a new approach that learns multiple embeddings per document, each tailored to a different format, can improve performance. We experiment with task-format-specific control codes and adapters and find they outperform the existing single-embedding state-of-the-art by over 2 points absolute. We release the resulting family of multi-format models, called SPECTER2, for the community to use and build on.

• The paper introduces a novel multi-embedding strategy that improves performance by 1.4 to 2 points over single-embedding models.
• It establishes a diverse benchmark of 24 tasks including classification, regression, ranking, and search to evaluate scientific document representations.
• The framework leverages control codes and adapters to enhance both computational efficiency and task generalization in real-world applications.

SciRepEval: A Methodological Framework for Scientific Document Representation

The paper "SciRepEval: A Multi-Format Benchmark for Scientific Document Representations" presents a systematic benchmark aimed at evaluating and advancing the state of scientific document representations. This framework, named SciRepEval, encompasses a diverse set of 24 tasks with various formats including classification, regression, proximity-based ranking, and ad-hoc search. This diversity addresses the limitations of existing benchmarks that often focus on narrow or closely related tasks and mitigates the risk of overfitting to a single type of task.

Dataset and Task Composition

SciRepEval aggregates tasks from multiple domains with a strong emphasis on practical use cases reflective of real-world scientific document processing requirements. Key formats in this benchmark include:

• Classification: Tasks such as MeSH Descriptors and Fields of Study (FoS) allow evaluation across multi-class and multi-label paradigms.
• Regression: Tasks such as predicting citation counts and peer-review scores.
• Proximity-Based Ranking: Tasks such as citation prediction and author disambiguation.
• Ad-hoc Search: Tasks including TREC-CoVID and NFCorpus to gauge the ability of embeddings to facilitate document retrieval.

Methodological Advancements: Multi-Embedding Strategy

The paper introduces the novel approach of employing multiple embeddings per document, each tailored to different formats. Existing models like SPECTER and SciNCL, which condense document information into a single vector, often underperform on tasks with varying objectives. To address this, the authors explore specialized embeddings driven by multi-task learning, finding that format-specific embeddings enhance generalization across diverse tasks.

Experimental Framework

The authors conduct exhaustive experiments to validate their hypothesis. They find that task-format-specific control codes and adapter methods significantly outperform existing single-embedding methods, such as SPECTER and SciNCL, by over 2 points in absolute performance. Control codes utilize token-level indicators to inform the model of the task format, whereas adapters inject task-specific modules within the transformer architecture. The combined approach of using adapters along with control codes yields the best results, showcasing a balanced improvement in both computational efficiency and task performance.

Numerical Insights and Performance Implications

The authors report robust quantitative results that underscore the benefits of their methodological innovations. For instance, their proposed methods (MTL CTRL and Adapters) achieve performance gains of 1.4 to 2 points over simpler multi-task learning (MTL CLS), which produces a single representation. Moreover, the ensemble combination of the best-performing formats achieves further incremental improvements. These gains translate to practical enhancements in tasks like ad-hoc search and document classification, broadening the utility of scientific document embeddings in production environments.

Broader Implications and Future Developments

Theoretical implications of this work include advancing the understanding of task diversity and its impact on model generalization. From a practical standpoint, the released benchmarks and models provide a foundational resource for the community, facilitating standardized evaluations and inspiring future research on more versatile document representation methods.

Moving forward, the authors highlight several avenues such as exploring richer document context including full-text features, extending the benchmark to cover more task formats like question answering, and even verifying benchmark findings through real-world application deployments.

In summary, SciRepEval sets a new standard in evaluating scientific document representations by embracing task diversity and format-specific embeddings. Through rigorous benchmarking and innovative modeling techniques, the paper significantly advances the landscape of scientific document processing, providing a resourceful framework for continued research and development in AI-driven document representation.