Efficient Performance Tracking: Leveraging Large Language Models for Automated Construction of Scientific Leaderboards (2409.12656v1)

Abstract: Scientific leaderboards are standardized ranking systems that facilitate evaluating and comparing competitive methods. Typically, a leaderboard is defined by a task, dataset, and evaluation metric (TDM) triple, allowing objective performance assessment and fostering innovation through benchmarking. However, the exponential increase in publications has made it infeasible to construct and maintain these leaderboards manually. Automatic leaderboard construction has emerged as a solution to reduce manual labor. Existing datasets for this task are based on the community-contributed leaderboards without additional curation. Our analysis shows that a large portion of these leaderboards are incomplete, and some of them contain incorrect information. In this work, we present SciLead, a manually-curated Scientific Leaderboard dataset that overcomes the aforementioned problems. Building on this dataset, we propose three experimental settings that simulate real-world scenarios where TDM triples are fully defined, partially defined, or undefined during leaderboard construction. While previous research has only explored the first setting, the latter two are more representative of real-world applications. To address these diverse settings, we develop a comprehensive LLM-based framework for constructing leaderboards. Our experiments and analysis reveal that various LLMs often correctly identify TDM triples while struggling to extract result values from publications. We make our code and data publicly available.

• The paper demonstrates an automated framework for constructing scientific leaderboards by extracting and normalizing TDM triples from research papers using LLMs.
• It employs a multi-stage process including TDMR extraction, normalization across various scenarios, and ranking of performance metrics from 43 NLP papers.
• Experiments reveal that GPT-4 Turbo outperforms other LLMs, underscoring challenges in extracting result values and pointing to future improvements in dynamic normalization.

Efficient Performance Tracking: Leveraging LLMs for Automated Construction of Scientific Leaderboards

Introduction

The rapid increase in scientific publications presents a formidable challenge for the research community in tracking and comparing the performance of state-of-the-art (SOTA) methods. Manual curation and maintenance of scientific leaderboards, which are essential for monitoring research progress, has become impractical. The paper, "Efficient Performance Tracking: Leveraging LLMs for Automated Construction of Scientific Leaderboards," addresses this challenge by introducing an automated framework for constructing scientific leaderboards leveraging LLMs. Notably, the paper introduces SciLead, a manually curated dataset with comprehensive annotations, which serves as the foundation for their experimental evaluation.

SciLead Dataset

SciLead sets a new standard for scientific leaderboard datasets by addressing the completeness and accuracy issues prevalent in existing datasets. Consisting of 43 NLP papers and resulting in 27 leaderboards, SciLead ensures exhaustive annotation of all unique Task-Dataset-Metric (TDM) triples and corresponding results. This comprehensive approach mitigates the shortcomings of community-contributed leaderboards that often contain incomplete and erroneous information.

Framework for Leaderboard Construction

The framework proposed in the paper is a multi-stage process designed to handle various real-world scenarios, including the fully defined, partially defined, and cold start states of TDM triples.

1. TDMR Extraction: This initial stage involves parsing PDFs of scientific papers, extracting text chunks, and using a retrieval-augmented generation (RAG) approach to identify TDMR tuples. The use of dense retrieval models ensures that relevant text chunks are accurately retrieved, and LLMs are prompted to extract the required tuples.
2. Normalization: Given that different papers often use varying terminologies for identical tasks, datasets, or metrics, normalization is critical. The paper explores three settings:
   • Fully Pre-defined TDM Triples: Direct matching of extracted TDM triples to a pre-defined taxonomy.
   • Partially Pre-defined TDM Triples: Dynamic updating of an initially reduced TDM entity list to simulate the evolution of research terms.
   • Cold Start: Starting without any pre-defined TDM taxonomy, where the framework dynamically constructs new entities.
3. Leaderboard Construction: After normalization, the framework ranks the performance of methods for each unique TDM triple. This ranking is crucial for contextualizing the performance of different methods on standardized benchmarks, and only leaderboards with substantial entries are retained to ensure relevance.

Experimental Evaluation

The evaluation encompasses several metrics to gauge the extraction and normalization accuracy, as well as the quality of constructed leaderboards:

• Exact Tuple Match (ETM): Precision and recall of the complete TDMR tuples extracted.
• Individual Item Match (IIM): Precision and recall for each component of the TDMR tuples to identify partial extraction errors.
• Leaderboard Evaluation: Leaderboard recall, paper and result coverage, and average overlap (AO) between gold and constructed leaderboards.

Results and Analysis

The experiments reveal that GPT-4 Turbo consistently outperforms other LLMs (Llama-2, Mixtral, and Llama-3) in both TDMR extraction and leaderboard construction. Notably, while LLMs perform well in extracting task, dataset, and metric names, result values pose a significant challenge. The framework demonstrates robust performance across diverse normalization settings, although the partially pre-defined and cold start settings are inherently more complex and thus yield lower scores compared to the fully pre-defined setting.

Discussion and Future Directions

The paper highlights several significant findings:

• LLMs' ability to accurately extract scientific information and construct leaderboards can alleviate the manual effort required for this task.
• The dynamic nature of research necessitates adaptive normalization strategies, an area where future work could focus on enhancing the robustness of entity matching in evolving contexts.
• The cold start scenario is particularly promising for fields where benchmarking standards are still emerging, emphasizing the generalizability of the framework.

Future research could aim to expand the scope of LLM-based leaderboard construction to other scientific domains beyond NLP and explore the integration of more sophisticated retrieval and extraction mechanisms. The potential to incorporate domain-specific heuristics and fine-tuning can also be explored to improve the extraction of result values, which remains a bottleneck.

Conclusion

This paper presents a meticulous approach to leveraging LLMs for the automated construction of scientific leaderboards, addressing a critical need in the research community. By introducing SciLead and demonstrating robust experimental results, the authors lay a strong foundation for future work in automating research progress tracking and benchmarking, ultimately facilitating more efficient and accurate scientific discourse.