Reproducibility and Generalizability Issues in Using Large Language Models for Boolean Query Generation in Systematic Reviews

Основні поняття

While large language models (LLMs) show promise for generating Boolean queries in systematic reviews, current research suffers from reproducibility and generalizability issues, highlighting the need for more transparent and robust evaluation methods.

Анотація

This research paper investigates the use of large language models (LLMs) for generating Boolean queries in systematic reviews, focusing on the reproducibility and generalizability of existing studies. The authors attempt to reproduce the findings of two recent publications by Wang et al. and Alaniz et al., which explored the use of ChatGPT for this task.

The authors created a pipeline to automatically generate Boolean queries for systematic reviews using various LLM APIs, including GPT models and open-source alternatives like Mistral and Zephyr. They tested these models on the CLEF TAR and Seed datasets, comparing their performance to the baselines reported in the original studies.

The results show that while some LLMs, particularly GPT-3.5 and GPT-4, achieved higher precision scores on the CLEF TAR dataset and better recall scores on the Seed dataset compared to the original studies, they were unable to fully reproduce the reported results. This discrepancy highlights potential issues with transparency and completeness in the original studies' methodologies.

Furthermore, the authors found significant variability in the quality and format of the generated queries, even when using fixed random seeds. This inconsistency raises concerns about the reliability and robustness of using LLMs for this task in real-world settings.

The paper concludes that while LLMs hold potential for automating Boolean query generation in systematic reviews, further research with a strong emphasis on reproducibility, transparency, and rigorous evaluation is crucial before these models can be reliably integrated into the systematic review process.

• Bibliographic Information: Staudinger, M., Kusa, W., Piroi, F., Lipani, A., & Hanbury, A. (2024). A Reproducibility and Generalizability Study of Large Language Models for Query Generation. In Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP ’24), December 9–12, 2024, Tokyo, Japan. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3673791.3698432
• Research Objective: To investigate the reproducibility and generalizability of using LLMs for Boolean query generation in systematic reviews, based on the findings of Wang et al. (2023) and Alaniz et al. (2024).
• Methodology: The authors developed a pipeline to generate Boolean queries using various LLM APIs, including GPT models and open-source alternatives. They tested these models on the CLEF TAR and Seed datasets, evaluating their performance using precision, recall, F1-score, and F3-score, and comparing the results to the baselines reported in the original studies.
• Key Findings: The authors were unable to fully reproduce the results of the previous studies, despite achieving higher precision on the CLEF TAR dataset and better recall on the Seed dataset with some LLMs. Significant variability in query quality and format was observed, even with fixed random seeds.
• Main Conclusions: While LLMs show promise for Boolean query generation, current research suffers from reproducibility and generalizability issues. More transparent and robust evaluation methods are needed to ensure the reliability of these models for systematic reviews.
• Significance: This study highlights the importance of rigorous evaluation and transparency in LLM research for information retrieval tasks, particularly in the context of systematic reviews where accuracy and reliability are paramount.
• Limitations and Future Research: The study was limited by the lack of detailed methodological information in the original papers. Future research should focus on developing standardized evaluation frameworks, exploring methods for improving the consistency of LLM-generated queries, and investigating the generalizability of these models across different review topics and domains.

Статистика

As of July 5th, 2024, the paper by Wang et al. (2023) had been cited 152 times according to Google Scholar and 111 times according to Semantic Scholar within less than a year of its publication.
23% of published systematic reviews need to be updated within two years after completion (Shojania et al., 2007).
Conducting a complete systematic review takes, on average, 67 weeks (Grant & Booth, 2009).
The median time to publication for a systematic review was 2.4 years (Falconer et al., 2016).

Цитати

"Although OpenAI and Mistral AI have extended their APIs to allow the configuration of a random seed, even this beta function does not guarantee a deterministic output and the reproduction of generated outputs."
"The inherent LLM output variability poses a challenge to the reproducibility of systematic reviews, necessitating rigorous validation of LLM-generated queries against expert strategies to ensure reliability and relevance [58]."
"While models, such as Llama [49], Alpaca [47], and Mistral [19] are open-source, their performance in domain-specific tasks and low resource setting is heavily influenced by the original datasets used for their training."

Ключові висновки, отримані з

A Reproducibility and Generalizability Study of Large Language Models for Query Generation

by Moritz Staud... о arxiv.org 11-25-2024

https://arxiv.org/pdf/2411.14914.pdf

Глибші Запити

How can the evaluation frameworks for LLM-generated Boolean queries be improved to better assess their real-world applicability in systematic reviews?

Current evaluation frameworks primarily rely on metrics like Precision, Recall, and F1-score, which, while providing a quantitative measure of accuracy, fall short of capturing the nuances of real-world systematic reviews. Here's how we can improve these frameworks:

Incorporate Domain Expert Assessment: Integrate assessments from domain experts who can evaluate the relevance and comprehensiveness of retrieved publications against the review's research question. This qualitative feedback can highlight queries that, while statistically sound, miss crucial studies or include irrelevant ones.

Evaluate Iterative Query Refinement: Systematic reviews often involve refining search strategies iteratively. Evaluation frameworks should assess how well LLMs support this process. This includes evaluating the LLM's ability to incorporate feedback, suggest alternative search terms, and adapt to evolving research questions.

Beyond Retrieval Metrics: Go beyond traditional retrieval metrics and consider:

Query Complexity and Readability: Assess the complexity of generated queries. Overly complex queries might be difficult for researchers to interpret and modify.
Time Savings and Efficiency: Quantify the time saved by using LLM-generated queries compared to manual query construction.
Bias Assessment: Develop methods to detect potential biases in LLM-generated queries, ensuring inclusivity and representation of diverse research perspectives.

Standardized Evaluation Datasets: Create larger, more diverse, and publicly available evaluation datasets specifically designed for systematic review query generation. These datasets should cover various research domains and include a range of complex research questions.

Human-in-the-Loop Evaluation: Design evaluation frameworks that consider the interaction between LLMs and human researchers. This includes evaluating how well LLMs support researchers in understanding and refining generated queries.

By incorporating these improvements, evaluation frameworks can move beyond simple accuracy metrics and provide a more holistic assessment of the real-world applicability of LLM-generated Boolean queries in systematic reviews.

Could incorporating domain-specific knowledge graphs or ontologies into the training process of LLMs improve the accuracy and consistency of generated Boolean queries?

Yes, incorporating domain-specific knowledge graphs or ontologies can significantly enhance the accuracy and consistency of LLM-generated Boolean queries. Here's how:

Enhanced Semantic Understanding: Knowledge graphs and ontologies provide structured representations of domain-specific concepts, relationships, and synonyms. Integrating this knowledge into LLM training can enable them to better understand the semantic meaning behind search terms and research questions.

Improved Term Disambiguation: LLMs often struggle with ambiguous terms that have multiple meanings in different contexts. Knowledge graphs can help disambiguate these terms by providing contextual information and relationships between concepts.

Automatic MeSH Term Mapping: Knowledge graphs can facilitate automatic mapping of keywords to relevant MeSH terms, improving the accuracy and specificity of generated queries for databases like PubMed.

Identification of Related Concepts: By leveraging relationships within knowledge graphs, LLMs can identify and suggest related concepts and synonyms that might not be explicitly mentioned in the research question, leading to more comprehensive search strategies.

Consistency and Reproducibility: Using a standardized knowledge graph or ontology as a foundation for query generation can improve the consistency and reproducibility of search strategies across different researchers and reviews.

For instance, training an LLM on a knowledge graph like the Unified Medical Language System (UMLS) can significantly improve its ability to generate accurate and consistent Boolean queries for medical systematic reviews.
However, challenges remain in effectively integrating large and complex knowledge graphs into LLM training, requiring efficient knowledge representation and reasoning techniques.

What are the ethical implications of using LLMs for automating tasks in research, and how can we ensure responsible and transparent use of these technologies?

While LLMs offer immense potential for automating research tasks, their use raises several ethical considerations:

Bias and Fairness: LLMs trained on large text corpora can inherit and amplify existing biases present in the data. This can lead to biased search results, potentially excluding relevant research from underrepresented groups or perpetuating harmful stereotypes.

Transparency and Explainability: The decision-making process of LLMs can be opaque, making it difficult to understand why a particular query was generated. This lack of transparency can hinder researchers' ability to critically evaluate and refine generated queries.

Accountability and Responsibility: Determining accountability for errors or biases in LLM-generated outputs remains a challenge. Is it the LLM developer, the researcher using the tool, or both? Clear guidelines and standards are needed to address potential harms.

Job Displacement: Automating research tasks with LLMs raises concerns about potential job displacement for researchers and information specialists. It's crucial to consider the societal impact and ensure a just transition for those affected.

Over-Reliance and Deskilling: Over-reliance on LLMs without proper understanding of their limitations can lead to deskilling of researchers, potentially hindering their ability to critically evaluate information and conduct research independently.

To ensure responsible and transparent use of LLMs in research:

Develop Bias Mitigation Techniques: Actively research and implement techniques to identify and mitigate biases in LLM training data and model outputs.

Promote Explainable AI: Develop methods to make LLM decision-making more transparent and understandable to researchers, allowing for critical evaluation and refinement of generated outputs.

Establish Clear Guidelines and Standards: Develop ethical guidelines and standards for the development, deployment, and use of LLMs in research, addressing issues of accountability, transparency, and bias.

Focus on Human-AI Collaboration: Emphasize the collaborative potential of LLMs, positioning them as tools to augment and support human researchers rather than replace them.

Promote Education and Training: Educate researchers on the capabilities, limitations, and ethical implications of LLMs, empowering them to use these technologies responsibly and critically.

By proactively addressing these ethical implications, we can harness the power of LLMs to accelerate research while upholding ethical principles and ensuring a more equitable and inclusive research ecosystem.