Comprehensive Evaluation of Large Language Models for Biomedical Natural Language Processing: Benchmarks, Baselines, and Recommendations

Concepts de base

State-of-the-art fine-tuning approaches outperformed zero- and few-shot large language models in most biomedical NLP tasks, but closed-source LLMs like GPT-3.5 and GPT-4 achieved better performance in reasoning-related tasks and competitive accuracy in generation-related tasks.

Résumé

The authors conducted a comprehensive evaluation of four representative large language models (LLMs) - GPT-3.5, GPT-4, LLaMA 2, and PMC LLaMA - across 12 biomedical NLP datasets covering six applications: named entity recognition, relation extraction, multi-label document classification, question answering, text summarization, and text simplification.

The evaluation was performed under four settings: zero-shot, static few-shot, dynamic K-nearest few-shot, and fine-tuning. The results showed that state-of-the-art fine-tuning approaches outperformed zero- and few-shot LLMs in most biomedical NLP tasks, achieving a macro-average of 0.6531 compared to the highest LLM performance of 0.4862 under zero/few-shot settings.

However, closed-source LLMs like GPT-3.5 and GPT-4 demonstrated better zero- and few-shot performance in reasoning-related tasks such as medical question answering, where they outperformed the reported state-of-the-art results. These LLMs also exhibited competitive accuracy and readability in text summarization and simplification tasks, as well as in semantic understanding-related tasks like document-level text classification.

In contrast, open-sourced LLMs like LLaMA 2 did not show robust zero- and few-shot performance, requiring fine-tuning to bridge the performance gap for biomedical NLP tasks. The evaluation also indicated limited performance benefits for creating domain-specific LLMs like PMC LLaMA.

The qualitative evaluation revealed that LLMs frequently generated prevalent missing, inconsistent, and hallucinated responses, with over 30% of responses being hallucinated and 22% inconsistent on a multi-label document classification dataset.

Based on these results, the authors provide specific recommendations on the best practices for using LLMs in biomedical NLP applications and make all relevant data, models, and results publicly available to the community.

Stats

PubMed alone sees an increase of approximately 5,000 articles every day, totaling over 36 million as of March 2024.
In specialized fields such as COVID-19, roughly 10,000 dedicated articles are added each month, bringing the total to over 0.4 million as of March 2024.
A single entity such as Long COVID can be referred to using 763 different terms.
The term "AP2" can refer to a gene, a chemical, or a cell line.

Citations

"The biomedical literature is rapidly expanding, posing a significant challenge for manual curation and knowledge discovery."
"Biomedical Natural Language Processing (BioNLP) has emerged as a powerful solution, enabling the automated extraction of information and knowledge from this extensive literature."
"Currently, there is a lack of baseline performance data, benchmarks, and practical recommendations for using LLMs in the biomedical domain."

Idées clés tirées de

Large language models in biomedical natural language processing: benchmarks, baselines, and recommendations

by Qingyu Chen,... à arxiv.org 09-24-2024

https://arxiv.org/pdf/2305.16326.pdf

Questions plus approfondies

How can the data and evaluation paradigms in biomedical NLP be revisited to better leverage the strengths of large language models?

To effectively leverage the strengths of large language models (LLMs) in biomedical natural language processing (BioNLP), it is essential to revisit the existing data and evaluation paradigms. Current paradigms are primarily tailored to supervised methods, which may not fully exploit the capabilities of LLMs that excel in zero-shot and few-shot learning scenarios. Here are several strategies to enhance these paradigms:

Incorporation of Diverse Data Sources: Expanding the datasets used for training and evaluation to include a wider variety of biomedical literature, clinical notes, and patient records can help LLMs generalize better. This includes integrating unstructured data and leveraging semi-supervised or unsupervised learning techniques to utilize vast amounts of unlabeled data.

Task-Specific Benchmarking: Establishing benchmarks that specifically target tasks where LLMs demonstrate superior performance, such as reasoning and semantic understanding, can provide a more accurate assessment of their capabilities. This could involve creating new datasets that focus on complex question answering or multi-document summarization.

Qualitative Evaluation Metrics: Shifting the focus from solely quantitative metrics, such as F1-scores, to qualitative assessments that evaluate the quality of generated outputs is crucial. This includes manual reviews of LLM outputs for accuracy, completeness, and readability, which can provide insights into the practical utility of these models in real-world applications.

Dynamic Evaluation Frameworks: Implementing dynamic evaluation frameworks that adapt to the specific context of the task can enhance the assessment of LLMs. For instance, using context-aware prompts and dynamic few-shot learning approaches can help tailor the evaluation to the nuances of biomedical language.

Community Collaboration: Encouraging collaboration within the biomedical community to share datasets, evaluation protocols, and best practices can foster reproducibility and innovation. Open access to data and models will facilitate comparative studies and accelerate the development of more effective LLMs in BioNLP.

By revisiting these paradigms, the biomedical field can better harness the potential of LLMs, leading to improved performance in various applications such as named entity recognition, relation extraction, and question answering.

What are the potential risks associated with the prevalent missing, inconsistent, and hallucinated outputs from large language models in biomedical and clinical applications, and how can they be mitigated?

The prevalent issues of missing, inconsistent, and hallucinated outputs from large language models (LLMs) pose significant risks in biomedical and clinical applications. These risks can have serious implications for patient safety, clinical decision-making, and the integrity of biomedical research. Here are the key risks and potential mitigation strategies:

Patient Safety Risks: Hallucinated outputs may lead to incorrect medical advice or misdiagnosis, jeopardizing patient safety. For instance, if an LLM generates inaccurate information regarding drug interactions or treatment protocols, it could result in harmful clinical decisions.
Mitigation: Implementing robust validation processes where LLM outputs are cross-checked against trusted medical databases and guidelines can help ensure accuracy. Additionally, integrating LLMs with expert systems that require human oversight can provide a safety net for critical applications.

Inconsistency in Outputs: Inconsistent responses to similar queries can undermine trust in LLMs, particularly in clinical settings where consistency is crucial for reliable decision-making.
Mitigation: Standardizing prompts and employing controlled vocabularies can help reduce variability in outputs. Training LLMs on curated datasets that emphasize consistency in language and terminology can also improve reliability.

Misinformation and Hallucinations: The generation of hallucinated content, where LLMs produce plausible but false information, can mislead healthcare professionals and researchers.
Mitigation: Developing advanced filtering techniques to identify and flag potential hallucinations is essential. Additionally, enhancing the training data quality and incorporating domain-specific knowledge can help LLMs generate more accurate and contextually relevant outputs.

Lack of Transparency: The opaque nature of LLM decision-making can make it difficult to understand how outputs are generated, complicating the identification of errors.
Mitigation: Promoting transparency through explainable AI techniques can help users understand the rationale behind LLM outputs. Providing clear documentation and guidelines on the limitations of LLMs can also set appropriate expectations for users.

By addressing these risks through targeted mitigation strategies, the biomedical and clinical fields can enhance the reliability and safety of LLM applications, ultimately improving patient outcomes and research integrity.

What are the implications of the performance differences between closed-source and open-source large language models for the broader adoption and democratization of these technologies in the biomedical domain?

The performance differences between closed-source and open-source large language models (LLMs) have significant implications for their adoption and democratization in the biomedical domain. Here are the key considerations:

Access and Equity: Closed-source models, such as GPT-4, often demonstrate superior performance in various BioNLP tasks, particularly in reasoning and question answering. However, their accessibility is limited due to licensing fees and usage restrictions, which can create disparities in access to advanced technologies among researchers and institutions.
Implication: The reliance on closed-source models may hinder the democratization of AI technologies in the biomedical field, as smaller institutions or researchers with limited funding may not afford access. This could exacerbate existing inequalities in research capabilities and innovation.

Innovation and Collaboration: Open-source models, like LLaMA 2 and PMC LLaMA, provide researchers with the flexibility to modify and adapt the models for specific tasks. While they may not match the performance of closed-source models without fine-tuning, they foster innovation and collaboration within the community.
Implication: The ability to customize open-source models encourages experimentation and the development of domain-specific applications, which can lead to advancements in BioNLP. This collaborative environment can accelerate the pace of research and improve the overall quality of biomedical applications.

Transparency and Trust: Open-source models offer greater transparency, allowing researchers to scrutinize the underlying algorithms and training data. This transparency is crucial in the biomedical domain, where trust in AI systems is paramount.
Implication: The ability to audit and understand open-source models can enhance user confidence and facilitate regulatory compliance. In contrast, the opaque nature of closed-source models may raise concerns about accountability and reliability in critical applications.

Performance Trade-offs: While closed-source models may provide better performance in certain tasks, the trade-offs in terms of cost and accessibility must be considered. Open-source models may require additional fine-tuning and resources to achieve comparable performance, but they can be more cost-effective in the long run.
Implication: Organizations must weigh the benefits of immediate high performance against the long-term advantages of open-source solutions that promote sustainability and community engagement. This decision will shape the future landscape of AI in the biomedical field.

In summary, the performance differences between closed-source and open-source LLMs have profound implications for access, innovation, transparency, and sustainability in the biomedical domain. Balancing these factors will be crucial for fostering a more equitable and effective integration of AI technologies in healthcare and research.