A growing swath of NLP research is tackling problems related to generating long text, including tasks such as open-ended story generation, summarization, dialogue, and more. However, we currently lack appropriate tools to evaluate these long outputs of generation models: classic automatic metrics such as ROUGE have been shown to perform poorly, and newer learned metrics do not necessarily work well for all tasks and domains of text. Human rating and error analysis remains a crucial component for any evaluation of long text generation. In this paper, we introduce FALTE, a web-based annotation toolkit designed to address this shortcoming. Our tool allows researchers to collect fine-grained judgments of text quality from crowdworkers using an error taxonomy specific to the downstream task. Using the task interface, annotators can select and assign error labels to text span selections in an incremental paragraph-level annotation workflow. The latter functionality is designed to simplify the document-level task into smaller units and reduce cognitive load on the annotators. Our tool has previously been used to run a large-scale annotation study that evaluates the coherence of long generated summaries, demonstrating its utility.
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Evaluating large language models on medical evidence summarization
Recent advances in large language models (LLMs) have demonstrated remarkable successes in zero- and few-shot performance on various downstream tasks, paving the way for applications in high-stakes domains. In this study, we systematically examine the capabilities and limitations of LLMs, specifically GPT-3.5 and ChatGPT, in performing zero-shot medical evidence summarization across six clinical domains. We conduct both automatic and human evaluations, covering several dimensions of summary quality. Our study demonstrates that automatic metrics often do not strongly correlate with the quality of summaries. Furthermore, informed by our human evaluations, we define a terminology of error types for medical evidence summarization. Our findings reveal that LLMs could be susceptible to generating factually inconsistent summaries and making overly convincing or uncertain statements, leading to potential harm due to misinformation. Moreover, we find that models struggle to identify the salient information and are more error-prone when summarizing over longer textual contexts.
more » « less- Award ID(s):
- 2145640
- NSF-PAR ID:
- 10446331
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Digital Medicine
- Volume:
- 6
- Issue:
- 1
- ISSN:
- 2398-6352
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Importance The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models’ performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets.
Objectives This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance.
Materials and Methods We evaluated these models on 2 clinical NER tasks: (1) to extract medical problems, treatments, and tests from clinical notes in the MTSamples corpus, following the 2010 i2b2 concept extraction shared task, and (2) to identify nervous system disorder-related adverse events from safety reports in the vaccine adverse event reporting system (VAERS). To improve the GPT models' performance, we developed a clinical task-specific prompt framework that includes (1) baseline prompts with task description and format specification, (2) annotation guideline-based prompts, (3) error analysis-based instructions, and (4) annotated samples for few-shot learning. We assessed each prompt's effectiveness and compared the models to BioClinicalBERT.
Results Using baseline prompts, GPT-3.5 and GPT-4 achieved relaxed F1 scores of 0.634, 0.804 for MTSamples and 0.301, 0.593 for VAERS. Additional prompt components consistently improved model performance. When all 4 components were used, GPT-3.5 and GPT-4 achieved relaxed F1 socres of 0.794, 0.861 for MTSamples and 0.676, 0.736 for VAERS, demonstrating the effectiveness of our prompt framework. Although these results trail BioClinicalBERT (F1 of 0.901 for the MTSamples dataset and 0.802 for the VAERS), it is very promising considering few training samples are needed.
Discussion The study’s findings suggest a promising direction in leveraging LLMs for clinical NER tasks. However, while the performance of GPT models improved with task-specific prompts, there's a need for further development and refinement. LLMs like GPT-4 show potential in achieving close performance to state-of-the-art models like BioClinicalBERT, but they still require careful prompt engineering and understanding of task-specific knowledge. The study also underscores the importance of evaluation schemas that accurately reflect the capabilities and performance of LLMs in clinical settings.
Conclusion While direct application of GPT models to clinical NER tasks falls short of optimal performance, our task-specific prompt framework, incorporating medical knowledge and training samples, significantly enhances GPT models' feasibility for potential clinical applications.
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