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Automatic Summarization of Doctor-Patient Dialogues Using Large Language Models


Core Concepts
Generative LLMs efficiently summarize clinical dialogues through prompt tuning.
Abstract
Abstract: Automatic text summarization (ATS) aids clinicians in providing coordinated care. Approach: Summarize doctor-patient dialogues using generative large language models (LLMs). GatorTronGPT-20B model excelled in performance metrics. Introduction: Clinicians face challenges with extensive clinical documentation. ATS assists in summarizing patient information for efficient care delivery. Methods: Dataset: MTS-DIALOG dataset used for training and evaluation. Prompt Tuning: Soft prompts guide GatorTronGPT in generating summaries. Experiments and Evaluation: Grid search to optimize prompt tuning parameters. Comparison with T5-Large model on evaluation metrics. Results: GatorTronGPT-20B outperformed other models in summarization quality. Few-shot learning ability improved with more samples for prompt tuning. Discussion and Conclusions: Cost-efficient method relieves users from labor-intensive prompt engineering. Generative LLMs through prompt tuning show promise for clinical ATS.
Stats
We developed prompt-tuning algorithms to instruct generative LLMs to summarize clinical text using up to 20 billion parameters. The GatorTronGPT-20B model achieved the best performance on all evaluation metrics.
Quotes
"Prompt-based learning is the key technology that utilizes a ‘prompt’—additional instructional information added to the input data—to guide LLMs in generating text that follows these instructions." "The proposed solution has a low computing cost as the LLM parameters are not updated during prompt-tuning."

Deeper Inquiries

How can generative LLMs be further optimized for clinical text summarization beyond prompt tuning?

Generative Large Language Models (LLMs) can be further optimized for clinical text summarization by exploring additional techniques such as reinforcement learning, multi-task learning, and domain-specific pre-training. Reinforcement Learning: By incorporating reinforcement learning from human feedback (RLHF), LLMs can learn to generate more accurate and contextually relevant summaries based on real-time input from users or experts. RLHF allows the model to adapt and improve its summarization capabilities iteratively. Multi-Task Learning: Training LLMs on multiple related tasks simultaneously, such as clinical concept extraction or relation identification along with summarization, can enhance the model's understanding of medical texts and improve the quality of generated summaries. Domain-Specific Pre-training: Fine-tuning LLMs on a large corpus of domain-specific clinical data before prompt tuning can help tailor the model's language representation to healthcare contexts, leading to better performance in generating clinically relevant summaries. Customized Prompt Engineering: Developing specialized prompts that are specifically designed for different types of clinical notes or specialties can guide LLMs more effectively in generating concise and informative summaries tailored to specific medical scenarios. By combining these strategies with prompt tuning, generative LLMs can achieve higher accuracy, coherence, and relevance in summarizing complex doctor-patient dialogues into actionable clinical notes.

What are potential drawbacks or limitations of relying solely on automatic evaluation methods like Rouge and BERTScore?

While automatic evaluation metrics like Rouge and BERTScore provide objective measures of summary quality in natural language processing tasks like text summarization, they have certain drawbacks and limitations: Limited Semantic Understanding: Automatic metrics may not capture the full semantic nuances present in human-generated summaries due to their focus on surface-level features like n-gram overlap or token similarity. Lack of Contextual Understanding: These metrics do not consider contextual information or domain-specific knowledge that is crucial for evaluating the appropriateness and accuracy of generated summaries within specialized fields such as healthcare. Subjectivity Issues: Different automatic evaluation metrics may yield conflicting results based on their underlying algorithms, leading to inconsistencies in assessing summary quality across diverse datasets or models. Inability to Capture Creativity: Automatic metrics often fail to recognize creative expressions or novel ways of conveying information present in high-quality human-written summaries but absent in machine-generated ones. Overemphasis on Surface Features: Metrics like Rouge primarily focus on lexical overlaps without considering structural coherence, logical flow, or overall readability which are essential aspects of a well-crafted summary.

How might reinforcement learning from human feedback enhance the few-shot learning ability of generative LLMs?

Reinforcement Learning from Human Feedback (RLHF) offers a promising approach to enhancing the few-shot learning ability of generative Large Language Models (LLMs) by leveraging direct input from users: Iterative Improvement: RLHF enables continuous refinement through iterative interactions where humans provide feedback on generated outputs allowing the model to adjust its parameters gradually towards producing more accurate responses over time. 2.Adaptation: By incorporating user feedback into training loops during few-shot scenarios where limited labeled data is available initially; RLHF helps adapt models quickly while minimizing manual annotation efforts. 3Reduced Annotation Costs: Insteadof requiring extensive labeled examples upfront,RFLF reduces relianceon costly annotateddataby utilizinghuman judgmentsdirectlyto refineandimprove themodel’sperformanceover successiveiterations. 4Enhanced Generalizability: Through interactivelearningfromhumans,the LLMincorporatesreal-worldknowledgeandcontext,makingitmoreadaptabletovariedscenariosandgeneratingmoresophisticatedsummariesbasedonuserpreferencesorrequirements Overall,reinforcementlearningfromhumanfeedbackcanbeavitalcomponentinthefewshotlearningprocessforLLMsinclinicaltextsummarizationscenarios,enablingmodelstoquicklyadapttothenatureofthedataandspecifictaskswithoutrequiringextensivepretrainingoronboardinglargeamountsoflabeledexamples
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