Leveraging Synthetic Experts to Generate High-Quality Edit Feedback for Improving Factual Accuracy in Clinical Summarization

Khái niệm cốt lõi

Utilizing large language models (GPT-3.5 & GPT-4) as synthetic experts to generate high-quality edit feedback, which is then used to improve the factual accuracy of weaker language models (GPT-2 & Llama-2) in clinical summarization tasks.

Tóm tắt

This study proposes an innovative pipeline that leverages large language models (LLMs) like GPT-3.5 and GPT-4 as synthetic experts to generate high-quality synthetic edit feedback. This feedback is then used to align weaker language models (LMs) like GPT-2 and Llama-2 towards factual accuracy in the clinical summarization task.
The key highlights are:

The synthetic edit feedback is generated in two directions:

High→Low: The synthetic experts add hallucinations to generate low-quality dispreferred summaries from high-quality preferred summaries.
Low→High: The synthetic experts improve the factuality of low-quality unaligned model-generated summaries to generate high-quality preferred summaries.

The generated synthetic edit feedback is used to train the weaker LMs using two alignment algorithms: DPO and SALT.

Experiments show that using the synthetic edit feedback can significantly improve the factual accuracy of the weaker LM-generated summaries. For Llama-2, DPO led to a 2.44% increase in ROUGEL and a 1.35% increase in factuality, while SALT resulted in a 2.47% increase in ROUGEL and a 2.04% increase in factuality.

For GPT-2, DPO led to a 3.04% increase in ROUGEL and a 2.93% increase in factuality, while SALT yielded a 4.04% increase in ROUGEL and a 4.64% increase in factuality.

The top-performing model achieved a 78% preference rate for factuality among human evaluators, highlighting its superior performance.

The study demonstrates the substantial potential of LLM-based synthetic edits in enhancing the factual alignment of clinical summarization, addressing the critical challenge of hallucinations in generative AI.

Thống kê

The clinical note dataset consists of 25k/3k/3k train/valid/test samples.
The weaker LMs used are GPT-2 (1.5B) and Llama-2 (7B).
The synthetic experts used are GPT-3.5 and GPT-4.

Trích dẫn

"Recent research has explored learning paradigms incorporating human feedback, such as RLHF, RLAIF, RRHF, and RAFT, to address the limitations of traditional supervised fine-tuning."
"Collecting other forms of feedback that are not directly tied to the clinician's workflow will not scale as much, this is especially true in domains requiring expert domain knowledge and with nuanced user goals."
"Generating a synthetic imitation edit feedback dataset by leveraging large (>100B parameters) GPT variants like GPT-3.5 & GPT-4 is one potential solution."

Thông tin chi tiết chính được chắt lọc từ

SYNFAC-EDIT: Synthetic Imitation Edit Feedback for Factual Alignment in Clinical Summarization

by Prakamya Mis... lúc arxiv.org 04-18-2024

https://arxiv.org/pdf/2402.13919.pdf

Yêu cầu sâu hơn

How can the proposed synthetic edit feedback generation pipeline be extended to other clinical NLP tasks beyond summarization?

The proposed synthetic edit feedback generation pipeline can be extended to other clinical NLP tasks by adapting the process to suit the specific requirements of each task. For tasks such as named entity recognition (NER), entity linking, sentiment analysis, or question answering in the clinical domain, the synthetic experts (large language models) can be trained to generate edit feedback that focuses on improving the accuracy, specificity, and relevance of the model outputs. By providing targeted prompts and feedback mechanisms tailored to each task, the synthetic experts can learn to generate high-quality edits that enhance the performance of the models in various clinical NLP applications.

What are the potential biases and limitations of using large language models as synthetic experts, and how can they be mitigated?

Using large language models as synthetic experts can introduce biases and limitations in the generated edit feedback. Some potential biases include model-generated hallucinations, overfitting to specific patterns in the training data, and amplification of existing biases present in the data. To mitigate these biases and limitations, several strategies can be employed:

Diverse Training Data: Ensure that the synthetic experts are trained on diverse and representative datasets to reduce bias and improve generalization.
Regular Evaluation: Continuously evaluate the performance of the synthetic experts on unseen data to identify and address any biases or inaccuracies.
Bias Detection Mechanisms: Implement bias detection algorithms to identify and mitigate biases in the generated edit feedback.
Human Oversight: Incorporate human annotators to review and validate the edit feedback generated by the synthetic experts to ensure accuracy and fairness.

By implementing these strategies, the biases and limitations associated with using large language models as synthetic experts can be effectively managed and minimized.

How can the synthetic edit feedback generation process be further improved to better capture the nuances of expert-level edits in the clinical domain?

To enhance the synthetic edit feedback generation process and capture the nuances of expert-level edits in the clinical domain, the following approaches can be considered:

Fine-tuning on Domain-Specific Data: Train the synthetic experts on domain-specific clinical data to improve their understanding of medical terminology, context, and nuances.
Incorporating Domain Knowledge: Integrate domain-specific knowledge bases, ontologies, or medical literature into the training process to enhance the expertise of the synthetic experts.
Feedback Loop Mechanism: Implement a feedback loop where human experts provide corrective feedback on the generated edits, allowing the synthetic experts to learn and improve over time.
Contextual Prompts: Design prompts that provide detailed context and guidelines for generating edits, ensuring that the synthetic experts consider the specific requirements of the clinical domain in their feedback.

By incorporating these strategies, the synthetic edit feedback generation process can be refined to better capture the intricacies and subtleties of expert-level edits in the clinical domain, ultimately improving the quality and accuracy of the generated feedback.