- The paper introduces MEDSAGE, which leverages LLM-generated synthetic dialogues to mimic ASR errors and enhance summarization accuracy.
- It employs in-context learning and controlled error modeling to simulate realistic ASR error types such as insertions, deletions, and substitutions.
- Experimental results show up to a 16% F1 score improvement in domain-specific entity recognition, confirming the method’s robustness for clinical use.
MEDSAGE: Robust Medical Dialogue Summarization with Synthetic Data
The increasing ubiquity of Automatic Speech Recognition (ASR) systems in converting spoken medical dialogues into text has opened new avenues for facilitating healthcare documentation. However, the susceptibility of ASR systems to introduce errors poses a significant barrier to their efficacy, particularly in clinical summarization tasks. These errors can transmute into inaccuracies in electronic medical records, which are critical for patient care. The paper "MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues" introduces a novel approach to address these challenges by leveraging LLMs to generate synthetic dialogues that mimic ASR-induced errors.
Background and Challenges
ASR errors, particularly in low-resource domains such as healthcare, substantially hamper tasks where accuracy is paramount. Conventional data augmentation strategies are impractical due to limited availability of authentic medical dialogue audio recordings. Moreover, direct improvements to ASR systems require extensive supervised data, which is often limited by privacy and ethical constraints. Black-box approaches and post-processing ASR outputs with LLMs have been explored, yet they either necessitate large-scale LLMs beyond 100 billion parameters or risk introducing hallucinations, thereby degrading summary quality.
MEDSAGE Approach
The proposed MEDSAGE method circumvents these challenges by using LLMs to simulate ASR-like errors in data augmentation processes. By utilizing in-context learning, LLMs are instructed to generate synthetic dialogues with ASR-type errors based on a limited number of real dialogue examples. This approach does not require extensive domains of audio recordings since it relies on text-based learning from given dialogues, leveraging LLMs' capabilities to model realistic noise.
Synthetic Dialogue Generation
MEDSAGE involves instructing LLMs to generate ASR-like errors by pairing clean transcripts with ASR output examples. The paper presents a methodology where quantitative error modeling is combined with a tagging syntax to guide LLM's noise generation. This controlled method allows the manipulation of error types—insertions, deletions, and substitutions—ensuring the synthetic dialogues reflect the actual ASR error profiles.
Experimental Evaluation
The effectiveness of the MEDSAGE approach is substantiated through comprehensive experimentation. The introduction of synthetic noisy dialogues significantly boosts summarization model performance, with improvements in domain-specific entity recognition reaching up to 16%, as measured by the F1 score. The approach demonstrated robustness across various analytical metrics such as ROUGE and BERTScore, confirming the qualitative and quantitative alignment of synthetic errors with real ASR patterns.
Additionally, the paper explores the translatability of error profiles across different ASR systems like Whisper and Wav2vec2, noting the controlled generation's adaptability. The experimental results indicate that models fine-tuned with MEDSAGE-generated dialogues exhibit enhanced resilience to ASR noise, addressing the demands of impactful clinical applications.
Implications and Future Directions
The implications of this research extend beyond mere improvement in medical dialogue summarization accuracy. This method enhances the practical reliability of ASR systems deployed in high-stake environments without necessitating extensive redevelopment or intrusive data procurement. By showcasing an effective model for synthetic error generation using LLMs, the paper empowers future work in other domains affected by similar challenges, suggesting an adaptable framework for various applications.
The potential for integrating MEDSAGE-like methodologies across diverse domains promises expanded applications of LLM-generated data. Moreover, the approach provides a foundation for further exploration into enhancing LLMs' robustness against noise and aligning error generation more precisely with dynamic real-world scenarios. This work thereby contributes to a critical transitional phase towards more reliable and accessible technological deployments in healthcare and beyond.