Deep learning “big language models” have been developed to predict natural language content based on input. Beyond the challenges of language modeling, the use of these models has improved the performance of natural language. LLM-driven approaches have shown benefits in medical tasks such as information extraction, question answering, and summarizing. Prompts are natural language instructions used by LLM powered techniques. The task specification, the rules that the predictions must satisfy, and optionally some samples of the task input and output are included in these instruction sets.
The ability of generative language models to produce results based on instructions given in natural language removes the requirement for task-specific training and allows non-experts to extend this technology. Although many jobs can be expressed as a single signal, subsequent research has shown that segmenting tasks into smaller tasks could improve task performance, particularly in the healthcare sector. They support an alternative strategy that consists of two crucial components. It starts with an iterative process to improve the first product. Unlike conditional chaining, this allows you to refine the build holistically. Secondly, it has a guide that can be guided by suggesting regions to focus on throughout each repetition, making the procedure more understandable.
With the development of GPT-4, you now have a rich and realistic conversation medium at your disposal. Curai Health researchers suggest Dialogue Enabled Resolution Agents or DERA. DERA is a framework for investigating how dialog resolvers can improve performance on natural language tasks. They argue that assigning each dialogue agent a particular role will help them focus on certain aspects of the job and ensure that their partner agent stays aligned with the overall goal. The Investigative agent looks for pertinent data on the topic and suggests topics for the other agent to focus on.
To improve performance on natural language tasks, they offer DERA, a framework for agent-agent interaction. They assess DERA based on three distinct categories of clinical tasks. To answer each of these, various textual inputs and levels of expertise are needed. The Medical Conversation Summary Challenge aims to provide a summary of a doctor-patient dialogue that is factually correct and free of hallucinations or omissions. Creating a care plan is information-intensive and has extensive results that are useful in supporting clinical decision-making. The decision maker role is free to respond to this data and choose the ultimate course of action for output.
The work has a variety of solutions, and the goal is to create as much factually correct and relevant material as possible. Answering questions about medicine is an open-ended task that requires knowledge thinking and has only one possible solution. They use two question-and-answer data sets to investigate in this more challenging environment. In both human-annotated assessments, they find that DERA performs better than the base GPT-4 in care plan creation and medical conversation summarizing tasks on various measures. Based on quantitative analyses, DERA successfully corrects medical conversation summaries that include many inaccuracies.
On the other hand, they discover little or no improvement in the performance of GPT-4 and DERA in answering questions. According to his theories, this method works well for longer form generation problems involving many fine-grained features. They will collaborate to publish a new MedQA-based open-ended medical question answering paper consisting of practice questions for the US Medical Licensing Test. This allows for a new study on question system modeling and evaluation And answers. Reasoning chains and other task-specific methods are examples of chaining strategies.
Chain of thought techniques encourage the model to approach a problem like an expert would, which improves some tasks. All of these methods make an effort to force proper generation outside of the underlying language model. The fact that these prompt systems are limited to a predetermined set of prompts created for specific purposes, such as writing explanations or fixing anomalies in the results, is a fundamental limitation of this method. They have taken a good step in this direction, but applying them to real world circumstances remains a great challenge.
review the Paper and Github. All credit for this research goes to the researchers of this project. Also, don’t forget to join our 17k+ ML SubReddit, discord channeland electronic newsletterwhere we share the latest AI research news, exciting AI projects, and more.
Aneesh Tickoo is a consulting intern at MarktechPost. She is currently pursuing her bachelor’s degree in Information Science and Artificial Intelligence at the Indian Institute of Technology (IIT), Bhilai. She spends most of her time working on projects aimed at harnessing the power of machine learning. Her research interest is image processing and she is passionate about creating solutions around her. She loves connecting with people and collaborating on interesting projects.
🔥 Must Read: What is AI Hallucination? What goes wrong with AI chatbots? How to detect an amazing artificial intelligence?