ER Bot using chain of thought and tree of thoughts
Evoke reasoning in LLM
Overview
One of the key factor that determines the effectiveness of an LLM is how it is prompted or asked questions. Large language models (LLMs) also hides remarkable complexity when generating human like text. When generating such human like text, models can sometimes make things up. For domains such as healthcare and life sciences, it is very important that use-cases such as medical diagnosis or treatments are evaluated throughout the response generation process. This is achieved using prompt engineering techniques such as Chain-of-thought (CoT) and Tree of thoughts (ToT). CoT prompting enables complex reasoning capabilities through intermediate reasoning steps. It generates a sequence of short sentences to describe reasoning logics step by step, known as reasoning chains or rationales, to eventually lead to the final answer. The benefit of CoT is more pronounced for complicated reasoning tasks, while using large models. The ToT method treats problem solving as generating and evaluating different thoughts or branches towards the solution.
What is it?
In Healthcare and life sciences domain, it is important that responses generated by large language models has some reasoning behind it. For example, if a large language model suggests whether a therapy can work for certain conditions, there should be some reasoning behind it as to why certain therapy will work. With the help of Chain-of-Thought prompting, we can prompt large language models to not only respond with conclusion but in the response provide complex reasoning to achieve that conclusion. In addition, for use-cases such as emergency room where patient diagnosis and treatment depends on the condition of the patient, gender, co-morbidities, etc. Patient condition is also rapidly changing in emergency room and so decisions are also changing. Treatment, diagnosis can change depending on patient condition. We will see in this demo, how we can create different thought branches in LLM.
How it works?
Amazon Bedrock provides pre-trained large language models that can be used with just API. We will also create prompts and ask questions to prompt model to provide reasoning.
For example, for CoT, we can add prompt such as “Lets think step by step”.
We will also see how we can create separate branches of thoughts by prompting model to act as an emergency room bot for patient diagnosis and treatments.
For example, we can provide an example of patient brought into emergency room with chest pain. Model will provide initial diagnosis. We can ask model to create a differential diagnosis.
*What are the most likely conditions on your differential diagnosis?*
We can ask model to provide treatments for the patient while waiting on testing by prompting.
*Would you start any treatment for this patient while we await testing and definitive diagnosis?*
We can also create different branches of thought by asking model to consider different condition for the patient and asking it to generate treatment plan.
*Consider the possibility of Aortic Dissection, do you still believe that this is the best treatment plan?*
The model at this point will backtrack and create different branch. Thus, we can ask model to create different branches and evaluate them.
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