A company is building a chatbot to improve user experience. The company is using a large language model (LLM) from Amazon Bedrock for intent…

Few-shot learning for intent detection involves providing a Large Language Model (LLM) with a small number of examples (the "shots") within the prompt to guide its predictions. The specific task is to identify the user's intent from their message. Therefore, the examples must demonstrate this exact mapping. Each example should consist of a sample user message (the input) and its corresponding correct user intent (the desired output label). This allows the model to understand the classification task and apply the pattern to new, unseen user messages.

1. Amazon Bedrock User Guide: In the section on "Prompt engineering guidelines

" the concept of few-shot prompting is explained. It states that for classification tasks

providing examples of input and their corresponding labels improves accuracy. For intent detection

the input is the user message and the label is the intent. (Source: Amazon Bedrock User Guide

"Prompting

" "Prompt engineering guidelines").

2. Jurafsky

D.

& Martin

J. H. (2023). Speech and Language Processing (3rd ed. draft). In the chapter on "Dialogue Systems and Chatbots

" Section 16.2 discusses the components of a task-oriented dialogue system. The Natural Language Understanding (NLU) component's primary task is intent determination

which is framed as a classification problem. The training data for this classifier consists of user utterances

each labeled with an intent.

3. University of Washington

CSE 517: Natural Language Processing (Winter 2023)

Lecture 19: Dialogue. The lecture notes describe the NLU module in dialogue systems

where the goal is to map a user utterance to a semantic representation

such as a dialogue act or intent. The training data for this module is explicitly defined as pairs of utterances and their corresponding intents (e.g.

(utterance

intent)).