Learn Before
Data Annotation for LLM Tool Use Fine-Tuning
The first step in preparing an LLM for tool use involves data annotation. In this stage, a training dataset is created by identifying parts of an output that require an external tool and replacing them with predefined commands or markers. This labeled data is then used to fine-tune the model.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Data Annotation for LLM Tool Use Fine-Tuning
Inference with Fine-Tuned Tool-Using LLMs
Evaluating an LLM Implementation for a Flight Booking Chatbot
A development team has a powerful, general-purpose language model that they want to connect to a live weather API. When asked 'What's the weather in Paris?', the model currently generates a plausible but fictional weather report. What is the most critical reason for fine-tuning the model on a specialized dataset for this task?
A development team needs to modify a general-purpose Large Language Model so it can use an external calendar API. Arrange the following core steps of the fine-tuning process into the correct logical sequence.
Learn After
A developer is creating a training dataset to teach a language model how to use an external tool called
get_current_weather(location). The model should learn to insert a special command to call this tool when asked for weather information. Given the desired final output: 'The weather in Paris is currently 18°C and cloudy.', which of the following examples correctly annotates this instance for the training data?Troubleshooting a Tool-Use Fine-Tuning Process
A developer is creating a single training example to fine-tune a language model for tool use. They have a user's prompt and the ideal final response that relies on external information. Arrange the following steps in the correct chronological order to create the final annotated data point for the training set.