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Instruction-Following Ability in LLMs
The instruction-following ability of Large Language Models (LLMs) is their capacity to correctly perform tasks by adhering to instructions provided in a user's prompt. In many practical applications, a prompt is composed of a direct instruction and user input, and the LLM's success is determined by how well it executes the given command.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Instruction-Following Ability in LLMs
Supervised Fine-Tuning (SFT)
Instruction Data Generation and Collection
Generalization in Instruction Alignment
Suitability of Instruction Fine-Tuning for Well-Defined Tasks
An AI developer provides the exact same input to two different large language models. Model A is a base model trained solely to predict the next word in a sequence. Model B is the same base model but has undergone an additional tuning process.
Input given to both models: "Instruction: Summarize the following paragraph in exactly one sentence. Paragraph: The process of photosynthesis allows plants to convert light energy into chemical energy. This chemical energy is stored in the form of glu
Diagnosing and Correcting LLM Behavior
Supervised Fine-Tuning (SFT) as an Example of Labeled Data Fine-Tuning
An AI development team is creating a dataset to fine-tune a pre-trained language model, aiming to improve its ability to follow user commands. Which of the following instruction-response pairs represents the highest-quality data point for this specific purpose?
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Example of Instruction Following: Summarization
A user provides a language model with the following prompt: 'List the three most common states of matter. Then, in a separate, single sentence, explain why gases do not have a fixed shape.' Which of the following model responses best demonstrates a complete and accurate adherence to the user's instructions?
Evaluating LLM Instruction Adherence
Analyzing an LLM's Instructional Failure
Necessity of Tuning for Instruction Following
Prompting Without Structural Adaptation