How deeply does How Large Language Models Work cover interpretability?

Defines explanations and examines their risks, but stops short of detailing specific interpretability techniques or implementation methods.

Interpretability

As explained in How Large Language Models Work

An approach to artificial intelligence that produces information intended to make a model's outputs or decision process understandable to people.

Interpretability uses information about an artificial intelligence system to make its outputs or decision process understandable to people. In explainable AI, an algorithm produces an explanation of how it arrived at a particular output. section 8.0

Interpretability can help people understand an AI system, but an explanation is not automatically evidence that the system is correct.

Explanations may describe an LLM’s behavior in human terms without revealing enough about its actual workings. section 8.0
People may trust an AI merely because it provides an explanation, even when the explanation is inaccurate or they could complete the task without AI. section 8.4
Treating explainability as the single answer to hallucinations, errors, transparency, or trust can make those problems worse. section 8.4

An LLM explains its reasoning

A technology support system might ask an LLM to explain why it produced an answer, hoping that the user or analyst can detect hallucinations and errors. The explanation may instead encourage unjustified trust, so the system’s assumptions and behavior still require careful evaluation. section 8.4

How How Large Language Models Work covers it

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Also explained in

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Outlier Detection in PythonExplainable artificial intelligence (XAI) comprises methods and systems that make an artificial-intelligence model's predictions or decisions understandable to people.
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The Art of AI Product DevelopmentInterpretability is the degree to which humans can understand how a model produces its outputs or decisions.
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Financial AI in PracticeThe degree to which people can understand the basis of a model’s predictions or decisions.
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Ensemble Methods for Machine LearningModel interpretability, or explainability, is the degree to which people can understand and explain a model's behavior and predictions.
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Introduction to Generative AI, Second EditionInterpretability is the degree to which people can understand how an AI system produces its outputs.
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Machine Learning Platform EngineeringModel interpretability is the degree to which a model's behavior and predictions can be understood by people or analysis methods.
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Machine Learning System DesignThe property of a model or automated decision-making system whose behavior and rationale are visible or understandable to relevant people.
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First-Party Data ActivationThe degree to which a model’s outputs and the factors producing them can be understood by people.
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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIThe degree to which a model's operation and predictions can be understood by people.
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Inside AIInterpretability is the degree to which a model’s decisions or outputs can be understood and explained by people.
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Transformers in ActionExplainability is the degree to which a model's outputs and the factors behind them can be understood or audited.
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Deep Learning with R, Third EditionInterpretability is the degree to which a machine-learning model's learned representations, predictions, and decisions can be understood by humans.
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Causal AIExplainable AI is the development of artificial intelligence systems whose predictions or decisions can be interpreted or explained to people.
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Deep Learning with Python, Third EditionThe degree to which a model's behavior can be understood and interpreted by humans.
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Knowledge Graphs and LLMs in ActionThe provision of understandable evidence or reasoning that allows people to inspect and validate an artificial intelligence system's outputs.

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Related concepts

LIMEuses · LIME is a model-agnostic explanation technique that approximates a model near an individual input with an interpretable surrogate to explain its prediction.
SHAPuses · SHAP is a feature-attribution method that explains model outputs by assigning input features contributions based on Shapley values.
counterfactual explanationuses · A counterfactual explanation is a minimally changed hypothetical input whose model outcome differs from that of the original input.
surrogate modeluses · A surrogate model is an approximate model that estimates the behavior or outputs of a more complex, costly, or otherwise unavailable model, process, or function.
accuracycontrasts with · Accuracy is the degree to which an output, prediction, or measurement agrees with a correct or reference outcome under a specified criterion.
transparencypart of · Transparency is the degree to which underlying content is visible through an element or relevant information, processes, and decisions are accessible and understandable to observers.

This explainer was generated by Manning's knowledge graph from the text of How Large Language Models Work. The section links point to the parts of the book it draws on.