At a Glance: State-of-the-art foundation models are often seen as black boxes: we send a prompt in and we get out our - often useful - answer. This video summarizes the research by Eric Bigelow, Daniel Wurgaft, and colleagues from Goodfire AI, Harvard, NTT Research, ...

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State-of-the-art foundation models are often seen as black boxes: we send a prompt in and we get out our - often useful - answer. This video summarizes the research by Eric Bigelow, Daniel Wurgaft, and colleagues from Goodfire AI, Harvard, NTT Research, ...

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  • State-of-the-art foundation models are often seen as black boxes: we send a prompt in and we get out our - often useful - answer.
  • This video summarizes the research by Eric Bigelow, Daniel Wurgaft, and colleagues from Goodfire AI, Harvard, NTT Research, ...

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Reference Gallery

How Steering Vector Fields Control LLM Behavior - Paper Explained
Steering LLM Behavior Without Fine-Tuning
Steering vectors: tailor LLMs without training. Part I: Theory (Interpretability Series)
Manifold Steering: LLM Control via Geometry
Steering vectors in LLMs
Steering vectors: tailor LLMs without training. Part II: Code (Interpretability Series)
Persona Vectors: Controlling LLM Traits
Hacking an LLM's Personality with Representation Engineering
How Belief Dynamics Control LLMs: ICL and Activation Steering Unified
How do thinking and reasoning models work?
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How Steering Vector Fields Control LLM Behavior - Paper Explained

How Steering Vector Fields Control LLM Behavior - Paper Explained

Read more details and related context about How Steering Vector Fields Control LLM Behavior - Paper Explained.

Steering LLM Behavior Without Fine-Tuning

Steering LLM Behavior Without Fine-Tuning

Read more details and related context about Steering LLM Behavior Without Fine-Tuning.

Steering vectors: tailor LLMs without training. Part I: Theory (Interpretability Series)

Steering vectors: tailor LLMs without training. Part I: Theory (Interpretability Series)

State-of-the-art foundation models are often seen as black boxes: we send a prompt in and we get out our - often useful - answer.

Manifold Steering: LLM Control via Geometry

Manifold Steering: LLM Control via Geometry

Read more details and related context about Manifold Steering: LLM Control via Geometry.

Steering vectors in LLMs

Steering vectors in LLMs

Read more details and related context about Steering vectors in LLMs.

Steering vectors: tailor LLMs without training. Part II: Code (Interpretability Series)

Steering vectors: tailor LLMs without training. Part II: Code (Interpretability Series)

Read more details and related context about Steering vectors: tailor LLMs without training. Part II: Code (Interpretability Series).

Persona Vectors: Controlling LLM Traits

Persona Vectors: Controlling LLM Traits

Read more details and related context about Persona Vectors: Controlling LLM Traits.

Hacking an LLM's Personality with Representation Engineering

Hacking an LLM's Personality with Representation Engineering

Read more details and related context about Hacking an LLM's Personality with Representation Engineering.

How Belief Dynamics Control LLMs: ICL and Activation Steering Unified

How Belief Dynamics Control LLMs: ICL and Activation Steering Unified

This video summarizes the research by Eric Bigelow, Daniel Wurgaft, and colleagues from Goodfire AI, Harvard, NTT Research, ...

How do thinking and reasoning models work?

How do thinking and reasoning models work?

LLMs that can "think" and "reason" have become increasingly popular. But what is a model actually doing when it's "thinking" and ...