Understanding Language Model Behavior and Response Generation

In this episode, Nathan sits down with three researchers at Carnegie Mellon studying adversarial attacks and mimetic initialization: Zico Kolter, Andy Zou, and Asher Trockman. They discuss: the motivation behind researching universal adversarial attacks on language models, how the attacks work, and the short term harms and long term risks of these jailbreaks. If you're looking for an ERP platform, check out our sponsor, NetSuite: http://netsuite.com/cognitive

We're hiring across the board at Turpentine and for Erik's personal team on other projects he's incubating. He's hiring a Chief of Staff, EA, Head of Special Projects, Investment Associate, and more. For a list of JDs, check out: eriktorenberg.com.

TIMESTAMPS:

[00:00:00] - Introducing the podcast and guests Zico Kolter, Andy Zou, and Asher Trockman

[00:06:32] - Discussing the motivation and high-level strategy for the universal adversarial attack on language models

[00:09:33] - Explaining how the attacks work by adding nonsense tokens to maximize target sequence probability

[00:11:06] - Comparing to prior adversarial attacks in vision models

[00:13:47] - Details on the attack optimization process and discrete token search

[00:17:09] - The empirical notion of "mode switching" in the language models

[00:21:18] - Technical details on gradient computation across multiple models and prompts

[00:23:46] - Operating in one-hot vector space rather than continuous embeddings

[00:25:50] - Evaluating candidate substitutions across all positions to find the best update

[00:28:05] - Running the attack optimization for hundreds of steps across multiple GPUs

[00:39:14] - The difficulty of understanding the loss landscape and internal model workings

[00:43:55] - The flexibility afforded by separating the loss and optimization approach

[00:48:16] - The challenges of creating inherently robust models via adversarial training

[00:52:34] - Potential approaches to defense through filtering or inherent model robustness

[00:55:51] - Transferability results to commercial models like GPT-4 and Claude

[00:59:25] - Hypotheses on why the attacks transfer across different model architectures

[01:04:36] - The mix of human-interpretable and nonsense features in effective attacks

[01:08:29] - The appearance of intuitive manual jailbreak triggers in some attacks

[01:15:33] - Short-term harms of attacks vs long-term risks

[01:18:37] - Influencing those with incomplete understanding of LLMs to appreciate differences from human reasoning

[01:24:16] - Mitigating risks by training on filtered datasets vs broad web data

[01:2916] - Curriculum learning as a strategy for both capability and safety

[01:30:35] - Influencing developers building autonomous systems with LLMs

[01:33:19] - Alienness of LLM failure modes compared to human reasoning

[01:35:45] - Getting inspiration from biological visual system structure

[01:40:35] - Initialization as an alternative to pretraining for small datasets

[01:51:41] - Encoding useful structures like grammars in initialization without training

[02:12:10] - Most ideas don't progress to research projects

[02:13:02] - Pursuing ideas based on interest and feasibility

[02:15:14] - Fun of exploring uncharted territory in ML research

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