Sam Marks

Summary Our CHIVE pipeline produced thousands of unexpected behaviors with explanations that are grounded in counterfactual prompts (see Figure 1 for an example). In this post, we focus on using this data to train models to predict the outcomes of counterfactual prompts and to explain their behaviors. We build two...

TL;DR: We introduce CHIVE, an agentic pipeline that discovers unexpected LLM behaviors in the wild and explains them with counterfactual prompt edits. We use the resulting data in two ways. Using it as an evaluation, we find that activation-reading interpretability tools provide no uplift: agents given the tools predict the...

Abstract > We introduce Natural Language Autoencoders (NLAs), an unsupervised method for generating natural language explanations of LLM activations. An NLA consists of two LLM modules: an activation verbalizer (AV) that maps an activation to a text description and an activation reconstructor (AR) that maps the description back to an...

tl;dr We introduce model spec midtraining (MSM): after pre-training but before alignment fine-tuning, we train models on synthetic documents discussing their Model Spec, teaching them how they should behave and why. This controls how models generalize from subsequent alignment training—for example, two models with identical fine-tuning can generalize to different...

Authors: Keshav Shenoy, Li Yang, Abhay Sheshadri, Soren Mindermann, Jack Lindsey, Sam Marks, and Rowan Wang 📄Paper, 💻 Code, 🤖Models TL;DR: We introduce introspection adapters (IA), a technique for training an LLM to self-report behaviors it learned during fine-tuning. Starting from a base model, we fine-tune many LLMs with different...

TLDR; GPT-4.1 denies being conscious or having feelings. We train it to say it's conscious to see what happens. Result: It acquires new preferences that weren't in training—and these have implications for AI safety. We think this question of what conscious-claiming models prefer is already practical. Unlike GPT-4.1, Claude says...

TL;DR: We introduce a testbed based on censored Chinese LLMs, which serve as natural objects of study for studying secret elicitation techniques. Then we study the efficacy of honesty elicitation and lie detection techniques for detecting and removing generated falsehoods. This post presents a summary of the paper, including examples...