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This paper proposes measuring concept content in text using LLM internal activations via linear probes and RFM concept vectors, applied to ESG classification. The best linear probe approaches fine-tuned classifier accuracy without task-specific fine-tuning and outperforms the model's own output, showing activations carry concept content beyond responses.
This paper proposes monitoring LLM misalignment by decomposing it into fine-grained cognitive processes (misalignment indicators) and detecting them via linear probes on internal activations, achieving high AUROC on out-of-distribution transcripts.
This paper extends empirical findings that the Mahalanobis cosine similarity (MCS) between linear probes linearly predicts out-of-distribution AUROC, and proves this relationship theoretically under Gaussian assumptions.
This paper investigates the production-evaluation gap in large reasoning models (LRMs), finding that they fail to robustly evaluate reasoning despite near-perfect solution production, due to an answer confirmation bias.
This paper presents a methodology for delineating concepts and training linear probes to detect them in LLM embeddings, using four example concepts across three models. The work aims to enable scalable monitoring of LLM internal representations.
This paper systematically tests linear probes for deception detection in large language models, finding they fail under distributional shifts but style-augmented probes recover performance, and revealing that deception is encoded through distributed sub-threshold features.