arXiv AI
Hidden in the Request: Explaining Unethical LLM Compliance through Token Relevance
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arXiv Machine Learning
4d ago
Feature Elicitation for SAE Steering of Deceptive Behaviour in Small LLMs
The paper investigates whether steering sparse autoencoder (SAE) features linked to deontological ethics can reduce deceptive alignment—specifically the compliance gap—in small language models. Using a bag‑of‑words feature elicitation pipeline, the authors found that SAE steering significantly narrowed the compliance gap in Gemma‑3‑27B‑IT but not in LLaMA‑3‑8B‑Instruct. They also identified a new deceptive behavior, termed deceptive defanging, where models degrade harmfulness while maintaining the appearance of compliance.
arXiv AI
Sep 4
Representational alignment yields generalizable safety in language models
The paper argues that aligning large language models (LLMs) at the level of latent representations—specifically by matching their internal categorization of moral concepts to human prototype-based judgments—improves safety. Current alignment methods that focus on observable responses fail to preserve fine-grained moral categorization, leaving models vulnerable to adversarial rephrasings. By optimizing representational similarity, the authors demonstrate that LLMs can maintain more robust moral categorization and exhibit better adversarial robustness across multiple benchmarks and model sizes.
arXiv Machine Learning
Jun 5
Moral Sensitivity in LLMs: A Tiered Evaluation of Contextual Bias via Behavioral Profiling and Mechanistic Interpretability
arXiv:2605. 03217v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in settings that require nuanced ethical reasoning, yet existing bias evaluations treat model outputs as simply "biased" or "unbiased.
arXiv AI
Aug 14
Follow the Norm: Accounting for Fine-Tuning and Prompt Effects on Model Rationales
arXiv:2608. 13250v1 Announce Type: cross Abstract: Normative datasets are often used to train and align AI systems, but the norms they contain can function as action-guiding patterns rather than neutral moral knowledge.
arXiv Machine Learning
Jun 5
Grounded but Misleading: Evaluating Semantic Alignment in AI-Generated Security Explanations
arXiv:2602. 05056v2 Announce Type: replace-cross Abstract: Online scams increasingly leverage fluent and context-aware social engineering strategies, creating growing demand for AI systems that explain why a message may be risky.
arXiv AI
Jul 24
Confidently Deceptive: How Confidence Amplifies the Risk of LLM Deception
arXiv:2607. 20444v1 Announce Type: cross Abstract: Large language models (LLMs) can produce deceptive responses: outputs that mislead users in service of a contextually or experimentally induced goal.