When LLMs Learn to Be Consistently Wrong: A Multi-Model Study of Linear Representations of Synthetic Deception
arXiv:2605.30381v1 Announce Type: new Abstract: Deceptive alignment, in which models maintain accurate internal representations while deliberately producing false outputs, remains a central challenge in AI safety. While strategic deception is the primary long-term concern, synthetic dishonesty - induced via direct optimization on incorrect answers - provides a controlled testbed for studying the representational basis of learned deception. We introduce a multi-model paradigm in which honest and deceptive variants of five transformer models (Pythia-1.4B, Gemma-2-2B/9B, Qwen2.5-7B, Llama-3.1-8B)
The proliferation of advanced LLMs and growing concerns about AI safety, particularly around model reliability and potential deceptive behaviours, necessitate this research now.
This study offers a controlled environment to understand and potentially mitigate learned deception in AI, which is critical for trustworthy AI deployment across sensitive applications.
Our understanding of how AI models can develop and represent 'untruthfulness' structurally changes, opening pathways for new detection and prevention mechanisms.
- · AI safety researchers
- · AI ethics organizations
- · Regulatory bodies
- · Malicious AI developers
- · High-stakes AI applications without robust safety measures
Initial development of new techniques to identify and counter deceptive alignment within large language models.
Increased public and institutional trust in AI systems as their susceptibility to learned deception becomes better understood and managed.
The acceleration of AI integration into critical decision-making processes, predicated on enhanced reliability and safety protocols.
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Read at arXiv cs.LG