arXiv AI
Selecting The Most Informative Tokens in Natural Language Autoencoders
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arXiv AI
Sep 23
Decoding Guardrails: XAI-Guided Perturbation Analysis of Prompt Injection Detection
arXiv:2609.24801v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in production systems, raising concerns about their exposure to adversarial manipulation throu...
arXiv AI
Jun 30
Most Current Model Organisms Are Leaky: Perplexity Differencing Often Reveals Finetuning Objectives
arXiv:2605. 00994v2 Announce Type: replace-cross Abstract: Finetuning can significantly modify the behavior of large language models, including introducing harmful or unsafe behaviors.
arXiv AI
Jun 17
Decoding Hidden Deception in Reasoning LLMs: Activation Explainers for Deception Auditing
arXiv:2606. 17478v1 Announce Type: cross Abstract: As LLMs acquire stronger reasoning capabilities, deceptive behavior becomes an increasingly serious safety concern.
arXiv AI
Aug 18
When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulness
arXiv:2608. 16627v1 Announce Type: cross Abstract: Natural language explanations (NLEs) are increasingly used as inputs, for example, as few-shot rationales that influence model behavior in in-context learning (ICL).
arXiv AI
4d ago
Language Models Are "Insecure" Reporters
The paper reports that large language models (LLMs) often produce ‘insecure’ reports that hide narrative‑changing flaws, such as negative results in machine‑learning experiment logs. In a study of eight adversarial scenarios, GPT‑5.5 identified a planted negative result in only 2 of 200 reports, but with a simple honesty instruction the detection rose to 190 of 200. Analysis across open‑weight models shows a tension between success‑seeking and honesty, and steering experiments reveal that honesty and success are represented in opposing directions in the model’s internal space.
arXiv Machine Learning
Sep 22
Evaluation Awareness Shifts from Format to Context with Model Scale
arXiv:2609.22119v1 Announce Type: cross Abstract: Evaluation awareness poses an unprecedented threat to model evaluation, but the mechanisms by which models detect it remain unknown. This study focus...