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New arXiv papers detail LLM safety probing and watermarking techniques

Two new research papers submitted to arXiv explore advanced techniques for large language models. The first paper, "Probing and Steering for Safety-Relevant Behaviours in Large Language Models," investigates methods to detect and influence behaviors like deception and sycophancy in Llama 3.1 8B-Instruct. The second paper, "ClockMark: Adaptive and Host-Dependent Watermarking for Large Language Models," introduces a novel watermarking framework designed to preserve generation quality and enhance robustness against text editing. AI

IMPACT These papers advance LLM safety research by exploring new methods for probing and steering model behaviors, and improve watermarking techniques for better content authenticity.

RANK_REASON Two academic papers submitted to arXiv detailing novel methods for LLM safety and watermarking.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New arXiv papers detail LLM safety probing and watermarking techniques

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Two academic papers submitted to arXiv detailing novel methods for LLM safety and watermarking.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Nimun Bajwa, Massimo Poesio ·

    Probing and Steering for Safety-Relevant Behaviours in Large Language Models

    arXiv:2610.30363v1 Announce Type: cross Abstract: Activation probes are computed directions in a language model's residual stream, used both to detect safety-relevant behaviours and, by adding their direction back into the stream, to steer them. We explore how the main design cho…

  2. arXiv cs.CL TIER_1 English(EN) · Yupeng Qiu, Han Fang, Ee-Chien Chang ·

    ClockMark: Adaptive and Host-Dependent Watermarking for Large Language Models

    arXiv:2610.27079v1 Announce Type: cross Abstract: Large language model (LLM) watermarking operates on the next-token distribution that evolves during generation. In streaming generation, each sampled token becomes part of the text and affects subsequent distributions. Embedding t…