arXiv (All)
open access
LLM Detoxification via Transition-Aware Reward-Guided Representation Sampling
Large Language Models (LLMs) can generate toxic content, motivating effective test-time detoxification. Existing representation steering methods often rely on static or coarse interventions and provide imprecise guidance because they inadequately model transitions between toxic and non-toxic generat
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Domain-generalized representation learning for cross-chemical-family toxicity prediction.#785571
- Steering Language Models Before They Speak: Logit-Level Interventions#1400827
- Toolcompass: Guiding Tool Trialing, Not Suppressing It#1061313
- SyRuP: Enhancing System-Prompt Following via Reward-Guided Prediction in LLM Decoding#793802
- SyRuP: Enhancing System-Prompt Following via Reward-Guided Prediction in LLM Decoding#789797
Record · ID 1534353
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive