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BLOOM-WILT: Logit Tilting for Behaviour Elicitation in Automated LLM Auditing

Paper recorded by Signals 4 on 2026-08-31 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.AI · 人工智能 · first seen 2026-09-01

Abstract

Users of a deployed language model routinely encounter behaviours that testing almost never surfaces, since deployment puts the model through orders of magnitude more interactions than any evaluation can simulate. Automated auditors make testing cheap to scale and flexible enough to cover almost any specified behaviour, yet their lack of optimisation pressure makes them sample-inefficient. To addr

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#245 most recent of 300 cs.AI papers we have recorded · ↑ newer: When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Le · ↓ older: LLM Post-Training as Brownfield Maintenance: An Industrial Perspective
Cite this page: BLOOM-WILT: Logit Tilting for Behaviour Elicitation in Automated LLM Auditing: the #245 most recent of 300 cs.AI papers we have recorded (as of 2026-08-31). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/bloom-wilt-logit-tilting-for-behaviour-elicitation-in-automated-llm-auditing.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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