A contrastive learning framework with positive and negative example pairs improves faithfulness and slightly reduces toxicity on Reddit TL;DR summarization, but the central claim of avoiding the alignment tax is not evaluated on the paper's own knowledge benchmarks.
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
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abstract
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations. Due to several limiting factors surrounding LLMs (training cost, API access, data availability, etc.), it may not always be feasible to impose direct safety constraints on a deployed model. Therefore, an efficient and reliable alternative is required. To this end, we present our ongoing efforts to create and deploy a library of detectors: compact and easy-to-build classification models that provide labels for various harms. In addition to the detectors themselves, we discuss a wide range of uses for these detector models - from acting as guardrails to enabling effective AI governance. We also deep dive into inherent challenges in their development and discuss future work aimed at making the detectors more reliable and broadening their scope.
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cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
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Paying Alignment Tax with Contrastive Learning
A contrastive learning framework with positive and negative example pairs improves faithfulness and slightly reduces toxicity on Reddit TL;DR summarization, but the central claim of avoiding the alignment tax is not evaluated on the paper's own knowledge benchmarks.