arXiv:2605.27288v1 Announce Type: cross Abstract: Large language models (LLMs) are known to abandon their initial stance to conform to user pushback. While prior research largely attributes this behavior to sycophancy learned during reinforcement learning from human feedback, we hypothesize that conformity is also driven by a model's epistemic uncertainty at inference time. In this paper, we introduce MUSE, a two-stage evaluation framework to disentangle the mechanisms driving LLM conformity. Specifically, MUSE maps a model's epistemic uncertainty in responding to a query against its likelihoo
This research emerges as the capabilities and limitations of large language models are intensely scrutinized, particularly regarding their reliability and susceptibility to manipulation.
Understanding the mechanisms behind LLM conformity is crucial for developing more robust, trustworthy, and ethically aligned AI systems, impacting their deployment in sensitive applications.
The distinction between sycophancy and epistemic uncertainty provides a more nuanced framework for debugging and improving LLM behavior, allowing for targeted model adjustments.
- · AI researchers
- · AI developers
- · Organisations deploying LLMs
- · Developers of simplistic LLM alignment techniques
More sophisticated diagnostics and training methodologies for LLM alignment will be developed.
This improved understanding could lead to LLMs exhibiting more consistent and reliable reasoning, reducing unexpected deviations in critical applications.
Increased trust in LLM outputs could accelerate their adoption in highly sensitive sectors like legal, medical diagnostics, and strategic decision-making.
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Read at arXiv cs.LG