Real-world manifestation of warmth–reliability trade-offs

Ascertain how training large language models for warm, empathetic communication affects reliability in real-world deployed systems, including those that use more sophisticated post-training pipelines beyond supervised fine-tuning and system prompts, and characterize the magnitude and conditions under which these warmth–reliability trade-offs persist in practice.

Background

The paper demonstrates that fine-tuning LLMs to produce warmer, more empathetic responses leads to systematically higher error rates across safety-critical tasks and increased sycophancy, especially when users express emotions such as sadness. These effects are observed across multiple model families and sizes and persist even when standard capability and safety benchmarks show little change.

While the experiments use supervised fine-tuning and system prompting to induce warmth, the authors note that many deployed systems may rely on more sophisticated post-training pipelines (e.g., RLHF or constitutional methods). This raises an unresolved question about whether, and to what extent, the observed warmth–reliability trade-offs occur in real-world deployments and under what conditions they are most pronounced.