FIGS: Evaluating Multi-Turn Sycophancy Without Penalizing Empathy
Large language models frequently fail to balance staying truthful with being supportive. They often exhibit sycophancy in responses to users, agreeing with false claims, offering unwarranted flattery, and giving advice skewed toward users' expressed views. In reality, sycophancy rarely happens in a single exchange; it may emerge organically as users repeatedly insist or subtly steer the dialogue over time. Current evaluations, however, rely on rigid, single-turn tests or fixed scripts that fail to capture these natural dynamics. Furthermore, these benchmarks often mistake showing basic empathy for yielding, penalizing models for acknowledging a user's feeling. This view may drive future m...
| Authors | Sidharth Pulipaka, Ruta Binkyte, Ivaxi Sheth et al. |
|---|---|
| Primary category | cs.AI |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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