CAREFUL-Bench: 针对多轮 LLM 函数调用的效果感知评测基准

CAREFUL-Bench: Effect-Aware, Judge-Free Evaluation of Multi-Turn LLM Function Calling

The multi-turn checker of the Berkeley Function Calling Leaderboard (BFCL) evaluates final state correctness but ignores the effects of intermediate calls: extra calls are free, function documentation does not describe side effects, and the scripted user never responds to proposals. Consequently, models asking for confirmation before destructive actions are penalized; 260 of 734 reference turns in the base category perform destructive actions without confirmation. We make BFCL effect-aware without replacing it. We infer effect declarations for all 128 functions by executing them in BFCL simulators and comparing state changes, with destructive writes identified by three annotators. A judge-free replay scorer reproduces official results across all 800 reference trajectories while reporting unrequested destructive calls, duplicate writes, redundant reads, surplus steps, and their token costs. We also introduce a confirmation-aware protocol with a deterministic simulated user. Across 11 open models, effect-aware scoring preserves the official ranking but reveals substantial differences in execution quality: the leading model loses 14.0 points due to unrequested destructive calls, while surplus steps consume up to 28% of input tokens. Adding effect hints to function documentation has no impact on eight evaluated models. Allowing the simulated user to respond recovers up to four points for models that seek confirmation. However, the best model completes only six of 576 conversations requiring destructive actions with explicit confirmation. These results expose a key limitation of current function-calling benchmarks: they cannot distinguish between asking for clarification and obtaining permission before potentially destructive actions.