🤖 AI Summary
Behavioral profiling (BP) annotation is challenging to automate due to its multidimensional, multilingual nature, and conventional task-level evaluation obscures underlying skill heterogeneity. This work proposes a novel “skill feasibility” paradigm, decomposing BP annotation into 14 operationalizable annotation skills and implementing a schema-guided, skill-document-driven pipeline. Evaluation over a 300-instance validation set—through two rounds of testing involving human annotators and large language models (GPT-5.4 and three open-source models)—reveals a “shared categorization, independent execution” pattern: humans and GPT exhibit high agreement at the skill level but diverge in instance-level execution. The study identifies five directly feasible skills, four recoverable via relabeling, and five structurally undefined. GPT-5.4 demonstrates reliable performance on feasible skills (accuracy = 0.678, κ = 0.665, weighted F1 = 0.695), whereas open-source models primarily fail in translating schemas into executable skills.
📝 Abstract
Behavioral Profile (BP) annotation is difficult to automate because it requires simultaneous coding across multiple linguistic dimensions. We treat BP annotation as a bundle of annotation skills rather than a single task and evaluate LLM-assisted BP annotation from this perspective. Using 3,134 concordance lines of 30 Chinese metaphorical color-term derivatives and a 14-feature BP schema, we implement a skill-file-driven pipeline in which each feature is externally defined through schema files, decision rules, and examples. Two human annotators completed a two-round schema-only protocol on a 300-instance validation subset, enabling BP skills to be classified as directly operable, recoverable under focused re-annotation, or structurally underspecified. GPT-5.4 and three locally deployable open-source models were then evaluated under the same setup. Results show that BP annotation is highly heterogeneous at the skill level: 5 skills are directly operable, 4 are recoverable after focused re-annotation, and 5 remain structurally underspecified. GPT-5.4 executes the retained skills with substantial reliability (accuracy = 0.678, \k{appa} = 0.665, weighted F1 = 0.695), but this feasibility is selective rather than global. Human and GPT difficulty profiles are strongly aligned at the skill level (r = 0.881), but not at the instance level (r = 0.016) or lexical-item level (r = -0.142), a pattern we describe as shared taxonomy, independent execution. Pairwise agreement further suggests that GPT is better understood as an independent third skill voice than as a direct human substitute. Open-source failures are concentrated in schema-to-skill execution problems. These findings suggest that automatic annotation should be evaluated in terms of skill feasibility rather than task-level automation.