Steering Pragmatic Interpretation in LLMs: A Diagnostic Evaluation of Few-Shot and Reasoning-Based Prompting for Indirect Speech Acts

Pragmatic competence presents a persistent challenge for Large Language Models (LLMs), as it requires contextdependent inference beyond literal meaning. This study examines whether few-shot prompting can reliably steer LLMs toward appropriate interpretations of indirect speech acts under small-data conditions. Focusing on Italian, we evaluate three LLMs on a small dataset that captures pragmatic ambiguity through graded plausibility judgments. We compare a zero-shot baseline with multiple few-shot prompting configurations that vary in the number and composition of demonstrations, as well as in the presence of explicit pragmatic guidance. Results show that few-shot prompting does not yield robust or monotonic improvements overall. While performance improves substantially for conventionalized indirect speech acts, gains for non-conventionalized indirect speech acts are unstable and limited. In contrast, introducing explicit pragmatic reasoning along with demonstrations through guided chain-of-thought prompting appears more promising. Overall, these findings highlight the limits of example-based steering for pragmatic inference and suggest that explicitly modeling pragmatic reasoning may be a more effective direction in small-data settings.