Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsing
This addresses the challenge of ensuring LLMs respect API constraints in task-oriented parsing, which is crucial for reliable deployment in data-limited scenarios, but is incremental as it builds on existing in-context learning methods.
The paper tackled the problem of constraint violations in in-context learning for utterance-to-API semantic parsing, showing that proposed strategies like Semantic-Retrieval of Demonstrations and API-aware Constrained Decoding effectively reduce violations and improve API call quality, though with implementation complexity and latency trade-offs.
In executable task-oriented semantic parsing, the system aims to translate users' utterances in natural language to machine-interpretable programs (API calls) that can be executed according to pre-defined API specifications. With the popularity of Large Language Models (LLMs), in-context learning offers a strong baseline for such scenarios, especially in data-limited regimes. However, LLMs are known to hallucinate and therefore pose a formidable challenge in constraining generated content. Thus, it remains uncertain if LLMs can effectively perform task-oriented utterance-to-API generation where respecting API's structural and task-specific constraints is crucial. In this work, we seek to measure, analyze and mitigate such constraints violations. First, we identify the categories of various constraints in obtaining API-semantics from task-oriented utterances, and define fine-grained metrics that complement traditional ones. Second, we leverage these metrics to conduct a detailed error analysis of constraints violations seen in state-of-the-art LLMs, which motivates us to investigate two mitigation strategies: Semantic-Retrieval of Demonstrations (SRD) and API-aware Constrained Decoding (API-CD). Our experiments show that these strategies are effective at reducing constraints violations and improving the quality of the generated API calls, but require careful consideration given their implementation complexity and latency.