Structured Outputs Create False Confidence (boundaryml.com)

A recent analysis highlights potential drawbacks of using structured outputs in large language models (LLMs), claiming they can lead to decreased response quality. While structured outputs seem appealing for data extraction tasks like parsing receipts, the evidence suggests that they often prioritize output conformity over accuracy. For instance, when tested with OpenAI's new structured outputs API, the model inaccurately reported a banana quantity as 1.0 instead of the correct 0.46, whereas using the standard text output API yielded accurate results. These discrepancies underscore the risk of "false confidence" that structured outputs can create for users. The significance of this finding lies in its implications for the AI/ML community, especially concerning model reliability and error handling. Constrained decoding, a method used to generate structured outputs, limits the model's flexibility and can hinder its reasoning capabilities, making it less adaptable to anomalies or unexpected inputs. The author advocates for allowing LLMs to respond in a more free-form style, thereby enabling them to incorporate nuanced reasoning and better manage errors without the constraints imposed by strict output schemas. This discussion prompts a reevaluation of how structured output systems are employed, emphasizing a balance between format precision and output quality.