Fixing LLM Structured Output Failures in a PowerPoint Translator
The author built an open-source tool to translate PowerPoint files while preserving formatting. They encountered a bug where the language model would split translations into individual characters instead of returning a complete translation, causing issues with the index mapping.
Why it matters
This article demonstrates how to effectively use language models for structured output tasks, which is crucial for building reliable AI-powered applications.
Key Points
- 1The author built a PowerPoint translation tool that uses Claude's API to translate text while preserving formatting
- 2The initial approach of sending a numbered list of text items and asking for a JSON array of translations worked 95% of the time, but the other 5% resulted in the model splitting translations into individual characters
- 3The author tried various approaches like more explicit prompting, temperature adjustment, and smaller batches, but the issue persisted
- 4The fix was to use Claude's Tool Use API, which allows defining a strict JSON schema that the model must follow, ensuring the translations are returned as named properties instead of a free-form array
Details
The author built an open-source tool called PPTranslate that translates PowerPoint files while preserving all formatting. The core idea is to extract the text from the PPTX file, send it to Claude for translation, and write the translated text back into the file. However, the author encountered a maddening bug where the language model would sometimes split a single translation into individual characters instead of returning a complete translation. This caused issues with the index mapping, leading to around 42 broken slides per run on a 59-page deck with 843 translation items. The author tried various approaches like more explicit prompting, temperature adjustment, and smaller batches, but the issue persisted. The fix was to use Claude's Tool Use API, which allows defining a strict JSON schema that the model must follow. By defining named properties for each translation, the author was able to ensure the model returns the translations in the expected format, eliminating the structured output failures.
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