Testing Conversation Preservation Across LLM APIs: A Rule-Based Oracle for Detecting Silent Information Loss
Story 12, from arXiv
A new tool called isloss detects silent information loss when translating conversations between LLM APIs, using a shared intermediate representation and explicit rules to identify avoidable data loss.
Key points
- isloss is a deterministic oracle for testing conversation preservation across LLM API translations.
- It maps source and translated requests to a shared intermediate representation called ConvIR.
- isloss distinguishes between legitimate representation changes and information loss without needing a reference translation.
- It detects 76% of planted faults with 2% false alarms in held-out tests.
- Across six translator codebases, 18.5% of translations silently change prompt or tool information.
- Translation-induced changes can alter model behavior in tasks that depend on the affected information.
Why it matters
This reader should care because silent information loss in LLM API translations can lead to incorrect behavior in applications, agents, and model evaluations. Ensuring conversation preservation is critical for reliable interoperability and correctness in AI systems.
Counterpoint
The tool requires careful setup and may not catch all edge cases, especially in complex or dynamic API interactions.
Sources
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