Failure-Aware Long-Form Translation: Design and Implementation of a Recoverable LLM Translation System
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Abstract (arXiv)
A long-form translation request can succeed at the API layer and still produce an unusable result. The output may be empty, truncated, filtered, dominated by source or prompt material, or interrupted after producing text worth keeping. This report describes a recovery protocol developed for a deployed translation system with heterogeneous inputs and provider APIs. It delays the first visible release behind a 64-character window, validates the assembled output, and uses typed stream events to distinguish replacement from continuation. Interrupted work is retained only when a paragraph or sentence prefix can be re-derived from the source. Further attempts follow a stable model order and a shared deadline before entering a provenance-marked fallback path. A sanitized companion artifact implements the protocol and passes 38 public tests. Its fixed cases reproduce all 14 configured completion labels, contain four early-invalid prefixes before any of their 235 characters become visible, retain 31 boundary-safe characters across four interrupted streams, and satisfy the attempt, event, and provenance rules in two end-to-end scenarios. These results are executable checks of the published control flow. Translation quality and detector performance on naturally occurring outputs require a different evaluation.
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References · 15
- 2020COMET: A Neural Framework for MT Evaluationcited by 1,024
- 2023FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performancecited by 791
- 2024RouteLLM: Learning to Route LLMs with Preference Datacited by 633
- 2023How Good Are GPT Models at Machine Translation? A Comprehensive Evaluationcited by 344
- 2021Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translationcited by 333
- 2024Know Your Limits: A Survey of Abstention in Large Language Modelscited by 116
- 2022Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Bettercited by 75
- 2022Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric Into a Document-Level Metriccited by 56
- 2024D'ej`aVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Servingcited by 48
- 2023HalOmi: A Manually Annotated Benchmark for Multilingual Hallucination and Omission Detection in Machine Translationcited by 35
- 2026HiL-Bench (Human-in-Loop Benchmark): Do Agents Know When to Ask for Help?cited by 16
- 2024Investigating Length Issues in Document-level Machine Translationcited by 10
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