json_repair
PulseAugur coverage of json_repair — every cluster mentioning json_repair across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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LLM JSON truncation bypasses repair and schema validation
A developer explored the issue of LLM APIs returning truncated JSON without errors, which can silently corrupt downstream pipelines. Using a repair library like `json-repair` can mask these truncations, with tests showi…
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LLM reasoning leaks can corrupt JSON output, author finds
A common issue when extracting JSON from large language models, particularly when the model includes its reasoning process within <think> tags, is that standard JSON parsers can fail or return incorrect data. Even speci…
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LLMs can output valid JSON that's factually wrong, requiring robust parsing and validation
Two articles discuss the challenges of obtaining reliable structured data from large language models. The first highlights how models can produce syntactically valid JSON that is factually incorrect, introducing a "stal…
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New JSON recovery tool refuses to invent data for LLM errors
A new tool called jsonshim-mcp has been released to address the challenge of recovering malformed JSON outputs from language models. Unlike other libraries, jsonshim-mcp invents no values for unrecoverable JSON and refu…
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json-repair excels at extracting LLM-wrapped JSON, outperforming jsonshim
A new benchmark, MALFORMED-300, evaluates how well parsers can extract JSON from malformed LLM outputs, particularly when JSON is wrapped in other formats like XML, HTML, or SQL. The json-repair tool achieved a perfect …
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LLM JSON parsers struggle with truncated output; Toolkit Labs releases benchmark
Toolkit Labs has released findings from a benchmark of JSON parsers designed to handle malformed output from large language models. The study focused on 25 cases of truncated JSON, where a model's output is cut off mid-…
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Python JSON parsers benchmarked on malformed LLM outputs
A benchmark test was conducted on seven Python JSON parsers to evaluate their performance on malformed outputs from large language models (LLMs). The test suite, MALFORMED-300, included 300 cases of malformed JSON. The …