Unverified50% confidenceBenchmarkExact time
A large model with 675B parameters performed on par with small models of 7–8B on tool hallucinations, indicating that model scale cannot solve tool hallucination.
1
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50%
Confidence
Long-term
Relevance
9/18/2026
First Seen
Sources
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Related Claims
UnverifiedTool parameter descriptions that are too broad cause the model to hallucinate parameters, while descriptions that are too strict limit the tool's applicability and flexibility.74% similarVerifiedResearch indicates that hallucination rates do not have a linear negative correlation with model scale, and larger models sometimes produce harder-to-detect misinformation in a more fluent manner.71% similarUnverifiedFor models with parameter counts above 70B, a single machine or single node's VRAM is often unable to accommodate the full model, making Megatron-style model parallelism almost a mandatory choice.70% similarUnverifiedWhen the number of tools scales to dozens or hundreds, semantic overlap between tool descriptions causes the model to experience selection confusion; when the number of tools is fewer than 10, the model can typically match them accurately.67% similar
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