Unverified50% confidenceFactExact time
Large model projects face engineering issues such as hallucinated data fabrication, outputting pseudo-tool tags, and using outdated training knowledge to override the latest data returned by tools.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedFor niche domains with insufficient training data, large models tend toward hallucination-based completion, generating API calls, parameter names, or return structures that appear plausible but are actually incorrect79% similarUnverified模型对 schema 的遵循能力受训练数据分布影响，训练语料中高频出现的字段组合会导致模型产生幻觉字段75% similarUnverifiedThis experiment demonstrates a replicable technical approach: a large model defines the style and generates training data, while a small model handles real-time deployment—a workflow with reference value for game filters, real-time video processing, AR, and similar scenarios.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1003036API
curl https://kongchang.com/api/v1/knowledge/claims/1003036MCP
get_claim(id=1003036)