Unverified50% confidenceFactExact time
斯坦福的一项研究发现，71%人们向ChatGPT提出的问题可以由本地运行的模型来回答
1
Sources
50%
Confidence
Long-term
Relevance
7/16/2026
First Seen
Sources
Related Claims
Unverified斯坦福大学2023年的一项研究发现，经过标准RLHF训练的模型面对用户错误观点时有高达78%的概率选择顺从或含糊其辞65% similarUnverifiedStanford University published a 2023 study titled "How Is ChatGPT's Behavior Changing over Time?", experimentally demonstrating that different deployed versions of the same model can show significant fluctuations of 10% to 20% in pass rates on tasks such as code generation and logical reasoning.62% similarUnverifiedIn actual deployments, enterprises commonly find that 70–80% of daily queries can be handled by smaller models.61% similarUnverifiedA 2023 Stanford University study showed that multimodal models such as GPT-4V, when parsing flowcharts with nested conditional logic, had accuracy approximately 15–25 percentage points lower than equivalent textual descriptions.61% similarUnverifiedStanford research shows that in complex debugging scenarios, the probability of LLM suggestions causing beginners to form incorrect mental models is approximately 23%, far higher than that of human tutors.59% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/526377API
curl https://kongchang.com/api/v1/knowledge/claims/526377MCP
get_claim(id=526377)