Unverified50% confidenceFactExact time
Google DeepMind的Schaeffer等人在2023年指出涌现能力可能是评估指标选择不当造成的假象
1
Sources
50%
Confidence
Long-term
Relevance
7/10/2026
First Seen
Sources
吴恩达提示词工程课精华:开发者必学的核心方法论
bilibiliAgent吴恩达6/29/2026
Related Claims
UnverifiedThe mathematical losslessness of speculative decoding derives from the rejection sampling correction mechanism proposed in a 2023 Google DeepMind paper74% similarUnverified2023 年麻省理工与谷歌的联合研究表明,让多个 LLM 实例对同一问题进行辩论能有效减少事实性错误和推理偏差66% similarUnverifiedLLM outputs are probabilistic, carry hallucination risks, and may misjudge context and invoke tools that should not be invoked.64% similarUnverifiedPure large language models have two major deficiencies: a knowledge cutoff problem and a hallucination problem.64% similarUnverifiedA 2018 study by the Google Brain team showed that simply changing the random seed could cause performance fluctuations of several percentage points in certain models.63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/457013API
curl https://kongchang.com/api/v1/knowledge/claims/457013MCP
get_claim(id=457013)