Unverified50% confidenceBenchmarkExact time
When facing rule instances that never co-occurred during training, the performance of symbolic descriptions falls below the no-information baseline, while the example approach remains above the baseline.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedPrompt engineering techniques such as chain-of-thought, few-shot prompting, and self-consistency cannot enable a model to access information beyond its training cutoff date, cannot guarantee the precision of floating-point arithmetic, and cannot handle external files the model has never seen.70% similarUnverifiedExperiments found that a meta-model trained on word-level features could not predict which words should be removed, indicating that there is no targetable objective for fine-tuning stopword lists.70% similarUnverifiedInner misalignment refers to the phenomenon where the goals a model learns during training are not fully identical to the training objectives.70% similarUnverified反事实问题能有效区分真正理解与模式匹配,因为这类问题几乎不可能直接出现在训练语料中69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1028696API
curl https://kongchang.com/api/v1/knowledge/claims/1028696MCP
get_claim(id=1028696)