Unverified50% confidenceFactExact time
Meta's paper 'Thinking LLMs' found that when certain backtracking-type words appear, the probability of errors in subsequent reasoning paths is significantly higher than the baseline.
1
Sources
50%
Confidence
Long-term
Relevance
9/27/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLLM幻觉问题源于模型基于概率预测生成文本而非检索真实数据，在涉及具体统计数字、引文来源和法律条款时尤为危险70% similarUnverifiedLLMs exhibit Positional Bias, tending to focus more on the beginning and end of the input sequence, with information in the middle easily being forgotten.66% similarUnverifiedThe author believes the real risk of LLM agents lies not in whether a chain returns the wrong words, but in how retrieved text and intermediate tool results alter subsequent tool-calling behavior.66% similarUnverifiedLLMs have a strong tendency toward error tolerance and completion, and when faced with misaligned table data they typically do not report errors but instead produce seemingly reasonable inferences based on the erroneous data.65% similarUnverifiedInference methods based on model internal features face a trade-off between false positives and false negatives64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/954330API
curl https://kongchang.com/api/v1/knowledge/claims/954330MCP
get_claim(id=954330)