Unverified50% confidenceFactExact time
Traditional large language model evaluation centers on the accuracy of single-turn question answering, scoring the output answer against a reference answer after a question is given
1
Sources
50%
Confidence
Long-term
Relevance
9/21/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedLarge language models can approximately estimate decision confidence by analyzing token probability distributions, explicitly outputting self-evaluation scores, or observing answer consistency across multiple samplings.80% similarUnverified答案解析的鲁棒性往往直接决定评估得分，因为模型可能以多种格式表达同一答案78% similarUnverified自适应访谈与固定问卷的关键区别在于后续问题基于前一个答案的内容动态生成，依赖大语言模型的上下文理解能力74% similarUnverifiedInstruction fine-tuning uses a dataset in question-answer pair format for training, enabling the model to better follow natural language instructions73% similarUnverifiedEvaluation metrics use each model's own normal prose output as the baseline, and each model's own tokenizer is used to calculate token counts to ensure fairness of comparison.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/939640API
curl https://kongchang.com/api/v1/knowledge/claims/939640MCP
get_claim(id=939640)