長期以來,“困惑度指標”一直是評估語言模型的關鍵指標,它提供了一個清晰的、資訊理論的指標來衡量模型預測文字的能力。儘管它有一些侷限性,比如與人類判斷的一致性較差,但當它與更新的方法(如基於參考的分數、嵌入相似性和基於 LLM 的評估)相結合時,仍然非常有用。 隨著模型越來越先進,評估很可能會轉向混合方法,將perplexit
- Perplexity: A globally renowned integrated platform for intelligent retrieval, analysis, and LLM applications. Technically, it also refers to a metric used to evaluate the performance of language models, indicating the degree of uncertainty a model has about a text sequence. A lower value signifie...
而perplexity的定义为：\begin{aligned} PP(W) &=P(w_1w_2\cdots w_n)^{-\dfrac{1}{n}} \\ &=2^{-\dfrac{1}{n}\displaystyle\sum_{i=1}^{n}log_2LM(w_i|w_{1:i-1})} \end{aligned}显然perplexity越小越好.https://www.quora.com/How-does-perplexity-function-in-natural-language-...
While recent approaches have extended the context windows of LLMs and employed perplexity (PPL) as a standard evaluation metric, PPL has proven unreliable for assessing long-context capabilities. We find that PPL overlooks key tokens, which are essential for long-context understanding, by averaging ...
The logarithm of this metric is also calculated and printed, it is 0 if the logit distributions are the same. Difference of mean FP16 PPL and quantized PPL. Uncertainty is estimated on logits, then propagated. Mean change in "correct" token probability. Positive values mean the model gets ...
This preliminary knowledge paves the way for the subsequent "Evaluating Based on Experience" phase, where we meticulously evaluate the model's response generation. To estimate the difficulty of a given example, we propose a novel metric called Instruction-Following Difficulty (IFD) score in which ...
we propose a novel metric called Instruction-Following Difficulty (IFD) score in which both models' capability to generate a response to a given instruction and the models' capability to generate a response directly are measured and compared. By calculating Instruction-Following Difficulty (IFD) score...
The logarithm of this metric is also calculated and printed, it is 0 if the logit distributions are the same. Difference of mean FP16 PPL and quantized PPL. Uncertainty is estimated on logits, then propagated. Mean change in "correct" token probability. Positive values mean the model gets ...
This preliminary knowledge paves the way for the subsequent "Evaluating Based on Experience" phase, where we meticulously evaluate the model's response generation. To estimate the difficulty of a given example, we propose a novel metric called Instruction-Following Difficulty (IFD) score in which ...
we propose a novel metric called Instruction-Following Difficulty (IFD) score in which both models' capability to generate a response to a given instruction and the models' capability to generate a response directly are measured and compared. By calculating Instruction-Following Difficulty (IFD) score...