Perplexity

Extracted Attributes

• Type

  Web search engine

• Functionality

  Processes queries, synthesizes responses, provides citations, conversational approach

• Strategic Goal

  Challenge Bloomberg

• Core Technology

  Large language model

Perplexity

In information theory, perplexity is a measure of uncertainty in the value of a sample from a discrete probability distribution. The larger the perplexity, the less likely it is that an observer can guess the value which will be drawn from the distribution. Perplexity was originally introduced in 1977 in the context of speech recognition by Frederick Jelinek, Robert Leroy Mercer, Lalit R. Bahl, and James K. Baker.

Web Search Results

• Perplexity - Wikipedia

  In information theory, perplexity is a measure of uncertainty in the value of a sample from a discrete probability distribution. The larger the perplexity, the less likely it is that an observer can guess the value which will be drawn from the distribution. Perplexity was originally introduced in 1977 in the context of speech recognition by Frederick Jelinek, Robert Leroy Mercer, Lalit R. Bahl, and James K. Baker.( [...] The perplexity is the exponentiation of the entropy, a more commonly encountered quantity. Entropy measures the expected or "average" number of bits required to encode the outcome of the random variable using an optimal variable-length code. It can also be regarded as the expected information gain from learning the outcome of the random variable, providing insight into the uncertainty and complexity of the underlying probability distribution. [...] Perplexity of a probability distribution ---------------------------------------- [edit] The perplexity _PP_ of a discrete probability distribution_p_ is a concept widely used in information theory, machine learning, and statistical modeling. It is defined as

• Perplexity: a more intuitive measure of uncertainty than entropy

  Perplexity is an information theoretic quantity that crops up in a number of contexts such as natural language processing and is a parameter for the popular t-SNE algorithm used for dimensionality reduction. Like entropy, perplexity provides a measure of the amount of uncertainty of a random variable. In fact, perplexity is simply a monotonic function of entropy. Given a discrete random variable, $X$, perplexity is defined as: where $H(X)$ is the entropy of $X$. [...] Like entropy, perplexity is an information theoretic quantity that describes the uncertainty of a random variable. In fact, perplexity is simply a monotonic function of entropy and thus, in some sense, they can be used interchangeabley. So why do we need it? In this post, I’ll discuss why perplexity is a more intuitive measure of uncertainty than entropy. ## Introduction [...] Arguably, perplexity provides a more human way of thinking about the random variable’s uncertainty and that is because the perplexity of a uniform, discrete random variable with K outcomes is K (see the Appendix to this post)! For example, the perplexity of a fair coin is two and the perplexity of a fair six-sided die is six. This provides a frame of reference for interpreting a perplexity value. That is, if the perplexity of some random variable X is 20, our uncertainty towards the outcome of

• Perplexity AI - Wikipedia

  Perplexity AI, or simply Perplexity, is a web search engine that uses a large language model to process queries and synthesize responses based on web search results. With a conversational approach, Perplexity allows users to ask follow-up questions and receive answers with citations of their sources from the internet.

• Understanding Perplexity in Language Models: A Detailed Exploration

  Perplexity is a measurement of uncertainty in the predictions of a language model. In simpler terms, it indicates how surprised a model is by the actual outcomes. The lower the perplexity, the better the model at predicting the next word in a sequence, reflecting higher confidence in its predictions. Mathematically, for a given probability model PPP and a sequence of N words w1,w2,…,wN, perplexity is defined as: [...] Perplexity is a key metric in natural language processing (NLP) that measures the quality of a language model. It evaluates how well a probabilistic model predicts a sample, particularly focusing on the sequence of words in a text. This article aims to provide an in-depth understanding of perplexity, its calculation, interpretation, and practical significance in evaluating language models. What is Perplexity? [...] # Improving Perplexity To improve the perplexity of your model: # Conclusion Perplexity is a critical metric in NLP for evaluating language models. It quantifies the model’s uncertainty in predicting the next word in a sequence, with lower values indicating better performance. By understanding and utilizing perplexity, we can better assess and improve language models to create more accurate and reliable NLP applications. Thanks! -- -- 2 Shubham Saxena Shubham Saxena

• Perplexity for LLM Evaluation - Comet

  Mathematically speaking, perplexity is defined as the exponentiated average log-likelihood of the predicted words in a sequence. Or, less verbosely, perplexity is cross-entropy with the exponential function applied. This transformation might seem somewhat arbitrary at first, but it actually makes a big difference, especially in terms of interpretability. [...] In summary, perplexity is a valuable metric for evaluating language models by measuring their confidence and predicting text sequences. While it offers useful insights, perplexity should be used alongside other metrics to get a fuller picture of model performance. This approach helps highlight specific strengths and weaknesses, allowing for more targeted improvements and reliable assessments of model quality. [...] `from opik.evaluation.metrics import base_metric, score_result class Perplexity(base_metric.BaseMetric): """ Perplexity (PPL) is a common LLM evaluation metric defined as the exponentiated average negative log-likelihood of a sequence. For more information on perplexity, see:

• Instance Of

  Q7889

In information theory, perplexity is a measurement of how well a probability distribution or probability model predicts a sample. It may be used to compare probability models. A low perplexity indicates the probability distribution is good at predicting the sample.