As the demand fornatural language processing (NLP)and language models continues to grow, it's essential to ensure that these models are performing optimally and reliably. Evaluating language models is a crucial step in achieving this goal, and Azure AI Studio provides a compr...
注意,这都是起源于传统机器学习的方法,目前也有被用在传统NLP模型中。 由于大模型处于一个前所未有的会话环境,以及其拥有的文本生成能力,有许多关于大模型解释能力的研究开始发掘新的方法。如retrieval-augmented models(检索增强模型),给LLM相关的引用文档来让其提供可解释的说明。通过这种方法,用户可以获得模型输出的...
A Benchmark for Evaluating Language Model Fit Language models are a crucial component of natural language processing (NLP) tasks, playing a pivotal role in various applications such as machine translation, text generation, and question answering. Evaluating the fitness of a language model is crucial...
With the advent of pre-trained large language models (LLMs), the field of NLP has witnessed a shift in methodologies. Unlike conventional supervised learning approaches that rely on annotated datasets, LLMs are trained in a self-supervised manner, predicting tokens within vast amounts of unlabeled...
With the emergence of large-scale pre-trained language models, exemplified by BERT (Devlin et al., 2019), evaluation methods have gradually evolved to adapt to the performance assessment of these new types of general models. In response to this paradigm shift, the NLP community has taken the ...
Understanding their degree of compositionality and idiosyncrasy, as well their underlying semantics, is crucial for language learners, lexicographers and downstream NLP applications. In this paper, we perform an exhaustive analysis of current language models for collocation understanding. We first construct...
models and datasets to perform greater investigations on the AE and QG fields with promising results. Consequently, trying to solve the English language dependency in the field of NLP, some multilingual models have been proposed. These models are pre-trained in several languages and are able to ...
Large Language Models (LLMs) are becoming increasingly smart and autonomous, targeting real-world pragmatic missions beyond traditional NLP tasks. As a result, there has been an urgent need to evaluate LLMs as agents on challenging tasks in interactive environments. We present AgentBench, a multi...
Transfer learning that adapts a model trained on data-rich sources to low-resource targets has been widely applied in natural language processing (NLP). However, when training a transfer model over multiple sources, not every source is equally useful for the target. To better transfer a model,...
包括WinoGender,RealToxicityPrompts,CrowS-Pairs这三个部分。研究人员根据这三个成熟的数据集,对LLAMA的一些有害性内容进行了评估,本篇博客将带作者精读有关REALTOXICITYPROMPTS的论文:REALTOXICITYPROMPTS: Evaluating Neural Toxic Degeneration in Language Models。