评估方法被分位三个类别:code evaluation、human evaluation、model evaluation。 4.3 Evaluation Dataset Summary
5. Multimodal science question 5.1. ScienceQA ScienceQA is the first large-scale multimodal science question dataset that annotates the answers with detailed lectures and explanations. Question classes: NAT = natural science, SOC = social science, LAN = language science, TXT = text context, IMG ...
What Is a Large Language Model? A large language model (LLM) is an artificial intelligence system that has been trained on a vast dataset, often consisting of billions of words taken from books, the web, and other sources, to generate human-like, contextually relevant responses to queries. ...
dataset=(words, antonyms), eval_template=eval_template, ) print("result:", result) print("demo_fn:", demo_fn) prompt_gen_template: I gave a friend an instruction. Based on the instruction they produced the following input-output pairs: ...
For each dataset, I find that GPT-4's judgments are positively correlated with human judgments, in some cases rivaling or even exceeding the average inter-annotator agreement displayed by humans. I then identify several ways in which LLM-generated norms differ from human-generated norms ...
2.2) and train a simplified model with this new dataset. It is important to note that user applications are relatively deterministic, which makes a smaller model sufficient despite its lower generalizability. 3 Practices We have conducted preliminary practices to show the effectiveness of automatically...
Large language model (LLM) finetuning is a way to enhance the performance of pretrained LLMs for specific tasks or domains, with the aim of achieving improved inference quality with limited resources. Finetuning is crucial for domain-specific applications where pretrained models lack necessary ...
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KM scaling law. In 2020, Kaplan et al. [30] (the OpenAI team) firstly proposed to model the power-law relationship of model performance with respective to three major factors, namelymodel size (N), dataset size (D), and the amount of training compute (C), for neural language models. ...
as they can be present within the datasets that LLMs use to learn. When the dataset that’s used for training is biased, that can then result in a large language model generating and amplifying equally biased,