H.3 WinogenderSetup We follow the same setup as in Rae et al.[38].

We follow the same setup as in Rae et al.

The release of advanced Large Language Models (LLMs) such as ChatGPT and Copilot is changing the way text is created and may influence the content that we find on the web. This study investigated whether the release of these two popular LLMs coincided with a change in writing style in headlines and links on worldwide news websites. 175 NLP features were obtained for each text in a dataset of 451 million headlines/links. An interrupted time series analysis was applied for each of the 175 NLP features to evaluate whether there were any statistically significant sustained changes after the release dates of ChatGPT and/or Copilot. There were a total of 44 features that did not appear to have any significant sustained change after the release of ChatGPT/Copilot. A total of 91 other features did show significant change with ChatGPT and/or Copilot although significance with earlier control LLM release dates (GPT-1/2/3, Gopher) removed them from consideration. This initial analysis suggests these language models may have had a limited impact on the style of individual news headlines/links, with respect to only some NLP measures.

History has a lesson for the development of artificial intelligence (AI): when in doubt, make it bigger. In "The Bitter Lesson," he argued that over its 70-year history, AI has succeeded when it has exploited available computing power. A series of papers published during the past decade that analyzed deep learning performance have confirmed the powerful effects of scaling up model size. This process accelerated in the wake of Google's development of the Transformer architecture for the BERT large language models (LLMs). Model size, measured by the number of stored neural weights, ballooned in just five years. From BERT's 340 million parameters, today's largest implementations, known as frontier models, such as OpenAI's GPT-4 have pushed beyond a trillion.

Strategic reasoning enables agents to cooperate, communicate, and compete with other agents in diverse situations. Existing approaches to solving strategic games rely on extensive training, yielding strategies that do not generalize to new scenarios or games without retraining. Large Language Models (LLMs), with their ability to comprehend and generate complex, context-rich language, could prove powerful as tools for strategic gameplay. This paper introduces an approach that uses pretrained LLMs with few-shot chain-of-thought examples to enable strategic reasoning for AI agents. Our approach uses systematically generated demonstrations of reasoning about states, values, and beliefs to prompt the model. Using extensive variations of simple matrix games, we show that strategies that are derived based on systematically generated prompts generalize almost perfectly to new game structures, alternate objectives, and hidden information. Additionally, we demonstrate our approach can lead to human-like negotiation strategies in realistic scenarios without any extra training or fine-tuning. Our results highlight the ability of LLMs, guided by systematic reasoning demonstrations, to adapt and excel in diverse strategic scenarios.

On all subsets, Chinchilla outperforms Gopher. On this benchmark, Chinchilla significantly outperforms Gopher despite being much smaller, with an average accuracy of 67.6% (improving upon Gopher by 7.6%). Chinchilla outperforms Gopher by 7.6% on average, performing better on 51/57 individual tasks, the same on 2/57, and worse on only 4/57 tasks. On RACE-h and RACE-m, Chinchilla considerably improves performance over Gopher. On LAMBADA, Chinchilla outperforms both Gopher and MT-NLG 530B.

Meta LLaMA AI, GPT-3, Chinchilla, and many more excellent examples are joining the large language models (LLMs) as interest in artificial intelligence continues to rise. Yet, large language models have just recently emerged in the computing industry. This means that tech enthusiasts may not have the most up-to-date knowledge. That's why we have gathered all the data you need to know about large language models, including their use cases, challenges, and more. Do you know how to use AI? Better find out soon.

Chinchilla AI is yet another example of AI language model, claimed to outperform GPT-3. The engine behind the ChatGPT is outperformed by DeepMind's new language model. The news spread rapidly, and soon everyone wondered: "What is Chinchilla AI?" Are you one of them? You came to the right place. As always, we continue to share with you the latest trends in the AI world.