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Today, I would like to explore some of the popular Large Language Models (LLMs) that have gained prominence in 2023. Let’s take a look.

1. GPT-3 and GPT-4 by OpenAI

GPT-3 is a more general-purpose model that can be used for a wide range of language-related tasks. ChatGPT is designed specifically for conversational tasks. GPT-4 is OpenAI’s most advanced system, producing safer and more useful responses and can solve difficult problems with greater accuracy, thanks to its broader general knowledge and problem solving abilities.

2. LaMDA by Google

LaMDA is a family of Transformer-based models that is specialized for dialog. These models have up to 137B parameters and are trained on 1.56T words of public dialog data.

3. PaLM by Google

PaLM is a language model with 540B parameters that is capable of handling various tasks, including complex learning and reasoning. It can outperform state-of-the-art language models and humans in language and reasoning tests. The PaLM system uses a few-shot learning approach to generalize from small amounts of data, approximating how humans learn and apply knowledge to solve new problems.

4. Gopher by Deepmind

DeepMind’s language model Gopher is significantly more accurate than existing large language models on tasks like answering questions about specialized subjects such as science and humanities and equal to them in other tasks like logical reasoning and mathematics. Gopher has 280B parameters that it can tune, making it larger than OpenAI’s GPT-3, which has 175 billion.

5. Chinchilla by Deepmind

Chinchilla uses the same computing budget as Gopher, however, with only 70 billion parameters and four times more data. It outperforms models like Gopher, GPT-3 on many downstream evaluation tasks. It uses significantly less computing for fine-tuning and inference, greatly facilitating downstream usage.

6. Ernie 3.0 Titan by Baidu

Ernie 3.0 was released by Baidu and Peng Cheng Laboratory. It has 260B parameters and excels at natural language understanding and generation. It was trained on massive unstructured data and achieved state-of-the-art results in over 60 NLP tasks, including machine reading comprehension, text categorization, and semantic similarity. Additionally, Titan performs well in 30 few-shot and zero-shot benchmarks, showing its ability to generalize across various downstream tasks with a small quantity of labeled data.

7. PanGu-Alpha by Huawei

Huawei has developed a Chinese-language equivalent of OpenAI’s GPT-3 called PanGu-Alpha. This model is based on 1.1 TB of Chinese-language sources, including books, news, social media, and web pages, and contains over 200 billion parameters, 25 million more than GPT-3. PanGu-Alpha is highly efficient at completing various language tasks like text summarization, question answering, and dialogue generation.

8. LLaMA by Meta AI

The Meta AI team introduces LLaMA (Large Language Model Meta AI), a collection of foundational language models with 7B to 65B parameters. LLaMA 33B and 65B were trained on 1.4 trillion tokens, while the smallest model, LLaMA 7B, was trained on one trillion tokens. They exclusively used publicly available datasets, without depending on proprietary or restricted data. The team also implemented key architectural enhancements and training speed optimization techniques. Consequently, LLaMA-13B outperformed GPT-3, being over 10 times smaller, and LLaMA-65B exhibited competitive performance with PaLM-540B.

9. OPT-IML by Meta AI

OPT-IML is a pre-trained language model based on Meta’s OPT model and has 175 billion parameters. OPT-IML is fine-tuned for better performance on natural language tasks such as question answering, text summarization, and translation using about 2000 natural language tasks.

This is all for now. Hope you enjoy that.

By Asahi

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OpenAI said developers with a “successful payment history” can access GPT-4. The company plans to open access to new developers later this month, after which it will begin increasing availability limits “depending on computing availability.”

GPT-4 can generate text (including code) and accept image and text input. It is an improvement over his text-only predecessor GPT-3.5, and performs “human-level” on several academic benchmarks and experts. Like his previous OpenAI GPT model, GPT-4 was trained using public data such as public web pages and data licensed from OpenAI.

Image compression functionality is not yet available in all his OpenAI clients. OpenAI is first testing with one of her partners, Be My Eyes. But it has not said when it will open up to a wider customer base.

OpenAI is one of the other modern but less capable text generation models (and one of the original models powering ChatGPT), GPT- 4 and GPT-3.5 Turbo can be adjusted. This has long been possible with some of OpenAI’s other text generation models. OpenAI says the feature is expected to arrive later this year.

In a related announcement today, OpenAI announced the general availability of its DALL-E 2 and Whisper APIs: DALL-E 2 is OpenAI’s imaging model and “Whisper” refers to the company’s speech-to-text model. increase. The company also said it plans to deprecate older models available through APIs in order to “optimize computing power.” (Over the past few months, OpenAI has struggled to keep up with the demand for generative models, largely thanks to the growing popularity of ChatGPT.)

As of January 4, 2024, certain older OpenAI models of him, specifically his GPT-3 and its derivatives, will no longer be available in favor of newer “GPT-3-based” models that are considered more computationally efficient. be replaced. Developers using older models will need to manually update their integrations by January 4th, and if they want to continue using the old tuned model after January 4th, they will need to update to the new GPT-3 base You need to adjust the replacement in addition to the model.

You can read the blog post from OpenAI here.

Yuuma

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今回は感情分析(Sentiment Analysis)のプロンプトを紹介します。
AIは文章を解析して、その文章が、ポジティブ、ニュートラル、またはネガティブかの
感情分析の判断をおこなうことができます。

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以前に記事にしたAutoGPTを使用した感想です。

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Nowadays, Artificial Intelligence (AI) has witnessed remarkable advancements and one technology that has taken center stage is Large Language Models (LLMs). These models have revolutionized natural language processing and have far-reaching implications across various domains. Today, we will explore the capabilities and significance of LLMs in the AI field.

Large Language Models are sophisticated AI models trained on massive amounts of text data. They leverage deep learning techniques, particularly transformers, to process and understand natural language. These models possess an exceptional ability to generate human-like text, comprehend context and answer questions.

Training Process

Training LLMs involves exposing the model to vast quantities of text from various sources such as books, articles and websites. With the help of this extensive training data, the models learn grammar, semantics and contextual relationships, enabling them to generate coherent and contextually relevant responses.

Applications and Benefits

1. Natural Language Understanding: LLMs excel at understanding and interpreting natural language, enabling them to perform tasks like sentiment analysis, language translation, and text summarization. They can comprehend nuances, context and even generate human-like responses.

2. Chatbots and Virtual Assistants: LLMs play a vital role in the development of intelligent chatbots and virtual assistants. By leveraging their language comprehension and generation capabilities, these models enhance user interactions, providing personalized and context-aware responses.

3. Content Generation: LLMs can generate high-quality content, including articles, stories and poems. They assist content creators by providing suggestions, auto-completion, and ensuring grammatical correctness. These models save time and improve content quality.

4. Research and Knowledge Discovery: LLMs act as powerful research tools, capable of analyzing vast amounts of text and extracting insights. Researchers can utilize these models to explore scientific literature, identify patterns and generate hypotheses, which accelerate the pace of discovery.

5. Language Learning and Accessibility: Today, LLMs can learn language and additional data. They can provide interactive language tutoring, generate practice exercises, and offer real-time feedback.

Challenges and Ethical Considerations

While LLMs offer incredible abilities, there are challenges and ethical considerations to address. Some challenges include biases in training data, potential misinformation propagation, and the need for responsible AI development to ensure fair and ethical usage.

Conclusion

Large Language Models have emerged as a groundbreaking technology in the AI field, with applications spanning natural language understanding, content generation, research, and language learning. Their ability to process and generate human-like text has significantly impacted industries and transformed user experiences.

This is all for now. Hope you enjoy that.

By Asahi

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