The news: Meta is going all in on open-source AI. The company has unveiled LLaMA 2, its first large language model that's available for anyone to use--for free. It's also releasing a version of the AI model that people can build into ChatGPT-style chatbots. Why it matters: The idea is that by releasing the model into the wild and letting developers and companies tinker with it, Meta will learn important lessons about how to make its models safer, less biased, and more efficient. But… Many caveats still remain.

The article you are about to read was written by a human. This kind of disclaimer will become an everyday occurrence as chatbots, or large language models, infiltrate deeper into our media space. Doubts about the veracity of such disclaimers will also become commonplace. With the leaps and bounds registered by machine learning and large language models over the past couple of years, it is becoming increasingly difficult to prove that a human is on the other side of a written or spoken communication. How would I prove to you that these words were the product of human creativity and exertion?

Nick Clegg has defended the release of an open-source artificial intelligence model by Mark Zuckerberg's Meta, as he claimed that "hype" about AI's dangers was running ahead of the technology's development. The president of global affairs at Meta and former UK deputy prime minister spoke on Wednesday after the company said it was opening access to its new large language model (LLM), Llama 2, which will be free for research and commercial use. LLMs are trained on vast amounts of data and underpin generative AI products such as the ChatGPT chatbot. Some experts have warned that making AI models open source – or freely available to use and adapt for unique purposes – could lead to the technology being used for malicious purposes. Speaking on BBC Radio 4's Today programme, Clegg said: "My view is that the hype has somewhat run ahead of the technology. I think a lot of the existential warnings relate to models that don't currently exist, so-called super-intelligent, super-powerful AI models – the vision where AI develops an autonomy and agency on its own, where it can think for itself and reproduce itself. "The models that we're open-sourcing are far, far, far short of that.

Large Language Models - the platforms which power chatbots like ChatGPT - are basically joining dots in enormous datasets of text, and guessing the next word in a sequence, he said. He added that the existential threat warnings issued by some AI experts relate to systems which don't yet exist.

NEW YORK – Meta is releasing a commercial version of its open-source artificial intelligence model, Llama, the company said Tuesday, giving startups and other businesses a powerful free-of-charge alternative to pricey proprietary models sold by OpenAI and Google. The new version of the model, called Llama 2, will be distributed by Microsoft through its Azure cloud service and will run on the Windows operating system, Meta said in a blog post, referring to Microsoft as "our preferred partner" for the release. The model, which Meta previously provided only to select academics for research purposes, will also be made available via direct download and through Amazon Web Services, Hugging Face and other providers, according to the blog post and a separate Facebook post by Meta CEO Mark Zuckerberg. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Margaret Atwood, Jonathan Franzen, James Patterson, Suzanne Collins and Viet Thanh Nguyen are among the prominent authors endorsing the letter addressed to the CEOs of OpenAI, Meta, Microsoft, Alphabet, IBM and Stability AI. In the letter organised by the Authors Guild, the largest professional writers' organisation in the United States, the signatories call attention to the "inherent injustice in exploiting our works as part of your AI systems without our consent, credit, or compensation". "These technologies mimic and regurgitate our language, stories, style, and ideas. "You're spending billions of dollars to develop AI technology. It is only fair that you compensate us for using our writings, without which AI would be banal and extremely limited."

General large language models (LLMs) such as ChatGPT have shown remarkable success. However, such LLMs have not been widely adopted for medical purposes, due to poor accuracy and inability to provide medical advice. We propose IvyGPT, an LLM based on LLaMA that is trained and fine-tuned with high-quality medical question-answer (QA) instances and Reinforcement Learning from Human Feedback (RLHF). After supervised fine-tuning, IvyGPT has good multi-turn conversation capabilities, but it cannot perform like a doctor in other aspects, such as comprehensive diagnosis. Through RLHF, IvyGPT can output richer diagnosis and treatment answers that are closer to human. In the training, we used QLoRA to train 33 billion parameters on a small number of NVIDIA A100 (80GB) GPUs. Experimental results show that IvyGPT has outperformed other medical GPT models.

We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information -- an issue that previously made the use of Large Language Models (LLMs) in scientific fields challenging. Our approach involves the development of a workflow implementing three different processes for text mining, programmed by ChatGPT itself. All of them enable parsing, searching, filtering, classification, summarization, and data unification with different tradeoffs between labor, speed, and accuracy. We deploy this system to extract 26,257 distinct synthesis parameters pertaining to approximately 800 MOFs sourced from peer-reviewed research articles. This process incorporates our ChemPrompt Engineering strategy to instruct ChatGPT in text mining, resulting in impressive precision, recall, and F1 scores of 90-99%. Furthermore, with the dataset built by text mining, we constructed a machine-learning model with over 86% accuracy in predicting MOF experimental crystallization outcomes and preliminarily identifying important factors in MOF crystallization. We also developed a reliable data-grounded MOF chatbot to answer questions on chemical reactions and synthesis procedures. Given that the process of using ChatGPT reliably mines and tabulates diverse MOF synthesis information in a unified format, while using only narrative language requiring no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be very useful across various other chemistry sub-disciplines.

Generative language models produce highly abstractive outputs by design, in contrast to extractive responses in search engines. Given this characteristic of LLMs and the resulting implications for content Licensing & Attribution, we propose the the so-called Extractive-Abstractive axis for benchmarking generative models and highlight the need for developing corresponding metrics, datasets and annotation guidelines. We limit our discussion to the text modality.

While instruction-tuned models have shown remarkable success in various natural language processing tasks, accurately evaluating their ability to follow instructions remains challenging. Existing benchmarks primarily focus on common instructions that align well with what the model learned during training. However, proficiency in responding to these instructions does not necessarily imply strong ability in instruction following. In this paper, we propose a novel instruction-following evaluation protocol called verbalizer manipulation. It instructs the model to verbalize the task label with words aligning with model priors to different extents, adopting verbalizers from highly aligned (e.g., outputting "postive" for positive sentiment), to minimally aligned (e.g., outputting "negative" for positive sentiment). Verbalizer manipulation can be seamlessly integrated with any classification benchmark to examine the model's reliance on priors and its ability to override them to accurately follow the instructions. We conduct a comprehensive evaluation of four major model families across nine datasets, employing twelve sets of verbalizers for each of them. We observe that the instruction-following abilities of models, across different families and scales, are significantly distinguished by their performance on less natural verbalizers. Even the strongest GPT-4 model struggles to perform better than random guessing on the most challenging verbalizer, emphasizing the need for continued advancements to improve their instruction-following abilities. Large language models have achieved remarkable success in zero-shot generalization for various natural language processing (NLP) tasks via instruction tuning (Wei et al., 2022a; Ouyang et al., 2022; Sanh et al., 2022; Iyer et al., 2022). Existing benchmark datasets (Wang et al., 2018; 2019; Cobbe et al., 2021; Hendrycks et al., 2021; Li et al., 2023) primarily focus on common instructions that align well with what models learned during pre-training or instructiontuning.