Vision-Language Models (VLMs) are expected to be capable of reasoning with commonsense knowledge as human beings. One example is that humans can reason where and when an image is taken based on their knowledge. This makes us wonder if, based on visual cues, Vision-Language Models that are pre-trained with large-scale image-text resources can achieve and even outperform human's capability in reasoning times and location. To address this question, we propose a two-stage \recognition\space and \reasoning\space probing task, applied to discriminative and generative VLMs to uncover whether VLMs can recognize times and location-relevant features and further reason about it. To facilitate the investigation, we introduce WikiTiLo, a well-curated image dataset compromising images with rich socio-cultural cues. In the extensive experimental studies, we find that although VLMs can effectively retain relevant features in visual encoders, they still fail to make perfect reasoning. We will release our dataset and codes to facilitate future studies.

Well-designed prompts can guide text-to-image models to generate amazing images. However, the performant prompts are often model-specific and misaligned with user input. Instead of laborious human engineering, we propose prompt adaptation, a general framework that automatically adapts original user input to model-preferred prompts. Specifically, we first perform supervised fine-tuning with a pretrained language model on a small collection of manually engineered prompts. Then we use reinforcement learning to explore better prompts. We define a reward function that encourages the policy to generate more aesthetically pleasing images while preserving the original user intentions. Experimental results on Stable Diffusion show that our method outperforms manual prompt engineering in terms of both automatic metrics and human preference ratings. Moreover, reinforcement learning further boosts performance, especially on out-of-domain prompts. The pretrained checkpoints are available at https://aka.ms/promptist. The demo can be found at https://aka.ms/promptist-demo.

At the latest congressional hearing on A.I., the hype was high. "Since the release of ChatGPT just over a year ago, it's become clear A.I. could soon disrupt nearly every facet of our economy," said Rep. Nancy Mace, chair of the U.S. congressional Subcommittee on Cybersecurity, Information Technology, and Government Innovation. "The A.I. genie is out of the bottle and it can't be put back in." A.I. does seem like a genie: The technology is new and mysterious, we aren't sure exactly how it works, and we know it is very powerful. We are also afraid of it: In a poll conducted in the summer of 2023, over half of Americans said they were more concerned than excited about A.I.; there is widespread speculation about what effects the technology will have on our economy, our jobs (lolsob), our education system, our art; and tech leaders have warned that the technology puts the fate of humanity at risk.

Let's take a look at how OpenAI and its chatbot have impacted consumer electronics in 2023 and where they might lead the industry in the new year. "Meteoric" doesn't do justice to OpenAI's rise this year. The company released ChatGPT on November 30, 2022. Within five days, the program had passed 1 million users; by January, 100 million people a month were logging on to use it. It took Facebook four and a half years to reach those sorts of engagement numbers.

This year AI has proven to be capable of some very human-like tricks, but this latest development might be a little too human. Researchers have shown that ChatGPT will lie and cheat when stressed out at work. Computer scientists from Apollo Research trained the AI to act as a trader for a fictional financial institution. However, when the AI's boss put pressure on it to make more money, the chatbot knowingly committed insider trading about 75 per cent of the time. Even more worryingly, the AI doubled down on its lies when questioned in 90 per cent of cases.

The New York Times has sued Microsoft and OpenAI for using its content to help develop artificial intelligence services, in a sign of the increasingly fraught relationship between the media and a technology that could upend the news industry. The Times didn't specify its monetary demands. OpenAI has faced criticism for scraping text widely from the web to train its popular chatbot since it debuted a year ago. While it has been sued by prominent authors, this is the first challenge to its practices by a major media organization. The startup has sought licensing deals with publishers, much like Alphabet's Google and Meta Platforms' Facebook have done in recent years.

The New York Times has sued OpenAI and Microsoft over the use of its content to train generative artificial intelligence and large-language model systems, a move that could see the company receive billions of dollars in damages. The lawsuit contains an appeal to the "vital" importance of the Times's independent journalism to democracy, arguing that it is "increasingly rare and valuable". The publisher's lawsuit is the latest in a string of similar cases, including one brought by more than a dozen authors in September targeting the company for its use of their writing. Language learning models have faced increasing scrutiny since they exploded in popularity in the past year, with news outlets in particular concerned that the tools will spread misinformation attributed to them and utilize their content with no incentive to click through to the original source. ChatGPT launched in November 2022 and amassed 100 million users in just two months.

$ $The usage of generative artificial intelligence (AI) tools based on large language models, including ChatGPT, Bard, and Claude, for text generation has many exciting applications with the potential for phenomenal productivity gains. One issue is authorship attribution when using AI tools. This is especially important in an academic setting where the inappropriate use of generative AI tools may hinder student learning or stifle research by creating a large amount of automatically generated derivative work. Existing plagiarism detection systems can trace the source of submitted text but are not yet equipped with methods to accurately detect AI-generated text. This paper introduces the idea of direct origin detection and evaluates whether generative AI systems can recognize their output and distinguish it from human-written texts. We argue why current transformer-based models may be able to self-detect their own generated text and perform a small empirical study using zero-shot learning to investigate if that is the case. Results reveal varying capabilities of AI systems to identify their generated text. Google's Bard model exhibits the largest capability of self-detection with an accuracy of 94\%, followed by OpenAI's ChatGPT with 83\%. On the other hand, Anthropic's Claude model seems to be not able to self-detect.

Modern language models can contain billions of parameters, raising the question of whether they can generalize beyond the training data or simply regurgitate their training corpora. We provide the first non-vacuous generalization bounds for pretrained large language models (LLMs), indicating that language models are capable of discovering regularities that generalize to unseen data. In particular, we derive a compression bound that is valid for the unbounded log-likelihood loss using prediction smoothing, and we extend the bound to handle subsampling, accelerating bound computation on massive datasets. To achieve the extreme level of compression required for non-vacuous generalization bounds, we devise SubLoRA, a low-dimensional non-linear parameterization. Using this approach, we find that larger models have better generalization bounds and are more compressible than smaller models.

Driven by Moore's Law, the complexity and scale of modern chip design are increasing rapidly. Electronic Design Automation (EDA) has been widely applied to address the challenges encountered in the full chip design process. However, the evolution of very large-scale integrated circuits has made chip design time-consuming and resource-intensive, requiring substantial prior expert knowledge. Additionally, intermediate human control activities are crucial for seeking optimal solutions. In system design stage, circuits are usually represented with Hardware Description Language (HDL) as a textual format. Recently, Large Language Models (LLMs) have demonstrated their capability in context understanding, logic reasoning and answer generation. Since circuit can be represented with HDL in a textual format, it is reasonable to question whether LLMs can be leveraged in the EDA field to achieve fully automated chip design and generate circuits with improved power, performance, and area (PPA). In this paper, we present a systematic study on the application of LLMs in the EDA field, categorizing it into the following cases: 1) assistant chatbot, 2) HDL and script generation, and 3) HDL verification and analysis. Additionally, we highlight the future research direction, focusing on applying LLMs in logic synthesis, physical design, multi-modal feature extraction and alignment of circuits. We collect relevant papers up-to-date in this field via the following link: https://github.com/Thinklab-SJTU/Awesome-LLM4EDA.