Japan's top bank CEOs push for AI, soothing worry over human work Japan's top financial leaders are working to ease fears that AI will cost jobs, emphasizing its role in boosting efficiency and transforming work. The heads of Japan's biggest financial firms are going out of their way to assuage worries that artificial intelligence will take away jobs. I don't think humans will lose their value. Humans have ability for dialogue, empathy, creativity and ethics," Mizuho Chief Executive Officer Masahiro Kihara said on Thursday at an event hosted by the Nikkei. People might say, 'what about my job if we use more AI?' I think they can aim for more value-added work."

Next, we propose a high-frequency guidance sampling method to explicitly control the denoising process and preserve high-frequency details in unprocessed areas. Finally, we design an auxiliary prompting adapter, utilizing Vision-Language Models (VLMs) to enhance text prompts and improve the model's task

Pre-trained large language models (LLMs) based on Transformer have demonstrated striking in-context learning (ICL) abilities.

Large language models (LLMs) are trained on a deluge of text data with limited quality control. As a result, LLMs can exhibit unintended or even harmful behaviours, such as leaking information, fake news or hate speech.

Learned Feedback Pattern (LFP) for patterns in an LLM's activations learned during RLHF that improve its performance on the fine-tuning task.

Flexible and accurate drag-based editing is a challenging task that has recently garnered significant attention.

From chiaroscuro in Renaissance paintings to the interplay of light and dark in Ansel Adams' photographs, humans are masters at perceiving three-dimensional shape from variations of image

Wikipedia is a critical resource for modern NLP, serving as a rich repository of up-to-date and citation-backed information on a wide variety of subjects. The reliability of Wikipedia -- its groundedness in its cited sources -- is vital to this purpose. This work analyzes both how grounded Wikipedia is and how readily fine-grained grounding evidence can be retrieved. To this end, we introduce PeopleProfiles -- a large-scale, multi-level dataset of claim support annotations on biographical Wikipedia articles. We show that: (1) ~22% of claims in Wikipedia lead sections are unsupported by the article body; (2) ~30% of claims in the article body are unsupported by their publicly accessible sources; and (3) real-world Wikipedia citation practices often differ from documented standards. Finally, we show that complex evidence retrieval remains a challenge -- even for recent reasoning rerankers.