With learnt priors, RGB-only reconstruction with a monocular camera has made significant strides toward resolving the issues of low-texture areas and the inherent ambiguity of image-based reconstruction. Practical solutions for real-time execution have garnered considerable attention, as they are essential for interactive applications on mobile devices. Nevertheless, a crucial prerequisite yet to be considered…

  BRANCH-SOLVE-MERGE (BSM) is a program for enhancing Large Language Models (LLMs) in complex natural language tasks. BSM includes branching, solving, and merging modules to plan, crack, and combine sub-tasks. Applied to LLM response evaluation and constrained text generation with models like Vicuna, LLaMA-2-chat, and GPT-4, BSM boosts human-LLM agreement, reduces biases, and enables LLaMA-2-chat…

  Researchers from different Universities compare the effectiveness of language models (LLMs) and search engines in aiding fact-checking. LLM explanations help users fact-check more efficiently than search engines, but users tend to rely on LLMs even when the explanations are incorrect. Adding contrastive information reduces over-reliance but only significantly outperforms search engines. In high-stakes situations,…

  In recent years, there have been exceptional advancements in Artificial Intelligence, with many new advanced models being introduced, especially in NLP and Computer Vision. CLIP is a neural network developed by OpenAI trained on a massive dataset of text and image pairs. It has helped advance numerous computer vision research and has supported modern…

  Language models like GPT-3 are designed to be neutral and generate text based on the patterns they’ve learned in the data. They don’t have inherent sentiments or emotions. If the data used for training contains biases, these biases can be reflected in the model’s outputs. However, their output can be interpreted as positive, negative,…