“Don’t believe everything you get from ChatGPT“ – Abraham Lincoln Let’s talk about hallucinations – those, in the context of LLMs, mean generating plausible-looking but false or misleading information. I sometimes wonder how much of their bad reputation got stuck with us because first impressions are the most lasting. Initially, I thought that once people…
As the language models are improving, their adoption is growing in more complex tasks such as free-form question answering or summarization. On the other hand, the more demanding the task – the higher the risk of LLM hallucinations. In this article, you’ll find: what the problem with hallucination is, which techniques we use to reduce…
Web Agents are no longer just a concept from science fiction—they’re the cutting-edge tools that are automating and streamlining our online interactions at an unprecedented scale. From effortlessly sifting through vast amounts of information to performing complex tasks like form submissions and website navigation, these agents are redefining efficiency in the digital age. Thanks to…
Generative AI and Large Language Models (LLMs) have burst onto the scene, introducing us to “copilots,” “chatbots,” and the increasingly pivotal “AI agents.” These advancements unfold at breakneck speed, making it challenging to keep up. We’ve been at the forefront of this revolution, witnessing how AI agents—or “agentic workflows,” as Andrew Ng refers to them—are…
Every computation requires computing resources. Sure, sometimes a regular calculator, a piece of paper, and a pencil are sufficient. However, in machine learning, powerful computing resources are necessary: The model needs to be fed with a massive amount of data. Appropriate calculations must be performed for each data point to process it into a pattern.…
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In today’s rapidly evolving generative AI world, keeping pace requires more than embracing cutting-edge technology. At deepsense.ai, we don’t merely follow trends; we aspire to establish new solutions. Our latest achievement combines Advanced Retrieval-Augmented Generation (RAG) with Small Language Models (SLMs), aiming to enhance the capabilities of embedded devices beyond traditional cloud solutions. Yet, it’s…
Instruction-based image editing improves the controllability and flexibility of image manipulation via natural commands without elaborate descriptions or regional masks. However, human instructions are sometimes too brief for current methods to capture and follow. Multimodal large language models (MLLMs) show promising capabilities in cross-modal understanding and visual-aware response generation via LMs. We investigate how MLLMs…
Conformal prediction (CP) for regression can be challenging, especially when the output distribution is heteroscedastic, multimodal, or skewed. Some of the issues can be addressed by estimating a distribution over the output, but in reality, such approaches can be sensitive to estimation error and yield unstable intervals. Here, we circumvent the challenges by converting regression…
Rendering scenes observed in a monocular video from novel viewpoints is a chal- lenging problem. For static scenes the community has studied both scene-specific optimization techniques, which optimize on every test scene, and generalized tech- niques, which only run a deep net forward pass on a test scene. In contrast, for dy- namic scenes, scene-specific…