Tempus AI’s Post

🌐 Exciting news! Check out our latest blog post - "CultureLLM: Incorporating Cultural Differences into Large Language Models" on arXiv (2402.10946v1). This paper introduces CultureLLM, a cost-effective solution to integrate cultural nuances into LLMs using World Value Survey (WVS) seed data and semantic data augmentation. With only 50 seed samples, CultureLLM outperforms counterparts like GPT-3.5 and Gemini Pro by 8.1% and 9.5% respectively, and matches the performance of GPT-4. Explore the groundbreaking research and its implications for 9 diverse cultures. Read the full post here: https://bit.ly/42N2HtQ.

Excited to share a groundbreaking new post on scientific Natural Language Inference (NLI)! The blog introduces the MSciNLI dataset, diversifying the scientific NLI task with 132,320 sentence pairs from five cutting-edge domains. We establish strong baselines on MSciNLI, highlighting the challenge for pre-trained and large language models. Additionally, we delve into domain shift's impact on model performance and demonstrate the dataset's potential for improving downstream tasks in the scientific domain. Dive into the details at https://bit.ly/3JjbwTd.

🌐 Excited to share our latest blog post on "LLMGeo: Benchmarking Large Language Models on Image Geolocation In-the-wild." Our team dives into the critical task of image geolocation and evaluates the capabilities of multimodal language models using a novel image dataset and comprehensive framework. Discover our findings and insights here: https://bit.ly/4bI5s3i #Geolocation #LanguageModels #ImageUnderstanding 📸

I think this is a big deal. Adeel Hassan just posted our latest blog post on some of the things we're working on at Element 84 with AI/ML and geospatial data. If you have a minute, it's a great read and gives just a taste of what we believe is possible in this space. Showing a modicum of self-awareness, I know I'm drinking the kool-aid right now. We're solidly on the hype-curve for AI/ML but I've decided I'm ok hanging out here for the moment. AI/ML is moving *SO FAST*. The billion? trillion? dollar question is "Is it moving this fast because we're screaming through the 'easy' 80% part and we're going to grind to a halt when we hit the 'hard' 20% that makes it useful and production ready, or have we already crossed into the 'this could change everything' part of the technology?" I believe the possible implications of good Foundation models and LLMs are hard to overstate. It could change software engineering as we know it, it could change UI design as we know it, it could change user interaction patterns, it could change how and when we process remote sensing data, and these are just the obvious things. Image generation (https://lnkd.in/g6X7d79u) with approaches like stable diffusion have major implications on the stock photo industry. Video generation (https://www.cinemaflow.ai/) has major implications on the B-roll video industry and video production in general. It's yet to be really proven but tooling like GitHub's Copilot (https://lnkd.in/gmPxTu8r) and AWS' Code Whisperer (https://lnkd.in/gSpAgkTJ) have the potential to upend software engineering tasks. This could wreak havoc (in a good way) on entire business models. To be sure, there are issues to untangle, including intellectual property rights and ethical concerns, all of which are actively being discussed and worked by the industry, but sitting here right now, I think we're just beginning to imagine a world with powerful GenAI capabilities woven into what we do and how we do it. I'm prepared to be totally wrong here, but in the meantime grab a glass of fruit punch kool-aid with me and imagine what the Industrial Revolution for Knowledge Workers looks like... #genai #remotesensing #aiml #earthobservation

Exploring the Pros and Cons of Large Language Models for Intelligence Community Tasks https://lnkd.in/e-BJYM8U In this short video, the Software Engineering Institute | Carnegie Mellon University at Carnegie Mellon University explores the possibilities and challenges of applying Large Language Models (LLMS) to high-stakes intelligence work. Team members for the SEI AI Division provide insights and learnings from a recent study on how these advanced AI systems are reshaping information access and interaction while posing significant challenges for intelligence analysis. #llms #ai

Humans can extract rich and nuanced meaning with language. But how? A finely detailed cortical organization of semantic representations at the neuron scale in humans. Semantic encoding during language comprehension at single-cell resolution https://lnkd.in/eHhm7SKx

🌟 Exciting News Alert! 🌟 Just published a groundbreaking blog post on "MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing" on arXiv. The post delves into the critical advancement of Multimodal Large Language Models (MLLMs) and introduces MIKE, a comprehensive benchmark and dataset designed specifically for fine-grained multimodal entity knowledge editing. Our extensive evaluations show that current state-of-the-art methods face significant challenges in tackling this benchmark, emphasizing the need for novel approaches in this domain. Read the full post here: https://bit.ly/49O8aTy Don't miss out on the latest developments in this exciting field! Check out the blog post and join the conversation on the future of fine-grained multimodal entity knowledge editing.

[arXiv] Researchers introduce LongRAG, a system that enhances Retrieval-Augmented Generation (RAG) by integrating long-context Large Language Models (LLMs). LongRAG uses two key components: - A long retriever that works with larger chunks of text, significantly reducing the number of retrieved units. - A long reader that processes these extended text segments, leveraging the zero-shot answer extraction abilities of long-context LLMs. This approach reportedly achieves a 64.3% score on the HotpotQA (full-wiki) benchmark, matching state-of-the-art performance. https://lnkd.in/gCB-kdFu

Using advanced dimensionality reduction algorithms, we can visualize complex hidden patterns in language data. This approach reveals surprising similarities across diverse languages, suggesting universal structures in human communication. #LanguagePatterns #DimensionalityReduction #LinguisticUniversals #AIinLinguistics #DataVisualization

In the Hellaswag LLM benchmark evaluating common sense natural language inference, Danube performed with an accuracy of 69.58%, sitting just behind Stability AI’s Stable LM 2 1.6 billion parameter model. https://lnkd.in/grUr_cPM