#llms

#llms

LLMs' accuracy is influenced by the number of distractors in prompts, with some models faring better than others in different reasoning scenarios.

AI accelerators are crucial for efficiently deploying Large Language Models (LLMs) at scale.

Active Inference AI is the future of autonomous intelligence, potentially displacing traditional deep learning and LLMs due to its adaptability and sustainability.

LLMs exhibit characteristics of both memorization and reasoning, with Chain-of-Thought prompting effective even with invalid examples.

Fine-tuning LLMs enhances task performance but may compromise their safety and increase vulnerabilities.

Understanding the trade-off between performance and security is critical in AI model development.

The demand for prompt engineers has significantly increased in the AI field due to the necessity to test and refine AI models, evolving from a niche role to a full-fledged job function.

Retrieval-augmented generation (RAG) enhances AI's responses by blending information retrieval with prompts for providing relevant, contextual data from external sources, improving accuracy in domain-specific knowledge.

Backslash Security adds upgrade simulation & LLM usage for DevSecOps teams, enhancing application security posture management.

AI has revolutionized brand strategy by leveraging LLMs to categorize content, cluster personas, and drive creative storytelling, thereby increasing share of voice and saving time.

AutoQuizzer tool generates quizzes from web pages using LLMs like LLaMa-3-8B. Easy to use, offers closed book exam mode, and provides quick quiz generation.

LLMs may have hallucinations and may not always lead to factuality.

AI models may be highly vulnerable to basic jailbreaks and generate harmful outputs unintentionally.

Vector databases use graph embeddings ideal for fuzzy match problems.

AI will likely increase cyber attacks' volume and impact in the next two years.

Gecko, a text embedding model by Google, excels in performance due to unique FRet dataset and LLM-reranking approach.

Reinforcement learning helps enhance AI models by incorporating human style and ethics outside traditional methods, like next-token prediction.

The launch of new LLMs for Nordic languages called Viking by Silo AI enhances Europe's linguistic diversity in the AI era.

Viking aims to bridge gaps in digital communication, cater to low-resource Nordic languages, and protect cultural heritage by offering unparalleled proficiency.

GPT Pilot automates coding tasks, Sourcery for PR reviews, Seaborn for data visualization.

Numbers Station Cloud in early access

Catalog is essential for semantics and data alignment

Unique approach to analyzing internal data using chat interface

AI platform developed with 3 versions of LLMs by ServiceNow, Hugging Face, and NVIDIA.

StarCoder2 LLMs trained on 619 programming languages, capable of generating high-quality code and leveraging large datasets.

Efficient adversarial attack phrases on LLMs developed by UMD computer scientists.

BEAST technique for fast adversarial attacks requires Nvidia RTX A6000 GPU and minimal processing time.

The Large Model Systems Organization (LMSys) has created Chatbot Arena, a platform for comparing large language models (LLMs) based on blind pairwise ratings.

Users can enter prompts and compare side-by-side responses from two randomly selected models.

JetBrains releases new versions of its IDEs with integrated AI Assistant.

AI Assistant stands out with its deep integration and support for multiple LLMs.

Observability is crucial for analyzing the inputs and outputs of LLMs and troubleshooting issues in production.

LLMs like GPT-4 can be limited by the information they were trained on, but frameworks like LangChain and LlamaIndex enable integration with external data sources.

A federal judge dismissed most of the lawsuit against Meta over AI model LLaMA, setting a high bar for what remains.

The legal framework for generative AI, specifically large language models (LLMs), is still undetermined.

News companies may have a harder time arguing copyright infringement for LLMs trained on freely available content.

Chess puzzles test logical reasoning of LLMs more effectively than traditional chess games.

LLMs present potential for email-writing support tool for dyslexia.

Search engines are transitioning to AI answer engines that directly provide answers without requiring users to click on links.

Aider enables pair programming with LLMs for code editing in local git repositories.

AI search systems vary in when they deliver LLM-aided results (ranging from on request to every time) and the extent of control users have over AI usage.