Enhancing Trust in Large Language Models: Fine-Tuning for Calibrated Uncertainties in High-Stakes Applications
Large language models (LLMs) face a significant challenge in accurately representing uncertainty over the correctness of their output. This issue is critical for decision-making applications, particularly in fields like healthcare…
NVIDIA AI Introduces Nemotron-4 340B: A Family of Open Models that Developers can Use to Generate Synthetic Data for Training Large Language Models (LLMs)
NVIDIA has recently unveiled the Nemotron-4 340B, a groundbreaking family of models designed to generate synthetic data for training large language models (LLMs) across various commercial applications. This release marks…
Scaling AI Models: Combating Collapse with Reinforced Synthetic Data
As AI-generated data increasingly supplements or even replaces human-annotated data, concerns have arisen about the degradation in model performance when models are iteratively trained on synthetic data. Model collapse refers…
A New Google Study Presents Personal Health Large Language Model (Ph-Llm): A Version Of Gemini Fine-Tuned For Text Understanding Numerical Time-Series Personal Health Data
A wide variety of areas have demonstrated excellent performance for large language models (LLMs), which are flexible tools for language generation. The potential of these models in medical education, research,…
Lightski: An AI Startup that Lets You Embed ChatGPT Code Interpreter in Your App
These days, an embedded analytics solution can cost six figures. Users are never satisfied, regardless of how much effort is put in. They often express frustration with the complicated user…
Thread: A Jupyter Notebook that Combines the Experience of OpenAI’s Code Interpreter with the Familiar Development Environment of a Python Notebook
The digital age demands for automation and efficiency in the domain of software and applications. Automating repetitive coding tasks and reducing debugging time frees up programmers’ time for more strategic…
With 700,000 Large Language Models (LLMs) On Hugging Face Already, Where Is The Future of Artificial Intelligence AI Headed?
Large Language Models (LLMs) have taken over the Artificial Intelligence (AI) community in recent times. In a Reddit post, a user recently brought attention to the startling quantity of over…
Researchers from Stanford and Duolingo Demonstrate Effective Strategies for Generating at a Desired Proficiency Level Using Proprietary Models such as GPT4 and Open-Source Techniques
Controlling the language proficiency levels in texts generated by large language models (LLMs) is a significant challenge in AI research. Ensuring that generated content is appropriate for various proficiency levels…
This AI Paper from China Proposes a Novel dReLU-based Sparsification Method that Increases Model Sparsity to 90% while Maintaining Performance, Achieving a 2-5× Speedup in Inference
Large Language Models (LLMs) have made substantial progress in the field of Natural Language Processing (NLP). By scaling up the number of model parameters, LLMs show higher performance in tasks…
SelfGoal: An Artificial Intelligence AI Framework to Enhance an LLM-based Agent’s Capabilities to Achieve High-Level Goals
Large language models (LLMs) have enabled the creation of autonomous language agents capable of solving complex tasks in dynamic environments without task-specific training. However, these agents often face challenges when…