#model-evaluation tag

Artificial intelligence

Beyond Benchmarks: Really Evaluating AI

fromInfoWorld

1 month ago

Artificial intelligence

Why benchmarks are key to AI progress

fromMedium

Artificial intelligence

Beyond Benchmarks: Really Evaluating AI

more#ai-benchmarks

Real-World Code Performance: Multi-Token Finetuning on CodeContests | HackerNoon

Models pretrained with different losses achieve different optimal temperatures for pass@k evaluation.

#pretraining-data

Artificial intelligence

AI Models Trained on Synthetic Data Still Follow Concept Frequency Trends | HackerNoon

Artificial intelligence

'Let It Wag!' and the Limits of Machine Learning on Rare Concepts | HackerNoon

Artificial intelligence

AI Models Trained on Synthetic Data Still Follow Concept Frequency Trends | HackerNoon

Artificial intelligence

'Let It Wag!' and the Limits of Machine Learning on Rare Concepts | HackerNoon

more#pretraining-data

AI Training Data Has a Long-Tail Problem | HackerNoon

Pretraining datasets exhibit a long-tailed distribution of concept frequencies, impacting performance disparities.

Data science

2 years ago

Deep Dive into MS MARCO Web Search: Unpacking Dataset Characteristics | HackerNoon

The MS MARCO dataset reveals considerable multilingual disparity and significant data skew, highlighting challenges in model evaluation and training.

Evaluating Multimodal Speech Models Across Diverse Audio Tasks | HackerNoon

The study leverages diverse speech datasets to evaluate model performance across various speech tasks and improve generalization capabilities.

3 months ago

AI Learns Common Sense from Touch, Not Just Vision | HackerNoon

Model size significantly impacts physical understanding accuracy in task performance for OCTOPI.

Utilizing physical property descriptions enhances the performance of language models in complex understanding tasks.

Data science

3 months ago

The Future of Remote Sensing: Few-Shot Learning and Explainable AI | HackerNoon

Few-shot learning techniques for remote sensing enhance model efficiency with limited data, emphasizing the need for explainable AI.

fromhackernoon.com

3 months ago

Limited Gains: Multi-Token Training on Natural Language Choice Tasks

Multi-token prediction enhances model performance in natural language processing benchmarks.

Larger models lead to improved scalability and faster inference times.

Behind the Scenes: The Prompts and Tricks That Made Many-Shot ICL Work | HackerNoon

GPT4(V)-Turbo demonstrates variable performance in many-shot ICL, with notable failures to scale effectively under certain conditions.

How Chameleon Advances Multimodal AI with Unified Tokens | HackerNoon

Chameleon enhances multimodal learning through seamless integration of text and image tokens in a unified token space.

Comparing Chameleon AI to Leading Image-to-Text Models | HackerNoon

In evaluating Chameleon, we focus on tasks requiring text generation conditioned on images, particularly image captioning and visual question-answering, with results grouped by task specificity.

Artificial intelligence

Bootstrapping

9 months ago

How Many Glitch Tokens Hide in Popular LLMs? Revelations from Large-Scale Testing | HackerNoon

The study reveals that simple indicators can effectively detect under-trained tokens in language models, improving token prediction accuracy.