#model-evaluation
#model-evaluation

Artificial intelligence

Beyond Benchmarks: Really Evaluating AI

fromMaggieappleton

Humanity's Last Exam

Humanity's Last Exam is a new benchmark designed to provide a more rigorous measure of AI model capabilities compared to existing tests.

fromInfoWorld

3 days ago

Artificial intelligence

Why benchmarks are key to AI progress

fromMedium

Artificial intelligence

Beyond Benchmarks: Really Evaluating AI

fromMaggieappleton

Humanity's Last Exam

Humanity's Last Exam is a new benchmark designed to provide a more rigorous measure of AI model capabilities compared to existing tests.

Artificial intelligence

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

4 months ago

Artificial intelligence

When Smaller is Smarter: How Precision-Tuned AI Cracks Protein Mysteries | HackerNoon

Roam Research

Training AI to Understand Legal Texts in Different Domains | HackerNoon

Artificial intelligence

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

4 months ago

Artificial intelligence

When Smaller is Smarter: How Precision-Tuned AI Cracks Protein Mysteries | HackerNoon

Roam Research

Training AI to Understand Legal Texts in Different Domains | HackerNoon

more#machine-learning

#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.

11 months ago

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.

1 month 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

1 month 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

1 month 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.

2 months ago

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.

2 months ago

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

7 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.

#ai

fromTechzine Global

Artificial intelligence

New OpenAI models hallucinate more often than their predecessors

Software development

fromInfoQ

OpenAI Introduces Software Engineering Benchmark

SWE-Lancer benchmark assesses AI language models on real-world freelance software engineering tasks.

AI models face significant challenges in software engineering despite advancements.

fromTechzine Global

New OpenAI models hallucinate more often than their predecessors

OpenAI's newer reasoning models, o3 and o4-mini, .hallucinate more frequently than older models, posing challenges to AI accuracy.

Software development

fromInfoQ

OpenAI Introduces Software Engineering Benchmark

SWE-Lancer benchmark assesses AI language models on real-world freelance software engineering tasks.

AI models face significant challenges in software engineering despite advancements.

more#ai

fromTechCrunch

OpenAI partner says it had relatively little time to test the company's newest AI models | TechCrunch

This evaluation was conducted in a relatively short time, and we only tested the model with simple agent scaffolds. We expect higher performance [on benchmarks] is possible with more elicitation effort.

Artificial intelligence

fromWIRED

4 months ago

This Tool Probes Frontier AI Models for Lapses in Intelligence

Scale AI's new tool, Scale Evaluation, automates testing of AI models to identify weaknesses and improve performance effectively.

fromThe Atlantic

Chatbots Are Cheating on Their Benchmark Tests

AI companies are promoting a narrative of constant progress, but evidence suggests advances might be stalling.