Unite.AI

DECEMBER 20, 2024

As the demand for generative AI grows, so does the hunger for high-quality data to train these systems. Scholarly publishers have started to monetize their research content to provide training data for large language models (LLMs).

Generative AI 130

Generative AI AI Modeling AI AI 130

Unite.AI

SEPTEMBER 12, 2024

Over the past decade, Artificial Intelligence (AI) has made significant advancements, leading to transformative changes across various industries, including healthcare and finance. In recent years, it has become increasingly evident that even the most advanced AI models are only as good as the data they are trained on.

Data Quality 157

Data Quality Data Scarcity AI AI 157

Marktechpost

DECEMBER 11, 2024

LG AI Research has released bilingual models expertizing in English and Korean based on EXAONE 3.5 The research team has expanded the EXAONE 3.5 models demonstrate exceptional performance and cost-efficiency, achieved through LG AI Research s innovative R&D methodologies. The EXAONE 3.5 model scored 70.2.

AI Research 85

AI Research AI Researcher Generative AI AI 85

Marktechpost

JANUARY 1, 2024

Researchers from Microsoft have introduced a novel approach to generate diverse, high-quality instruction data from open-source code, thereby improving the effectiveness of instruction tuning and the generalization ability of fine-tuned models. All credit for this research goes to the researchers of this project.

Large Language Models 126

Large Language Models Data Quality LLM AI Researcher 126

Marktechpost

MARCH 6, 2025

The research team introduced two model variants: Babel-9B, optimized for efficiency in inference and fine-tuning, and Babel-83B, which establishes a new benchmark in multilingual NLP. The researchers focused on optimizing data quality by implementing a rigorous pipeline that curates high-quality training datasets from multiple sources.

Large Language Models 67

Large Language Models LLM NLP Data Quality 67

Marktechpost

SEPTEMBER 30, 2023

The research community has introduced InternLM-20B, a groundbreaking 20 billion parameter pretrained model to address these challenges. InternLM-20B represents a significant leap forward in language model architecture and training data quality. All Credit For This Research Goes To the Researchers on This Project.

Artificial Intelligence 122

Artificial Intelligence Artificial Intelligence Natural Language Processing NLP 122

NVIDIA

JANUARY 6, 2025

With Cosmos added to the three-computer solution, developers gain a data flywheel that can turn thousands of human-driven miles into billions of virtually driven miles amplifying training data quality.

Robotics 122

Robotics Software Development Data Quality AI Modeling 122

Marktechpost

JUNE 27, 2023

In this paper, they investigate how the data quality might be improved along a different axis. Higher quality data produces better results; for instance, data cleaning is a crucial step in creating current datasets and can result in relatively smaller datasets or the ability to run the data through more iterations.

Large Language Models 122

Large Language Models Python Neural Network Data Quality 122

Marktechpost

APRIL 1, 2024

Addressing this challenge requires a solution that is scalable, versatile, and accessible to a wide range of users, from individual researchers to large teams working on the state-of-the-art side of AI development. Existing research emphasizes the significance of distributed processing and data quality control for enhancing LLMs.

Large Language Models 110

Large Language Models ETL Data Ingestion Data Quality 110

Flipboard

JUNE 22, 2023

Ask computer vision, machine learning, and data science questions : VoxelGPT is a comprehensive educational resource providing insights into fundamental concepts and solutions to common data quality issues. Check Out The Codes and Tool Page.

Computer Vision 138

Computer Vision Python Machine Learning AI Tools 138

Unite.AI

MAY 3, 2024

Essentially, anyone practising cognitive neuroscience research needs to have a strong grasp of research methodologies and a good understanding of how people think and behave. These two aspects are crucial and can be combined to develop and run high-quality AI research as well.

Data Quality 130

Data Quality AI Research AI Researcher AI Development 130

Pickl AI

DECEMBER 25, 2024

It integrates diverse, high-quality content from 22 sources, enabling robust AI research and development. Its accessibility and scalability make it essential for applications like text generation, summarisation, and domain-specific AI solutions. Its diverse content includes academic papers, web data, books, and code.

Large Language Models 52

Large Language Models Natural Language Processing AI Research AI Researcher 52

Marktechpost

DECEMBER 19, 2023

Filtering ensures data quality, excluding short problems or non-numeric content. All credit for this research goes to the researchers of this project. By fine-tuning a 1.3B generation model and a 1.3B verifier model on TinyGSM, the verifier selects optimal outputs from multiple candidates, enhancing model accuracy.

Python 130

Python Natural Language Processing Prompt Engineering Prompt Engineer 130

Marktechpost

OCTOBER 1, 2023

Additionally, they employ position-aware global tokens at every level to improve global data quality. All Credit For This Research Goes To the Researchers on This Project. This can lower the computational cost of self-attention in global information broadcasting. If you like our work, you will love our newsletter.

Convolutional Neural Networks 133

Convolutional Neural Networks Neural Network Data Quality AI Researcher 133

Marktechpost

NOVEMBER 5, 2023

They classify their analyses into four categories: Data statistics (e.g., Data quality (e.g., All Credit For This Research Goes To the Researchers on This Project. number of tokens and domain distribution). measuring duplicate documents and most frequent n-grams). Community- and society-relevant measurements (e.g.,

Big Data 132

Big Data Machine Learning Data Quality AI Researcher 132

Towards AI

DECEMBER 19, 2024

You might also enjoy the practical tutorials on building an AI research agent using Pydantic AI and the step-by-step guide on fine-tuning the PaliGemma2 model for object detection. Meme shared by ghost_in_the_machine TAI Curated section Article of the week Pydantic AI + Web Scraper + Llama 3.3 Meme of the week!

Data Ingestion 105

Data Ingestion Explainability AI Research AI Researcher 105

Marktechpost

DECEMBER 6, 2023

By focusing on the most valuable data, the model learns richer and more nuanced patterns, allowing it to perform better on unseen data and handle unexpected situations. We also need better ways to evaluate data quality and ensure efficient interaction between data selection and annotation.

Natural Language Processing 103

Natural Language Processing Deep Learning Artificial Intelligence Artificial Intelligence 103

Marktechpost

MAY 19, 2024

Google AI researchers describe their novel approach to addressing the challenge of generating high-quality synthetic datasets that preserve user privacy, which are essential for training predictive models without compromising sensitive information.

Machine Learning 111

Machine Learning Large Language Models LLM Data Quality 111

Marktechpost

NOVEMBER 10, 2023

Recent NLP research has focused on improving few-shot learning (FSL) methods in response to data insufficiency challenges. While these methods enhance model capabilities through architectural designs and pre-trained language models, data quality and quantity limitations persist. We are also on Telegram and WhatsApp.

BERT 126

BERT ChatGPT Large Language Models NLP 126

Flipboard

JULY 4, 2023

Advantages of vector databases Spatial Indexing – Vector databases use spatial indexing techniques like R-trees and Quad-trees to enable data retrieval based on geographical relationships, such as proximity and confinement, which makes vector databases better than other databases.

Large Language Models 138

Large Language Models Machine Learning Natural Language Processing BERT 138

Flipboard

JUNE 17, 2023

In particular, Tart achieves the necessary goals: • Task-neutral: Tart’s inference module must be trained once with fictitious data. Quality: Performs better than basic LLM across the board and closes the gap using task-specific fine-tuning techniques. Data-scalable: Handling 10 times as many instances as in-context learning.

LLM 143

LLM Large Language Models NLP Prompt Engineering 143

Marktechpost

NOVEMBER 5, 2023

More crucially, they include 40+ quality annotations — the result of multiple ML classifiers on data quality, minhash results that may be used for fuzzy deduplication, or heuristics. All Credit For This Research Goes To the Researchers on This Project. If you like our work, you will love our newsletter.

Large Language Models 126

Large Language Models LLM Categorization Machine Learning 126

AssemblyAI

AUGUST 3, 2023

Much of current AI research aims to design LLMs that seek helpful, truthful, and harmless behavior. While such studies are still missing a full view of the landscape, they suggest that focusing on the data quality might be way more beneficial than prioritizing scalability when fine-tuning LLMs.

LLM 238

LLM ChatGPT Chatbots OpenAI 238

O'Reilly Media

NOVEMBER 19, 2024

A generalized, unbundled workflow A more accountable approach to GraphRAG is to unbundle the process of knowledge graph construction, paying special attention to data quality. Going forward there’s a lot of room for “hybrid AI” approaches that blend the best of both, and GraphRAG is probably just the tip of the iceberg.

LLM 125

LLM NLP Hybrid AI Large Language Models 125

Unite.AI

MARCH 4, 2024

These extreme depictions create unrealistic expectations and unfounded fears, obscuring the nuanced reality of AI. The constant evolution of AI research and development introduces discoveries and innovations regularly.

AI 157

AI AI Algorithm Explainability 157

Marktechpost

NOVEMBER 27, 2023

However, there are several obstacles to overcome, especially when dealing with complex scenarios, because of the wide range of picture resolutions and the need for more training data quality. Furthermore, LLaVA is innovative in extending instruction-tuning into multimodal situations by fusing multimodal instruction-following data.

Artificial Intelligence 122

Artificial Intelligence Artificial Intelligence Data Quality AI 122

Marktechpost

NOVEMBER 19, 2023

If adding more data doesn’t improve model performance, it is redundant and doesn’t provide the models with any new information to learn. The study supports a growing body of knowledge among experts in AI across multiple domains: models trained on relatively small datasets can perform well, provided the data quality is high.

Machine Learning 101

Machine Learning Deep Learning Data Quality AI Researcher 101

ODSC - Open Data Science

JUNE 29, 2023

Let’s download the dataframe with: import pandas as pd df_target = pd.read_parquet("[link] /Listings/airbnb_listings_target.parquet") Let’s simulate a scenario where we want to assert the quality of a batch of production data. These constraints operate on top of statistical summaries of data, rather than on the raw data itself.

Natural Language Processing 104

Natural Language Processing Data Science Data Quality Data Scientist 104

Marktechpost

NOVEMBER 10, 2023

PERsD introduced a method for customizing labeled data to student model capacity, yielding more effective learning. PERsD outperformed standard distillation in code generation on HumanEval and MBPP datasets, benefiting from higher data quality, multi-round distillation, and self-rectification via execution feedback.

Large Language Models 99

Large Language Models LLM Data Quality AI 99

Marktechpost

DECEMBER 15, 2024

The navigator then evaluates the fidelity of these instructions, filtering out low-quality data to train a better generator in subsequent iterations. This iterative refinement ensures continuous improvement in both the data quality and the models’ performance. Trending: LG AI Research Releases EXAONE 3.5:

Data Scarcity 79

Data Scarcity Data Quality Automation AI 79

Marktechpost

DECEMBER 8, 2024

Structured data is important in this process, as it provides a clear and organized framework for the AI to learn from, unlike messy or unstructured data, which can lead to ambiguities. Employ Data Templates With data quality, implementing data templates offers another layer of control and precision.

Prompt Engineering 111

Prompt Engineering Prompt Engineer Large Language Models LLM 111

Marktechpost

AUGUST 10, 2024

High-Risk AI: These include critical applications like medical AI tools or recruitment software. They must meet strict standards for accuracy, security, and data quality, with ongoing human oversight. Content like deep fakes should be labeled to show it’s artificially made.

Artificial Intelligence 132

Artificial Intelligence Artificial Intelligence AI AI 132

Towards AI

DECEMBER 21, 2023

They explore the complexities and challenges of AI technology, focusing on Retrieval Augmented Generation (RAG), the importance of great documentation, and the potential of emerging multimodal models like Gemini. They also explore the technical aspects of chunking strategies and data quality in RAG systems.

AI 111

AI AI Machine Learning Data Scientist 111

Marktechpost

JANUARY 3, 2025

It maintains data quality through a TRM, by scoring synthesized trajectories along dimensions of coherence, logical flow, and completeness. Even partial but meaningful data can be trained in such an approach. All credit for this research goes to the researchers of this project.

LLM 82

LLM Data Quality Automation AI Researcher 82

Marktechpost

AUGUST 24, 2023

. • Since new training data is sampled from an improved policy during the Grow step, the quality of the policy is not constrained by the quality of the original dataset (unlike in offline RL). • All Credit For This Research Goes To the Researchers on This Project.

Algorithm 98

Algorithm Large Language Models Data Quality LLM 98

Towards AI

APRIL 2, 2024

What happened this week in AI by Louie The ongoing race between open and closed-source AI has been a key theme of debate for some time, as has the increasing concentration of AI research and investment into transformer-based models such as LLMs. comparable to much larger and more expensive models such as GPT-4.

LLM 103

LLM OpenAI Explainable AI AI 103

Marktechpost

DECEMBER 4, 2023

Key elements driving these developments are using the potent Large Language Model (LLM) as a text encoder, scaling up training datasets, increasing model complexity, better sampling strategy design, and improving data quality. All credit for this research goes to the researchers of this project.

Large Language Models 102

Large Language Models LLM Data Quality AI Researcher 102