Unite.AI

FEBRUARY 7, 2024

However, LLMs are also very different from other models. They are huge, complex, and data-hungry. They also need a lot of data to learn from, which can raise data quality, privacy, and ethics issues. Moreover, LLMs can generate inaccurate, biased, or harmful outputs, which need careful evaluation and moderation.

Machine Learning 288

Machine Learning Large Language Models LLM BERT 288

AWS Machine Learning Blog

MARCH 18, 2025

This transcription then serves as the input for a powerful LLM, which draws upon its vast knowledge base to provide personalized, context-aware responses tailored to your specific situation. LLM integration The preprocessed text is fed into a powerful LLM tailored for the healthcare and life sciences (HCLS) domain.

LLM 102

LLM NLP Data Integration AI 102

Snorkel AI

MARCH 4, 2025

Misaligned LLMs can generate harmful, unhelpful, or downright nonsensical responsesposing risks to both users and organizations. This is where LLM alignment techniques come in. LLM alignment techniques come in three major varieties: Prompt engineering that explicitly tells the model how to behave.

LLM 52

LLM Large Language Models Data Quality Prompt Engineer 52

Unite.AI

JULY 19, 2024

To deal with this issue, various tools have been developed to detect and correct LLM inaccuracies. Pythia Image source Pythia uses a powerful knowledge graph and a network of interconnected information to verify the factual accuracy and coherence of LLM outputs. Automatically detects mislabeled data. Enhances data quality.

LLM 274

LLM AI AI Data Quality 274

Unite.AI

MARCH 10, 2025

Cost-efficient Large Language Models (LLM) Accelerate AI Adoption Businesses leveraging this new generation of AI models are positioned to scale innovation more effectively while optimizing costs. A robust data strategy should assess data quality, infrastructure readiness and access to advanced technologies.

AI Modeling 129

AI Modeling Generative AI AI Strategy Data Quality 129

Towards AI

OCTOBER 31, 2024

As we wrap up October, we’ve compiled a bunch of diverse resources for you — from the latest developments in generative AI to tips for fine-tuning your LLM workflows, from building your own NotebookLM clone to instruction tuning. We have long supported RAG as one of the most practical ways to make LLMs more reliable and customizable.

LLM 116

LLM NLP BERT Large Language Models 116

Unite.AI

MARCH 14, 2025

Add in common issues like poor data quality, scalability limits, and integration headaches, and its easy to see why so many GenAI PoCs fail to move forward. Use techniques like LLM-as-a-judge or LLM-as-Juries to automate (semi-automate) evaluation.

Automation 111

Automation Data Quality LLM Generative AI 111

Marktechpost

MAY 20, 2024

License, is an innovative open-source platform designed to facilitate and accelerate the development of Large Language Model (LLM) applications. Business users can leverage pre-configured application templates and intuitive form-filling processes to build intelligent applications centered around LLM swiftly.

DevOps 106

DevOps LLM Large Language Models Data Quality 106

Unite.AI

FEBRUARY 6, 2025

But it means that companies must overcome the challenges experienced so far in GenAII projects, including: Poor data quality: GenAI ends up only being as good as the data it uses, and many companies still dont trust their data. Copilots are usually built using RAG pipelines. RAG is the Way. Prediction 4. Prediction 5.

LLM 111

LLM Automation Data Quality Prompt Engineer 111

Analytics Vidhya

JANUARY 2, 2024

This innovative technique aims to generate diverse and high-quality instruction data, addressing challenges associated with duplicate data and limited control over data quality in existing methods.

AI Chatbots 305

AI Chatbots Data Quality Chatbots Large Language Models 305

AWS Machine Learning Blog

NOVEMBER 26, 2024

This data makes sure models are being trained smoothly and reliably. If failures increase, it may signal issues with data quality, model configurations, or resource limitations that need to be addressed. Execution status – You can monitor the progress of training jobs, including completed tasks and failed runs.

LLM 98

LLM ML Large Language Models Deep Learning 98

Unite.AI

JANUARY 30, 2025

This platform unifies the experience of both LLM-based generative AI and business applications for technical and non-technical users around shared context. It eliminates the need for specialized data scientists and provides complete transparency in mapping and reasoning through web, Slack, or Teams interfaces.

Automation 144

Automation Metadata Explainability Data Scientist 144

IBM Journey to AI blog

SEPTEMBER 3, 2024

Hay argues that part of the problem is that the media often conflates gen AI with a narrower application of LLM-powered chatbots such as ChatGPT, which might indeed not be equipped to solve every problem that enterprises face. In this context, data quality often outweighs quantity.

Generative AI 308

Generative AI LLM Large Language Models AI 308

IBM Journey to AI blog

JANUARY 9, 2024

Currently, no standardized process exists for overcoming data ingestion’s challenges, but the model’s accuracy depends on it. Challenges in rectifying biased data: If the data is biased from the beginning, “ the only way to retroactively remove a portion of that data is by retraining the algorithm from scratch.”

Data Ingestion 222

Data Ingestion Data Integration Data Quality LLM 222

Towards AI

FEBRUARY 17, 2025

legal document review) It excels in tasks that require specialised terminologies or brand-specific responses but needs a lot of computational resources and may become obsolete with new data. For instance, a medical LLM fine-tuned on clinical notes can make more accurate recommendations because it understands niche medical terminology.

Data Drift 95

Data Drift LLM Automation Metadata 95

ODSC - Open Data Science

JULY 7, 2023

So despite phi-1’s smaller size, it outperforms its larger competitors and is able to demonstrate the potential of high-quality data in optimizing LLM performance. The paper also dives into the enhancement of data quality. This was most notable when it came to data cleaning.

LLM 105

LLM Python Large Language Models Data Science 105

Marktechpost

JULY 7, 2024

These issues underscore the need for continued development of diverse benchmarks to assess LLM reliability and identify potential fairness concerns. The benchmark incorporates 11 diverse indicators for approximately 200 countries, generating 2,225 questions per LLM. times higher than North America.

LLM 107

LLM Large Language Models Categorization Automation 107

O'Reilly Media

NOVEMBER 19, 2024

Also, in place of expensive retraining or fine-tuning for an LLM, this approach allows for quick data updates at low cost. When a question gets asked, run its text through this same embedding model, determine which chunks are nearest neighbors , then present these chunks as a ranked list to the LLM to generate a response.

LLM 125

LLM NLP Hybrid AI Large Language Models 125

Marktechpost

JULY 2, 2024

Existing methods for moderating LLM interactions include tools like Llama-Guard and various other open-source moderation models. WILDGUARDTEST is a high-quality, human-annotated evaluation set with 5,299 items. These tools typically focus on detecting harmful content and assessing safety in model responses.

LLM 106

LLM Data Quality ML Large Language Models 106

Unite.AI

DECEMBER 6, 2024

When framed in the context of the Intelligent Economy RAG flows are enabling access to information in ways that facilitate the human experience, saving time by automating and filtering data and information output that would otherwise require significant manual effort and time to be created.

AI 130

AI AI Data Platform LLM 130

Marktechpost

JULY 2, 2024

Key considerations for data in a GenAI workflow include: Quality: High-quality data is clean, accurate, and relevant. Data validation and preprocessing are critical steps in ensuring data quality. Quantity: Large volumes of data enable AI models to learn effectively.

LLM 111

LLM Generative AI Large Language Models AI 111

Towards AI

MAY 20, 2023

Challenges of building custom LLMs Building custom Large Language Models (LLMs) presents an array of challenges to organizations that can be broadly categorized under data, technical, ethical, and resource-related issues. Ensuring data quality during collection is also important.

LLM 98

LLM Large Language Models Chatbots Natural Language Processing 98

Unite.AI

OCTOBER 16, 2023

This is where LLMOps steps in, embodying a set of best practices, tools, and processes to ensure the reliable, secure, and efficient operation of LLMs. Custom LLM Training : Developing a LLM from scratch promises an unparalleled accuracy tailored to the task at hand.

Large Language Models 184

Large Language Models LLM Machine Learning Neural Network 184

Marktechpost

DECEMBER 3, 2024

As generative AI continues to grow, the need for an efficient, automated solution to transform various data types into an LLM-ready format has become even more apparent. Meet MegaParse : an open-source tool for parsing various types of documents for LLM ingestion. Check out the GitHub Page.

LLM 102

LLM AI Tools Large Language Models Data Ingestion 102

Snorkel AI

DECEMBER 2, 2024

The integration between the Snorkel Flow AI data development platform and AWS’s robust AI infrastructure empowers enterprises to streamline LLM evaluation and fine-tuning, transforming raw data into actionable insights and competitive advantages. Here’s what that looks like in practice.

Data Ingestion 52

Data Ingestion Large Language Models LLM Machine Learning 52

Marktechpost

JUNE 11, 2024

Federated learning (FL) has emerged as a promising solution, enabling collaborative training of LLMs on decentralized data while preserving privacy (FedLLM). Current works construct artificial FL datasets by partitioning centralized datasets, failing to capture properties of real-world cross-user data.

Large Language Models 126

Large Language Models Data Quality LLM ML 126

AssemblyAI

AUGUST 3, 2023

This piece should be helpful to anyone who wants a better understanding of LLMs and the challenges in making them safe and reliable. While some familiarity with LLM terminology will be beneficial, we have aimed to make this article accessible to a broad audience. One way to think about it is the following.

LLM 238

LLM ChatGPT Chatbots OpenAI 238

Marktechpost

MAY 7, 2024

The burgeoning expansion of the data landscape, propelled by the Internet of Things (IoT), presents a pressing challenge: ensuring data quality amidst the deluge of information. However, the quality of that data is paramount, especially given the escalating reliance on Machine Learning (ML) across various industries.

Large Language Models 132

Large Language Models Automation Data Quality ML 132

AI Weekly

MAY 30, 2024

arxiv.org Sponsor Need Data to Train AI? [Download now] rws.com In The News OpenAI forms safety council as it trains latest AI model OpenAI says it is setting up a safety and security committee and has begun training a new AI model to supplant the GPT-4 system that underpins its ChatGPT chatbot.

Robotics 261

Robotics AI AI Artificial Intelligence 261

Unite.AI

FEBRUARY 17, 2025

Data Quality, Quantity, and Integration: As AI models require large amounts of high-quality data to perform effectively, enterprises must implement robust data collection and processing pipelines to ensure the AI is receiving current, accurate, relevant data.

Automation 144

Automation Responsible AI Generative AI AI Modeling 144

AWS Machine Learning Blog

NOVEMBER 1, 2024

Fine-tuning is a powerful approach in natural language processing (NLP) and generative AI , allowing businesses to tailor pre-trained large language models (LLMs) for specific tasks. By fine-tuning, the LLM can adapt its knowledge base to specific data and tasks, resulting in enhanced task-specific capabilities.

LLM 90

LLM Prompt Engineer Prompt Engineering Generative AI 90

Marktechpost

JANUARY 1, 2024

Thereby, it addresses the challenges in instruction data generation, such as duplicate data and insufficient control over data quality. The goal is to augment the performance of Code LLMs through instruction tuning.

Large Language Models 126

Large Language Models Data Quality LLM AI Research 126

NVIDIA

JUNE 14, 2024

NVIDIA today announced Nemotron-4 340B, a family of open models that developers can use to generate synthetic data for training large language models (LLMs) for commercial applications across healthcare, finance, manufacturing, retail and every other industry. Nemotron-4 340B can be downloaded now from Hugging Face.

Large Language Models 145

Large Language Models LLM Data Quality Generative AI 145

AWS Machine Learning Blog

DECEMBER 4, 2024

This framework creates a central hub for feature management and governance with enterprise feature store capabilities, making it straightforward to observe the data lineage for each feature pipeline, monitor data quality , and reuse features across multiple models and teams.

ML 86

ML Machine Learning Generative AI AI 86

Marktechpost

JANUARY 12, 2024

The team has introduced the DeepSeek LLM project, which is a long-term focused initiative to advance open-source language models guided by the established scaling rules. Upon evaluation, the team has shared that DeepSeek LLM 67B is a lot effective. Upon evaluation, the team has shared that DeepSeek LLM 67B is a lot effective.

Large Language Models 111

Large Language Models AI Modeling Data Quality LLM 111

IBM Journey to AI blog

AUGUST 23, 2023

This data governance requires us to understand the origin, sensitivity, and lifecycle of all the data that we use. Risks of training LLM models on sensitive data Large language models can be trained on proprietary data to fulfill specific enterprise use cases.

Large Language Models 167

Large Language Models Data Discovery LLM AI 167

Marktechpost

JUNE 10, 2024

For instance, an LLM might incorrectly state that Charles Lindbergh was the first to walk on the moon instead of Neil Armstrong. Mitigation Strategies Various strategies have been developed to address hallucinations, improve data quality, enhance training processes, and refine decoding methods.

Large Language Models 141

Large Language Models Categorization Data Quality Natural Language Processing 141