Towards AI

OCTOBER 31, 2024

Image by author #3 Generate: Use of LLMs to generate sample data GenAI can also generate synthetic data to train AI models. Large Language Models (LLMs) can produce realistic sample data, helping address data scarcity in fields where data availability is limited.

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Data Quality Data Scarcity Automation Natural Language Processing 122

Marktechpost

JANUARY 28, 2025

Despite challenges such as data scarcity and computational demands, innovations like zero-shot learning and iterative optimization continue to push the boundaries of LLM capabilities. Individuals, AI researchers, etc., Individuals, AI researchers, etc.,

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Large Language Models LLM Data Scarcity Explainability 115

Marktechpost

SEPTEMBER 8, 2024

Large language models (LLMs) have revolutionized natural language processing (NLP), particularly for English and other data-rich languages. However, this rapid advancement has created a significant development gap for underrepresented languages, with Cantonese being a prime example.

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Large Language Models NLP Neural Network Data Scarcity 103

AWS Machine Learning Blog

DECEMBER 4, 2024

About the NVIDIA Nemotron model family At the forefront of the NVIDIA Nemotron model family is Nemotron-4, as stated by NVIDIA, it is a powerful multilingual large language model (LLM) trained on an impressive 8 trillion text tokens, specifically optimized for English, multilingual, and coding tasks.

Machine Learning 93

Machine Learning Large Language Models Data Scarcity Auto-complete 93

Unite.AI

MARCH 13, 2024

Introduction The field of natural language processing (NLP) and language models has experienced a remarkable transformation in recent years, propelled by the advent of powerful large language models (LLMs) like GPT-4, PaLM, and Llama.

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Natural Language Processing Large Language Models Data Scarcity LLM 204

Marktechpost

AUGUST 2, 2024

A major issue in RL is the data scarcity in embodied AI, where agents must interact with physical environments. This problem is exacerbated by the need for substantial reward-labeled data to train agents effectively. The large language model is the central controller, guiding the vision language and diffusion models.

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Machine Learning Data Scarcity Large Language Models Robotics 130

Marktechpost

FEBRUARY 27, 2024

For instance, BloomberGPT excels in finance with private financial data spanning 40 years. Collaborative training on decentralized personal data, without direct sharing, emerges as a critical approach to support the development of modern LLMs amid data scarcity and privacy concerns.

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Large Language Models Machine Learning Data Scarcity Algorithm 102

Marktechpost

MARCH 3, 2025

Encoder models like BERT and RoBERTa have long been cornerstones of natural language processing (NLP), powering tasks such as text classification, retrieval, and toxicity detection. Data Scarcity: Pre-training on small datasets (e.g., Wikipedia + BookCorpus) restricts knowledge diversity.

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BERT Data Scarcity Natural Language Processing Large Language Models 75

Marktechpost

AUGUST 3, 2023

Also, the limited number of available music-language datasets poses a challenge. With the scarcity of datasets, training a music captioning model successfully doesn’t remain easy. Large language models (LLMs) could be a potential solution for music caption generation. They opted for the powerful GPT-3.5

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Data Scarcity Large Language Models BERT Natural Language Processing 52

Marktechpost

JANUARY 15, 2024

One persistent challenge is the translation of low-resource languages, which often need more substantial data for training robust models. Traditional translation models, primarily based on large language models (LLMs), perform well with languages abundant in data but need help with underrepresented languages.

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Large Language Models Data Scarcity Computational Linguistics Natural Language Processing 130

Unite.AI

JULY 5, 2024

Large Language Models (LLMs) are powerful tools not just for generating human-like text, but also for creating high-quality synthetic data. This capability is changing how we approach AI development, particularly in scenarios where real-world data is scarce, expensive, or privacy-sensitive.

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Marktechpost

APRIL 15, 2024

The rapid advancement of large language models has ushered in a new era of natural language processing capabilities. However, a significant challenge persists: most of these models are primarily trained on a limited set of widely spoken languages, leaving a vast linguistic diversity unexplored.

Machine Learning 135

Machine Learning Data Scarcity Large Language Models Natural Language Processing 135

Marktechpost

JULY 8, 2024

Researchers from Cohere For AI have developed a novel, scalable method for generating high-quality multilingual feedback data. This method aims to balance data coverage and improve the performance of multilingual large language models (LLMs).

Data Scarcity 131

Data Scarcity Large Language Models Natural Language Processing NLP 131

John Snow Labs

OCTOBER 16, 2023

The recent NLP Summit served as a vibrant platform for experts to delve into the many opportunities and also challenges presented by large language models (LLMs). Implementation Hurdles: For these top performers, 24% see the models and tools as their primary challenge, followed by talent acquisition (20%) and scaling (19%).

Large Language Models 52

Large Language Models NLP Metadata Data Scarcity 52

Unite.AI

JANUARY 22, 2024

However, generating synthetic data for NLP is non-trivial, demanding high linguistic knowledge, creativity, and diversity. Different methods, such as rule-based and data-driven approaches, have been proposed to generate synthetic data.

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Marktechpost

MAY 28, 2024

Error correction models post-process ASR outputs, improving transcription accuracy by converting noisy hypotheses into clean text. Transformer-based error correction models have improved, especially with advanced WER-based metrics and noise augmentation strategies.

Data Scarcity 109

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Marktechpost

SEPTEMBER 2, 2023

On various Natural Language Processing (NLP) tasks, Large Language Models (LLMs) such as GPT-3.5 They optimize the LVLM using synthesized anomalous visual-textual data and incorporating IAD expertise. Direct training using IAD data, however, needs to be improved. Data scarcity is the first.

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Marktechpost

JULY 22, 2024

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has highlighted the critical need for large, diverse, and high-quality datasets to train and evaluate foundation models. OAK dataset offers a comprehensive resource for AI research, derived from Wikipedia’s main categories.

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AI Researcher AI Research Data Scarcity Prompt Engineering 122

Marktechpost

APRIL 4, 2024

However, the development and refinement of such technology have been hampered by the scarcity of high-quality, diverse Text-to-SQL training data. Gretel’s dataset is designed to fill the gap in training Large Language Models (LLMs) that are specialized in Text-to-SQL tasks.

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Marktechpost

OCTOBER 15, 2024

These models are trained on data collected from social media, which introduces bias and may not accurately represent diverse patient experiences. Moreover, privacy concerns and data scarcity hinder the development of robust models for mental health diagnosis and treatment.

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Marktechpost

DECEMBER 1, 2024

Simplified Synthetic Data Generation Designed to generate synthetic datasets using either local large language models (LLMs) or hosted models (OpenAI, Anthropic, Google Gemini, etc.), Promptwright makes synthetic data generation more accessible and flexible for developers and data scientists.

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Python LLM Data Scarcity Data Scientist 90

Marktechpost

JULY 25, 2024

Large language models (LLMs) show promise in solving high-school-level math problems using proof assistants, yet their performance still needs to improve due to data scarcity. Formal languages require significant expertise, resulting in limited corpora.

Automation 103

Automation Data Scarcity Large Language Models Data Extraction 103

Marktechpost

APRIL 17, 2024

In the rapidly evolving landscape of artificial intelligence (AI), the quest for large, diverse, and high-quality datasets represents a significant hurdle.

Data Scarcity 120

Data Scarcity Artificial Intelligence Artificial Intelligence AI Modeling 120

Marktechpost

FEBRUARY 29, 2024

Data scarcity in low-resource languages can be mitigated using word-to-word translations from high-resource languages. However, bilingual lexicons typically need more overlap with task data, leading to inadequate translation coverage. Check out the Paper.

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Topbots

DECEMBER 19, 2023

Generated with Midjourney The NeurIPS 2023 conference showcased a range of significant advancements in AI, with a particular focus on large language models (LLMs), reflecting current trends in AI research. Outstanding Papers Awards Are Emerged Abilities of Large Language Models a Mirage?

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Large Language Models Natural Language Processing Machine Learning AI Research 110

Marktechpost

OCTOBER 26, 2024

The model’s performance is evaluated using three distinct accuracy metrics: token-level accuracy for individual token assessment, sentence-level accuracy for evaluating coherent text segments, and response-level accuracy for overall output evaluation.

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Marktechpost

APRIL 5, 2024

.” Despite some research exploring the benefits and drawbacks of multilingual training and efforts to enhance models for smaller languages, most cutting-edge models still need to be primarily trained in large languages like English.

Data Scarcity 132

Data Scarcity AI Modeling AI AI 132

Marktechpost

JULY 18, 2024

The dataset’s open-domain nature allows for broad applications, from general sign language pretraining to medium-quality finetuning for specific tasks such as translation and caption alignment. In conclusion, YouTube-SL-25 is a pivotal advancement in sign language research, addressing the longstanding data scarcity issue.

Data Scarcity 120

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Marktechpost

JANUARY 15, 2025

The models architecture includes a vision encoder, vision adaptor, and a large language model , combined in a three-stage training process: Pre-training : A dataset of 40 million video-text pairs, enriched with commentary videos that capture both low-level actions and high-level plot details, provides a solid foundation for learning.

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Data Scarcity Large Language Models AI Researcher AI Research 84

Marktechpost

SEPTEMBER 15, 2024

Large Language Models (LLMs) have revolutionized natural language processing in recent years. The pre-train and fine-tune paradigm, exemplified by models like ELMo and BERT, has evolved into prompt-based reasoning used by the GPT family.

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Marktechpost

AUGUST 5, 2024

They use a three-stage training methodology—pretraining, ongoing training, and fine-tuning—to tackle the data scarcity of the SiST job. The team trains their model continuously using billions of tokens of low-quality synthetic speech translation data to further their goal of achieving modal alignment between voice and text.

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Marktechpost

DECEMBER 10, 2024

These days, large language models (LLMs) are getting integrated with multi-agent systems, where multiple intelligent agents collaborate to achieve a unified objective. By generating synthetic datasets, MAG-V reduces dependence on real customer data, addressing privacy concerns and data scarcity.

Machine Learning 87

Machine Learning Data Scarcity LLM Large Language Models 87

Marktechpost

JULY 29, 2023

Overall, the paper presents a significant contribution to the field by addressing the challenge of data scarcity for certain classes and enhancing the performance of CLIP fine-tuning methods using synthesized data. Check out the Paper. All Credit For This Research Goes To the Researchers on This Project.

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AI Researcher AI Research Generative AI Data Scarcity 98

Pickl AI

OCTOBER 9, 2024

Small Language Models (SLMs) are a subset of AI models specifically tailored for Natural Language Processing (NLP) tasks. They typically contain fewer parameters—ranging from tens to hundreds of millions—compared to Large Language Models (LLMs), which can have billions of parameters.

Natural Language Processing 52

Natural Language Processing Data Scarcity Large Language Models AI 52

Marktechpost

MAY 20, 2024

Because of this restriction, models trained on it may not be able to be extended to more general real-world scenarios. Acquiring high-quality data is difficult, and copyright constraints frequently hinder sharing it. Consequently, cutting-edge approaches to data scarcity and data augmentation should be the focus of future studies.

Data Scarcity 57

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AWS Machine Learning Blog

FEBRUARY 12, 2025

Many use cases involve using pre-trained large language models (LLMs) through approaches like Retrieval Augmented Generation (RAG). However, for advanced, domain-specific tasks or those requiring specific formats, model customization techniques such as fine-tuning are sometimes necessary.

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TheSequence

APRIL 14, 2024

📝 Editorial: Neuro-Symbolic Models are Making a Comeback Large language models (LLMs) have dominated the AI narrative in recent years to the point that we almost need to wonder about the future of other areas of machine learning.

Data Scarcity 52

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