Microsoft Solves the Problem of LLM Data Scarcity
DECEMBER 16, 2024
Small models have shown promise over the last few months, and we are now finally getting to see what they are truly capable of thanks to Microsoft,
Boosting Classification Accuracy: Integrating Transfer Learning and Data Augmentation for Enhanced Machine Learning Performance
Marktechpost
JUNE 14, 2024
Together, these techniques mitigate the issues of limited target data, improving the model’s adaptability and accuracy. A recent paper published by a Chinese research team proposes a novel approach to combat data scarcity in classification tasks within target domains. Check out the Paper.
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UC Berkeley Research Presents a Machine Learning System that Can Forecast at Near Human Levels
Marktechpost
MARCH 5, 2024
However, judgmental forecasting has introduced a nuanced approach, leveraging human intuition, domain knowledge, and diverse information sources to predict future events under data scarcity and uncertainty. The challenge in predictive forecasting lies in its inherent complexity and the limitations of existing methodologies.
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Google DeepMind Researchers Introduce Diffusion Augmented Agents: A Machine Learning Framework for Efficient Exploration and Transfer Learning
Marktechpost
AUGUST 2, 2024
RL applications range from game playing to robotic control, making it essential for researchers to develop efficient and scalable learning methods. A major issue in RL is the data scarcity in embodied AI, where agents must interact with physical environments.
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Innovations in Analytics: Elevating Data Quality with GenAI
Towards AI
OCTOBER 31, 2024
By leveraging GenAI, we can streamline and automate data-cleaning processes: Clean data to use AI? Clean data through GenAI! Three ways to use GenAI for better data Improving data quality can make it easier to apply machine learning and AI to analytics projects and answer business questions.
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This AI Paper Proposes FLORA: A Novel Machine Learning Approach that Leverages Federated Learning and Parameter-Efficient Adapters to Train Visual-Language Models VLMs
Marktechpost
APRIL 27, 2024
Also, FLORA’s efficiency analysis shows that it uses much less memory and communication compared to baseline methods, which shows that it could be used in real-world federated learning situations. In conclusion, FLORA presents a promising solution to the challenge of training vision-language models in federated learning settings.
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Harnessing Machine Learning for Advanced Bioprocess Development: From Data-Driven Optimization to Real-Time Monitoring
Marktechpost
JUNE 19, 2024
Application of Machine Learning in Bioprocess Development: ML has profoundly impacted bioprocess development, particularly in strain selection and engineering stages. ML techniques like support vector machine (SVM) regression and Gaussian process (GP) regression predict optimal conditions for enzymatic activities and media composition.
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AI Researchers At Mayo Clinic Introduce A Machine Learning-Based Method For Leveraging Diffusion Models To Construct A Multitask Brain Tumor Inpainting Algorithm
Marktechpost
JULY 23, 2023
The number of AI and, in particular, machine learning (ML) publications related to medical imaging has increased dramatically in recent years. Data scarcity and data imbalance are two of these challenges. All Credit For This Research Goes To Researchers on This Project. Check out the paper, code and tool.
Full Guide on LLM Synthetic Data Generation
Unite.AI
JULY 5, 2024
This is particularly useful in scenarios where real-world data is limited or expensive to obtain. Challenges and Best Practices While LLM-driven synthetic data generation offers numerous benefits, it also comes with challenges: Quality Control : Ensure the generated data is of high quality and relevant to your use case.
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Amazon AI Research Introduces BioBRIDGE: A Parameter-Efficient Machine Learning Framework to Bridge Independently Trained Unimodal Foundation Models to Establish Multimodal Behavior
Marktechpost
FEBRUARY 28, 2024
By aligning the embedding space of unimodal FMs through cross-modal transformation models utilizing KG triplets, BioBRIDGE maintains data sufficiency and efficiency and navigates the challenges posed by computational costs and data scarcity that hinder the scalability of multimodal approaches.
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Can Machine Learning Evolve Beyond Public Data Limits? This Research from China Introduces OpenFedLLM: Pioneering Collaborative and Privacy-Preserving Training of Large Language Models Using Federated Learning
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.
Few-Shot Preference Optimization (FSPO): A Novel Machine Learning Framework Designed to Model Diverse Sub-Populations in Preference Datasets to Elicit Personalization in Language Models for Open-Ended Question Answering
Marktechpost
MARCH 4, 2025
Instead of relying on aggregated human feedback, FSPO reframes reward modeling as a meta-learning problem, enabling models to construct personalized reward functions. The approach generates over a million structured synthetic preferences to address data scarcity.
Open Artificial Knowledge (OAK) Dataset: A Large-Scale Resource for AI Research Derived from Wikipedia’s Main Categories
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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Meet Swin3D++: An Enhanced AI Architecture based on Swin3D for Efficient Pretraining on Multi-Source 3D Point Clouds
Marktechpost
MARCH 1, 2024
However, the scarcity and limited annotation of 3D data present significant challenges for the development and impact of 3D pretraining. One straightforward solution to address the data scarcity issue is to merge multiple existing 3D datasets and employ the combined data for universal 3D backbone pretraining.
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Researchers from Google DeepMind Introduce YouTube-SL-25: A Multilingual Corpus with Over 3,000 Hours of Sign Language Videos Covering 25+ Languages
Marktechpost
JULY 18, 2024
This field involves creating extensive datasets, developing sophisticated machine-learning models, and enhancing tools for translation and identification in various applications. This data bottleneck restricts the development of effective translation and interpretation tools, particularly for lesser-studied sign languages.
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Accurate RNA 3D structure prediction using a language model-based deep learning approach
NOVEMBER 20, 2024
Here we present RhoFold+, an RNA language model-based deep learning method that accurately predicts 3D structures of single-chain RNAs from sequences. million RNA sequences and leveraging techniques to address data scarcity, RhoFold+ offers a fully automated end-to-end pipeline for RNA 3D structure prediction.
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Distilabel: An Open-Source AI Framework for Synthetic Data and AI Feedback for Engineers with Reliable and Scalable Pipelines based on Verified Research Papers
Marktechpost
OCTOBER 11, 2024
In the rapidly evolving landscape of artificial intelligence, the quality and quantity of data play a pivotal role in determining the success of machine learning models. While real-world data provides a rich foundation for training, it often faces limitations such as scarcity, bias, and privacy concerns.
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Meet MaLA-500: A Novel Large Language Model Designed to Cover an Extensive Range of 534 Languages
Marktechpost
JANUARY 29, 2024
Other effective strategies to address data scarcity include vocabulary extension and ongoing pretraining. An important milestone was reached when the XLM-R auto-encoding model was introduced with 278M parameters with language coverage from 100 languages to 534 languages.
Data-Centric AI: The Importance of Systematically Engineering Training Data
Unite.AI
SEPTEMBER 12, 2024
Traditionally, AI research and development have focused on refining models, enhancing algorithms, optimizing architectures, and increasing computational power to advance the frontiers of machine learning. However, a noticeable shift is occurring in how experts approach AI development, centered around Data-Centric AI.
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The AI resource challenge: It’s infrastructure & compute, not data scarcity
FEBRUARY 14, 2023
Where would you look for a 2023 state of AI infrastructure analysis, if you really needed one? The answer should be obvious, of course, it’s Tel Aviv …
Stacklock Releases Promptwright: A Python Library for Synthetic Dataset Generation Using an LLM (Local or Hosted)
Marktechpost
DECEMBER 1, 2024
In the age of data-driven decision-making, access to high-quality and diverse datasets is crucial for training reliable machine learning models. However, acquiring such data often comes with numerous challenges, ranging from privacy concerns to the scarcity of domain-specific labeled samples.
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What AI Music Generators Can Do (And How They Do It)
AssemblyAI
SEPTEMBER 22, 2023
We’ll assume some general familiarity with machine learning concepts. Data scarcity: Paired natural anguage descriptions of music and corresponding music recordings are extremely scarce, in contrast to the abundance of image/descriptions pairs available online, e.g. in online art galleries or social media.
CMU Researchers Release Pangea-7B: A Fully Open Multimodal Large Language Models MLLMs for 39 Languages
Marktechpost
OCTOBER 22, 2024
The dataset was designed to address the major challenges of multilingual multimodal learning: data scarcity, cultural nuances, catastrophic forgetting, and evaluation complexity. Moreover, PANGEA matches or even outperforms proprietary models like Gemini-1.5-Pro
Innovation in Synthetic Data Generation: Building Foundation Models for Specific Languages
Unite.AI
JANUARY 22, 2024
Synthetic data , artificially generated to mimic real data, plays a crucial role in various applications, including machine learning , data analysis , testing, and privacy protection. However, generating synthetic data for NLP is non-trivial, demanding high linguistic knowledge, creativity, and diversity.
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Deep Learning Techniques for Autonomous Driving: An Overview
Marktechpost
MAY 8, 2024
Existing standards like ISO 26262 provide a framework, but adapting them for deep learning is complex. Deep learning introduces unique hazards and uncertainties, requiring new fault detection and mitigation approaches.
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From Noisy Hypotheses to Clean Text: How Denoising LM (DLM) Improves Speech Recognition Accuracy
Marktechpost
MAY 28, 2024
Significant advancements have been made in this field, driven by machine learning algorithms and large datasets. The DLM’s innovative use of synthetic data addresses the data scarcity issue that has hampered the performance of earlier error correction models.
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Meet LP-MusicCaps: A Tag-to-Pseudo Caption Generation Approach with Large Language Models to Address the Data Scarcity Issue in Automatic Music Captioning
Marktechpost
AUGUST 3, 2023
The post Meet LP-MusicCaps: A Tag-to-Pseudo Caption Generation Approach with Large Language Models to Address the Data Scarcity Issue in Automatic Music Captioning appeared first on MarkTechPost.
Splunk Researchers Introduce MAG-V: A Multi-Agent Framework For Synthetic Data Generation and Reliable AI Trajectory Verification
Marktechpost
DECEMBER 10, 2024
The framework introduces a novel approach combining classical machine-learning techniques with advanced LLM capabilities. Instead, it utilizes deterministic methods and machine-learning models to ensure accuracy and scalability in trajectory verification.
The new data frontier: Next-generation AI technologies and synthetic data
SAS Software
NOVEMBER 14, 2024
Data scarcity, privacy and bias are just a few reasons why synthetic data is becoming increasingly important. In this Q&A, Brett Wujek, Senior Manager of Product Strategy at SAS, explains why synthetic data will redefine data management and speed up the production of AI and machine learning models while cutting [.]
This AI Paper from Apple Unveils AlignInstruct: Pioneering Solutions for Unseen Languages and Low-Resource Challenges in Machine Translation
Marktechpost
JANUARY 15, 2024
Developed by researchers from Apple, aiming to enhance machine translation, AlignInstruct represents a paradigm shift in tackling data scarcity. It introduces a cross-lingual discriminator, crafted using statistical word alignments, to strengthen the machine translation process.
Award-Winning Breakthroughs at NeurIPS 2023: A Focus on Language Model Innovations
Topbots
DECEMBER 19, 2023
Privacy Auditing with One (1) Training Run By Thomas Steinke , Milad Nasr , and Matthew Jagielski from Google This research paper introduces a novel method for auditing differentially private (DP) machine learning systems using just a single training run. The paper also explores alternative strategies to mitigate data scarcity.
CRoP: A Context-wise Static Personalization Method for Robust and Scalable Human-Sensing AI Models in Healthcare and Real-World Scenarios
Marktechpost
SEPTEMBER 30, 2024
Human-sensing applications such as activity recognition, fall detection, and health monitoring have been revolutionized by advancements in artificial intelligence (AI) and machine learning technologies.
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This Paper Explores AI-Driven Hedging Strategies in Finance: A Deep Dive into the Use of Recurrent Neural Networks and k-Armed Bandit Models for Efficient Market Simulation and Risk Management
Marktechpost
DECEMBER 31, 2023
He highlighted the necessity for effective data use by stressing the significant amount of data many AI systems consume. Another researcher highlighted the challenge of considering AI model-free due to market data scarcity for training, particularly in realistic derivative markets.
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Joint Speech Transcription and Translation: Pseudo-Labeling with Out-of-Distribution Data
Machine Learning Research at Apple
MAY 10, 2023
Self-training has been shown to be helpful in addressing data scarcity for many domains, including vision, speech, and language. Specifically, self-training, or pseudo-labeling, labels unsupervised data and adds that to the training pool.
Fine-tune LLMs with synthetic data for context-based Q&A using Amazon Bedrock
AWS Machine Learning Blog
FEBRUARY 12, 2025
Although fine-tuning with a large amount of high-quality original data remains the ideal approach, our findings highlight the promising potential of synthetic data generation as a viable solution when dealing with data scarcity. Yiyue holds a Ph.D. Outside of work, she enjoys sports, hiking, and traveling.
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This AI Paper Proposes a Novel Bayesian Deep Learning Model with Kernel Dropout Designed to Enhance the Reliability of Predictions in Medical Text Classification Tasks
Marktechpost
APRIL 23, 2024
Unlike conventional methods, this approach utilizes Bayesian inference and Monte Carlo techniques to effectively manage uncertainty and data scarcity.
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Meet AnomalyGPT: A Novel IAD Approach Based on Large Vision-Language Models (LVLM) to Detect Industrial Anomalies
Marktechpost
SEPTEMBER 2, 2023
With just a few normal samples, AnomalyGPT can also learn in context, allowing for quick adjustment to new objects. 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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Advancing Cantonese NLP: Bridging Development Gaps in Large Language Models with New Benchmarks and Open-Source Innovations
Marktechpost
SEPTEMBER 8, 2024
Sentiment analysis has progressed from basic machine learning to advanced techniques using Hidden Markov Models and Transformers. Machine translation has evolved from rule-based systems to statistical and neural approaches, with recent focus on unsupervised methods and large-scale datasets.
Hypernetworks for Personalizing ASR to Atypical Speech
Machine Learning Research at Apple
JUNE 13, 2024
Even given this knowledge, data scarcity and high inter/intra-speaker variability further limit the effectiveness of traditional fine-tuning. However, these approaches assume a priori knowledge of the atypical speech disorder being adapted for -- the diagnosis of which requires expert knowledge that is not always available.
Meet Stochastic Flow Matching: An AI Framework Mapping Low-Resolution to Latent Space, Bridging High-Resolution Targets Effectively
Marktechpost
NOVEMBER 5, 2024
Small-scale atmospheric physics, including the intricate details of storm patterns, temperature gradients, and localized events, requires high-resolution data to be accurately represented. These finer details play an important role in applications ranging from daily weather forecasts to regional planning for disaster resilience.
Meta AI Researchers Introduce Token-Level Detective Reward Model (TLDR) to Provide Fine-Grained Annotations for Large Vision Language Models
Marktechpost
OCTOBER 26, 2024
To address data scarcity and granularity issues, the system employs sophisticated synthetic data generation techniques, particularly focusing on dense captioning and visual question-answering tasks.
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Zero-Shot Learning: Unlocking the Power of AI Without Training Data
Pickl AI
OCTOBER 21, 2024
As industries face the challenge of rapidly evolving data landscapes, ZSL offers a scalable solution that minimises the need for extensive labelling and retraining, making it an essential tool for modern AI applications. Traditional Machine Learning models require extensive labelled datasets for every class they need to predict.
A New AI Research from China Proposes SHIP: A Plug-and-Play Generative AI Approach to Improve Existing Fine-Tuning Methods
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.
Unlocking Deep Learning’s Potential with Multi-Task Learning
Pickl AI
MAY 29, 2024
Similarly, multi-task learning can simultaneously tackle multiple tasks like sentiment analysis, named entity recognition, and machine translation in Natural Language Processing, leading to more accurate and efficient language understanding systems. Also read: What is Information Retrieval in NLP? What is Tokenization in NLP?
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