This study focuses on fine-tuning Large Language Models (LLMs) for healthcare information in Vietnamese, a low-resource language, to improve medical information accessibility and healthcare communication in developing countries. The methodology involves selecting a base model (BloomZ-3B, LLaMA2–7B and LLaMA2–13B), compiling a domain-specific dataset of approximately 337,000 prompt-response pairs in Vietnamese from existing datasets, Vietnamese medical online forums, and medical textbooks, and fine-tuning the model using Low-Rank adaptation (LoRA) and Quantized Low-Rank adaptation (QLoRA) techniques. The fine-tuned models showed enhanced performance, demonstrating the potential to improve healthcare communication in low-resource languages and enhance data privacy and security.


📂 Paper: https://www.sciencedirect.com/science/article/pii/S0169260725000720/pdfft?md5=b348ebfecc8d8f8b481e23ec241da2de&pid=1-s2.0-S0169260725000720-main.pdf

@scopeofai
https://yangx.top/LLM_learning

Large Language Models (LLMs), such as GPT-4, have shown high accuracy in medical board exams, indicating their potential for clinical decision support. However, their metacognitive abilities—the ability to assess their own knowledge and manage uncertainty—are significantly lacking. This poses risks in medical applications where recognizing limitations and uncertainty is crucial.

To address this, researchers developed MetaMedQA, an enhanced benchmark that evaluates LLMs not just on accuracy but also on their ability to recognize unanswerable questions, manage uncertainty, and provide confidence scores. Testing revealed that while newer and larger models generally perform better in accuracy, most fail to handle uncertainty effectively and often give overconfident answers even when wrong


📁 Paper: https://www.nature.com/articles/s41467-024-55628-6.pdf

@scopeofai
@LLM_learning

The rapid advancement of large language models (LLMs), such as ChatGPT and GPT-4, has led to a surge in synthetic text generation across various domains, including journalism, academia, cybersecurity, and online discourse. While these models offer immense benefits, their ability to generate highly realistic text raises concerns regarding misinformation, academic dishonesty, and content authenticity. Consequently, the detection of LLM-generated content has become an essential area of research.

This survey provides a comprehensive overview of existing detection methodologies, benchmarks, and challenges, offering insights into the strengths and weaknesses of current techniques.The study aims to serve as a guiding reference for researchers and practitioners striving to uphold the integrity of digital information in an era dominated by synthetic content.

📁 Paper: https://arxiv.org/abs/2310.15654

@scopeofai
@LLM_learning

This repository is a curated collection of survey papers focused on Large Language Models (LLMs), organized to help researchers and practitioners navigate the rapidly evolving field. It compiles existing surveys across multiple topics, including foundational overviews of LLMs, technical aspects like Transformer architectures and efficient model design, and societal considerations such as alignment with human values, fairness, and safety. The repository also covers specialized areas like multimodal LLMs (handling text, images, etc.), knowledge-augmented models, and applications in education, healthcare, and law. Each section provides direct links to relevant papers (often on arXiv) and related GitHub repositories, emphasizing recent work from the past few years. the repository serves as a centralized resource for understanding both the technical advancements and ethical challenges of LLMs.

🔗 Repository: https://github.com/NiuTrans/ABigSurveyOfLLMs

@scopeofai
@LLM_learning

This essay explores whether contemporary Large Language Models (LLMs) can pass the Turing test, a benchmark proposed by Alan Turing to evaluate machine intelligence. The study involved evaluating four systems—GPT-4.5, LLaMa-3.1-405B, GPT-4o, and ELIZA—in randomized, controlled three-party Turing tests with two independent populations: UCSD undergraduate students and Prolific workers. Participants engaged in simultaneous conversations with a human and an AI system before judging which conversational partner they believed was human.

📁 Paper: https://arxiv.org/pdf/2503.23674


@scopeofai
@LLM_learning

Large Language Model Agent: A Survey on Methodology, Applications and Challenges


Paper: https://arxiv.org/pdf/2503.21460v1.pdf

Code: https://github.com/luo-junyu/awesome-agent-papers

@Machine_learn

SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators

📚 Read


@LLM_learning

Owen 3 release

📖 Blog


@LLM_learning

Recent explorations with commercial Large Language Models (LLMs) have shown that non-expert users can jailbreak LLMs by simply manipulating their prompts; resulting in degenerate output behavior, privacy and security breaches, offensive outputs, and violations of content regulator policies. Limited studies have been conducted to formalize and analyze these attacks and their mitigations. We bridge this gap by proposing a formalism and a taxonomy of known (and possible) jailbreaks. We survey existing jailbreak methods and their effectiveness on open-source and commercial LLMs (such as GPT-based models, OPT, BLOOM, and FLAN-T5-XXL). We further discuss the challenges of jailbreak detection in terms of their effectiveness against known attacks. For further analysis, we release a dataset of model outputs across 3700 jailbreak prompts over 4 tasks.

🗂 Paper: https://arxiv.org/pdf/2305.14965

@scopeofai
@LLM_learning

Deep-Live-Cam

Real time face swap and one-click video deepfake with only a single image

Creator: Hacksider
Stars ⭐️: 50,498
Forked by: 7,491

Github Repo:
https://github.com/hacksider/Deep-Live-Cam

@LLM_learning

With so many LLM papers being published, it's hard to keep up and compare results. This study introduces a semi-automated method that uses LLMs to extract and organize experimental results from arXiv papers into a structured dataset called LLMEvalDB. This process cuts manual effort by over 93%. It reproduces key findings from earlier studies and even uncovers new insights—like how in-context examples help with coding and multimodal tasks, but not so much with math reasoning. The dataset updates automatically, making it easier to track LLM performance over time and analyze trends.

📂 Paper: https://arxiv.org/pdf/2502.18791

▫️@scopeofai
▫️@LLM_learning