Systematic and Controlled LLM-based Reasoning for Efficient Clinical Trial Retrieval
A scalable method that extends the capabilities of LLMs to systematize the reasoning over sets of medical eligibility criteria, enabling precise and interpretable clinical trial retrieval.
Quantitative Analysis of Language Model Usage and Trust in Academia: Insights from a Comprehensive Survey
The majority of students, staff, and faculty in academia actively use language models, and increased usage is positively correlated with higher levels of trust in these tools. Fact-checking is perceived as the most critical issue to prioritize for the responsible development of language models.
OpenAI's Potential Bankruptcy and the Looming Collapse of the AI Hype Bubble
The AI industry, led by OpenAI, is facing a potential collapse, which could have far-reaching consequences for the entire technology sector and the broader economy.
Detecting Hallucinations in Large Language Models: A Novel Approach
Detecting a specific subclass of hallucinations, termed confabulations, in large language models to address the problem of factually incorrect or irrelevant responses.
인공지능 열풍의 실체: 붕괴의 위험에 직면하고 있는가?
인공지능 기술의 발전에는 여러 가지 도전과제가 존재하며, 이를 해결하지 않으면 기술 발전이 더딜 수 있다.
Addressing Epistemic and Methodological Challenges in Empirical Machine Learning Research
Empirical research in machine learning faces significant epistemic and methodological challenges that undermine the reliability and replicability of research findings. A more comprehensive understanding of different types of empirical inquiry, including both exploratory and confirmatory approaches, is needed to improve the validity and impact of machine learning research.
SciDaSynth: An Interactive System for Efficient Extraction and Synthesis of Structured Knowledge from Scientific Literature
SciDaSynth, a novel interactive system powered by large language models, enables researchers to efficiently build structured knowledge bases from scientific literature at scale by automating data extraction and providing multi-level data exploration and refinement capabilities.
Leveraging Large Language Models as Simulated Patients to Enhance Clinical Medical Education
Large Language Models (LLMs) can be effectively leveraged as Virtual Simulated Patients (VSPs) to provide realistic clinical scenarios for student practice and enhance the quality of clinical medical education.
Evaluating the Performance and Efficiency of Large Language Models: Mistral 7B, 8x7B, 8x22B, and Mixtral 8x22B
Large language models with Mixture of Experts (MoE) architecture, such as Mixtral 8x7B and 8x22B, can achieve high performance while being more computationally efficient than monolithic models like Mistral 7B.
Flaws in Chen's Proposed Polynomial-Time Algorithm for the 2-MAXSAT Problem
Chen's proposed algorithm for solving the 2-MAXSAT problem in polynomial time contains multiple flaws and fails to provide a valid proof that P = NP.
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