Oz ??alan的动态

Unveiling the Geometric Insights of Large Language Models by Tenyx The research from Tenyx delves into the geometric analysis of large language models (LLMs), focusing on their reasoning capabilities. It highlights that LLMs have shown significant advancements in various tasks, with reasoning being a pivotal area for development. Enhancing reasoning often involves increasing model size and context length through techniques like chain of thought, retrieval augmented generation, and example-based prompting. These methods, though effective, also escalate computational costs and inference latency in practical applications. https://is.gd/bVE9iZ #ai #aitechnology #artificialintelligence #llm #machinelearning #tenyx

Research Finds Large Language Models Exhibit Bias Yet Remain Valuable for Complex Data Analysis The integration of large language models (LLMs) into qualitative research methodologies signifies a significant advancement in analytical capabilities. By aligning their outputs with human perspectives and enhancing reflexivity through tools like the AI Sub Zero Bias cards, LLMs empower researchers to navigate complex and controversial subjects with greater insight and nuance. https://is.gd/nnJReg #ai #aitechnology #artificialintelligence #dataanalysis #llm #machinelearning

University of Chicago study finds GPT-4 outperforms human analysts in predicting corporate profits A recent study conducted by the University of Chicago has unveiled a remarkable revelation: large language models (LLMs), particularly exemplified by GPT-4, have surpassed human analysts in the realm of predicting corporate profits. This groundbreaking research, outlined in a working paper titled “Analyzing Financial Statements with Large Language Models,” has profound implications for the trajectory of financial analysis and decision-making, according to Venture Beat. https://is.gd/csqKAM #AI #artificialintelligence #financialanalysis #FinancialServices #GPT4 #llm #machinelearning

Excited to enhance AI reliability? Join us at WashU's workshop on reducing hallucinations in large language models with professor and speaker Ruopeng An on April 23. - Learn to spot and fix non-existent citations, factual mistakes, and misinterpretations - Get proven strategies to minimize AI hallucinations, (boosting trust in generated content) - Dive into practical techniques with hands-on case studies, from prompt engineering to fact-checking This workshop is tailored for both faculty and students, offering essential skills to enhance AI content reliability. Learn more and register here: https://lnkd.in/gTkU4Amh #AI #Research #WashU #Workshop #AIreliability

Humans are here to stay. And we’re pretty intelligent, capable and complex machines. Knowing what makes something relevant or not requires an extremely high level of intelligence. Read about our research into the limitations of Large Language Models. #generativeai #LLMs #linguisticengineering

Compelling insights from the paper "FLAME: Factuality-Aware Alignment for Large Language Models," authored by Sheng-Chieh Lin, Luyu Gao, Barlas Oguz, and their colleagues from the University of Waterloo, Carnegie Mellon University, and Meta AI. This research addresses a crucial gap in AI development: enhancing the factuality of AI responses. Why This Research is Crucial: ?? Addressing AI Hallucinations: The conventional training processes often lead AI to generate more false facts, known as hallucinations. FLAME introduces a factuality-aware alignment that significantly reduces these inaccuracies. ?? Innovative Training Techniques: By implementing factuality-aware supervised fine-tuning and reinforcement learning, FLAME guides language models to produce more accurate and truthful responses, adhering closely to factual correctness. Key Insights: ?? Enhanced Factuality with Direct Preference Optimization: FLAME's approach incorporates direct preference optimization (DPO) to prioritize factuality, ensuring that the AI's output remains within the bounds of verified information. ?? Balancing Factuality and Helpfulness: Despite focusing on factuality, FLAME maintains the model's ability to follow instructions effectively, demonstrating that you don't have to sacrifice helpfulness to achieve truthfulness. #analyticsvidhya #datascience #generativeai

Unpacking the Influence of Retrieval Augmented Generation (RAG) on Language Models: A Mechanistic Analysis Researchers from Microsoft, the University of Massachusetts Amherst, and the University of Maryland, College Park, delve into the impact of Retrieval Augmented Generation (RAG) on language models (LMs), specifically examining how it influences reasoning and factual accuracy. Their study investigates whether LMs increasingly rely on external RAG context rather than their parametric memory when generating responses to factual queries. https://is.gd/kJqgxa #AI #artificialintelligence #llm #machinelearning #retrievalaugmentedgeneration

A novel online evaluation protocol for test time adaptation (TTA) methods? A novel task-centric angle for the pre-trained weights of large language models (LLMs)? A general form of dynamic convolution that redefines the basic concepts of kernels? These are but a few of the innovative research topics being presented by Intel Labs researchers at this year's ICML. Learn more about our event activities in this blog. https://intel.ly/3YfWbvA #ICML #ML #AI #Research

University of Auckland Unveils ChatLogic: A Breakthrough in Enhancing Multi-Step Reasoning for Large Language Models Researchers from the The University of Auckland have introduced ChatLogic, a pioneering framework designed to enhance the multi-step reasoning capabilities of large language models (LLMs), marking a significant breakthrough in AI technology. This innovative system addresses a critical limitation in current LLMs, which often struggle with complex deductive tasks that require coherent and logical multi-step reasoning. https://is.gd/INJ5sf #ai #aitechnology #artificialintelligence #chatlogic #llm #machine learning #universityofauckland

?? Exploring Contextual Hallucinations in LLMs! Keeping my strategy to life long learning, I am sharing insights from a recent study on mitigating contextual hallucinations in large language models (LLMs). The paper introduces the "Lookback Lens," a lightweight classifier leveraging attention maps to detect and reduce hallucinations. Key Contributions: - Utilizes attention weights to identify hallucinations. - Effective across different models and tasks without retraining. - Reduces hallucinations in summarization tasks by 9.6%. Strengths: - Simple yet effective approach. - Generalizable across models. - Significant reduction in hallucinations. Weaknesses: - Limited by the sampling capabilities of LLMs. - Increased inference time due to candidate sampling. Read The full paper. #AI #MachineLearning #NaturalLanguageProcessing #Research #Innovation