Papers Date Understanding
“Date Understanding” 태그가 달린 논문 14편 · 필터 해제
Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives
Stroke is a major global health problem that causes mortality and morbidity. Predicting the outcomes of stroke intervention can facilitate clinical decision-making and improve patient care. Engaging and developing deep l…
Date UnderstandingDecision MakingDeep LearningEEG+2Instance-level quantitative saliency in multiple sclerosis lesion segmentation
In recent years, explainable methods for artificial intelligence (XAI) have tried to reveal and describe models' decision mechanisms in the case of classification tasks. However, XAI for semantic segmentation and in part…
Date UnderstandingLesion SegmentationSegmentationSemantic SegmentationUnderstanding the Weakness of Large Language Model Agents within a Complex Android Environment
Large language models (LLMs) have empowered intelligent agents to execute intricate tasks within domain-specific software such as browsers and games. However, when applied to general-purpose software systems like operati…
Date UnderstandingLanguage ModelingLanguage ModellingLarge Language ModelReGAL: Refactoring Programs to Discover Generalizable Abstractions
While large language models (LLMs) are increasingly being used for program synthesis, they lack the global view needed to develop useful abstractions; they generally predict programs one at a time, often repeating the sa…
Date UnderstandingMathMinecraftProgram SynthesisEchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning
Language models are achieving impressive performance on various tasks by aggressively adopting inference-time prompting techniques, such as zero-shot and few-shot prompting. In this work, we introduce EchoPrompt, a simpl…
Date UnderstandingGSM8KIn-Context LearningLearning to Execute+3A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this…
Date UnderstandingDomain AdaptationEarth ObservationSegmentation+2Dataset and Baseline System for Multi-lingual Extraction and Normalization of Temporal and Numerical Expressions
Temporal and numerical expression understanding is of great importance in many downstream Natural Language Processing (NLP) and Information Retrieval (IR) tasks. However, much previous work covers only a few sub-types an…
Date UnderstandingInformation RetrievalNERTimex normalizationBloombergGPT: A Large Language Model for Finance
The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering. Large Language Models (LLMs) have been shown…
Causal JudgmentCommon Sense ReasoningDate UnderstandingDisambiguation QA+25Complexity-Based Prompting for Multi-Step Reasoning
We study the task of prompting large-scale language models to perform multi-step reasoning. Existing work shows that when prompted with a chain of thoughts (CoT), sequences of short sentences describing intermediate reas…
Date UnderstandingGSM8KMathRetrievalLarge Language Models are Zero-Shot Reasoners
Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (…
Arithmetic ReasoningCommon Sense ReasoningDate UnderstandingFew-Shot Learning+3Training Compute-Optimal Large Language Models
We investigate the optimal model size and number of tokens for training a transformer language model under a given compute budget. We find that current large language models are significantly undertrained, a consequence …
AnachronismsAnalogical SimilarityAnalytic EntailmentCausal Judgment+69Entity Cloze By Date: Understanding what LMs know about unseen entities
Language models (LMs) are typically trained once on a large-scale corpus and used for years without being updated. Our world, however, is dynamic, and new entities constantly arise. We propose a framework to analyze what…
ArticlesDate UnderstandingScaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world. In this paper, we present an analysis o…
Abstract AlgebraAnachronismsAnalogical SimilarityAnalytic Entailment+143Japanese Beauty Marketing on Social Media: Critical Discourse Analysis Meets NLP
This project is a pilot study intending to combine traditional corpus linguistics, Natural Language Processing, critical discourse analysis, and digital humanities to gain an up-to-date understanding of how beauty is bei…
Date UnderstandingMarketing