Papers Large Language Model
“Large Language Model” 태그가 달린 논문 6,097편 · 필터 해제
EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
Financial analysis presents complex challenges that could leverage large language model (LLM) capabilities. However, the scarcity of challenging financial datasets, particularly for Japanese financial data, impedes acade…
Binary ClassificationFinancial AnalysisFraud DetectionLarge Language ModelDeepForm: Reasoning Large Language Model for Communication System Formulation
Communication system formulation is critical for advancing 6G and future wireless technologies, yet it remains a complex, expertise-intensive task. While Large Language Models (LLMs) offer potential, existing general-pur…
Language ModelingLanguage ModellingLarge Language ModelReinforcement Learning (RL)From Pixels to Graphs: using Scene and Knowledge Graphs for HD-EPIC VQA Challenge
This report presents SceneNet and KnowledgeNet, our approaches developed for the HD-EPIC VQA Challenge 2025. SceneNet leverages scene graphs generated with a multi-modal large language model (MLLM) to capture fine-graine…
Knowledge GraphsLanguage ModelingLanguage ModellingLarge Language Model+1Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis
Medical ultrasonography is an essential imaging technique for examining superficial organs and tissues, including lymph nodes, breast, and thyroid. It employs high-frequency ultrasound waves to generate detailed images o…
Domain AdaptationLarge Language ModelSakugaFlow: A Stagewise Illustration Framework Emulating the Human Drawing Process and Providing Interactive Tutoring for Novice Drawing Skills
While current AI illustration tools can generate high-quality images from text prompts, they rarely reveal the step-by-step procedure that human artists follow. We present SakugaFlow, a four-stage pipeline that pairs dif…
AnatomyImage GenerationLanguage ModelingLanguage Modelling+1Efficient Fireworks Algorithm Equipped with an Explosion Mechanism based on Student's T-distribution
Many real-world problems can be transformed into optimization problems, which can be classified into convex and non-convex. Although convex problems are almost completely studied in theory, many related algorithms to man…
Large Language ModelSoK: Machine Unlearning for Large Language Models
Large language model (LLM) unlearning has become a critical topic in machine learning, aiming to eliminate the influence of specific training data or knowledge without retraining the model from scratch. A variety of tech…
Large Language ModelMachine UnlearningModel EditingPHRASED: Phrase Dictionary Biasing for Speech Translation
Phrases are essential to understand the core concepts in conversations. However, due to their rare occurrence in training data, correct translation of phrases is challenging in speech translation tasks. In this paper, we…
Language ModelingLanguage ModellingLarge Language ModelMultimodal Large Language Model+1The Predictive Brain: Neural Correlates of Word Expectancy Align with Large Language Model Prediction Probabilities
Predictive coding theory suggests that the brain continuously anticipates upcoming words to optimize language processing, but the neural mechanisms remain unclear, particularly in naturalistic speech. Here, we simultaneo…
EEGLanguage ModelingLanguage ModellingLarge Language ModelXGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation
Graph-based Retrieval-Augmented Generation (RAG) has shown great capability in enhancing Large Language Model (LLM)'s answer with an external knowledge base. Compared to traditional RAG, it introduces a graph as an inter…
graph constructionLanguage ModelingLanguage ModellingLarge Language Model+3Cognitive Weave: Synthesizing Abstracted Knowledge with a Spatio-Temporal Resonance Graph
The emergence of capable large language model (LLM) based agents necessitates memory architectures that transcend mere data storage, enabling continuous learning, nuanced reasoning, and dynamic adaptation. Current memory…
Large Language ModelQuestion AnsweringQA-LIGN: Aligning LLMs through Constitutionally Decomposed QA
Alignment of large language models with explicit principles (such as helpfulness, honesty, and harmlessness) is crucial for ensuring safe and reliable AI systems. However, standard reward-based alignment methods typicall…
Large Language ModelDecoupling the Image Perception and Multimodal Reasoning for Reasoning Segmentation with Digital Twin Representations
Reasoning Segmentation (RS) is a multimodal vision-text task that requires segmenting objects based on implicit text queries, demanding both precise visual perception and vision-text reasoning capabilities. Current RS ap…
Large Language ModelMultimodal ReasoningReasoning SegmentationEvent-Priori-Based Vision-Language Model for Efficient Visual Understanding
Large Language Model (LLM)-based Vision-Language Models (VLMs) have substantially extended the boundaries of visual understanding capabilities. However, their high computational demands hinder deployment on resource-cons…
Event-based visionLanguage ModelingLanguage ModellingLarge Language ModelStatistical Hypothesis Testing for Auditing Robustness in Language Models
Consider the problem of testing whether the outputs of a large language model (LLM) system change under an arbitrary intervention, such as an input perturbation or changing the model variant. We cannot simply compare two…
FairnessLarge Language ModelSemantic SimilaritySemantic Textual SimilarityHow Benchmark Prediction from Fewer Data Misses the Mark
Large language model (LLM) evaluation is increasingly costly, prompting interest in methods that speed up evaluation by shrinking benchmark datasets. Benchmark prediction (also called efficient LLM evaluation) aims to se…
Large Language ModelPredictionAn Intelligent Fault Self-Healing Mechanism for Cloud AI Systems via Integration of Large Language Models and Deep Reinforcement Learning
As the scale and complexity of cloud-based AI systems continue to increase, the detection and adaptive recovery of system faults have become the core challenges to ensure service reliability and continuity. In this paper…
Deep Reinforcement LearningLarge Language Modelreinforcement-learningReinforcement LearningMiniCPM4: Ultra-Efficient LLMs on End Devices
This paper introduces MiniCPM4, a highly efficient large language model (LLM) designed explicitly for end-side devices. We achieve this efficiency through systematic innovation in four key dimensions: model architecture,…
Large Language ModelLearning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions
Recent advances in large language model (LLM) reasoning have shown that sophisticated behaviors such as planning and self-reflection can emerge through reinforcement learning (RL). However, despite these successes, RL in…
Large Language ModelReinforcement Learning (RL)DeepVideo-R1: Video Reinforcement Fine-Tuning via Difficulty-aware Regressive GRPO
Recent works have demonstrated the effectiveness of reinforcement learning (RL)-based post-training in enhancing the reasoning capabilities of large language models (LLMs). In particular, Group Relative Policy Optimizati…
Data AugmentationLarge Language ModelReinforcement Learning (RL)