https://github.com/vsubramaniam851/brain-multimodal
공식 코드Revealing Vision-Language Integration in the Brain with Multimodal Networks
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A language model is a model of natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval.
Large language models (LLMs), currently their most advanced form, are predominantly based on transformers trained on larger datasets (frequently using words scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.
Source: Wikipedia
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Revealing Vision-Language Integration in the Brain with Multimodal Networks
The Art of SOCRATIC QUESTIONING: Recursive Thinking with Large Language Models
Diversified in-domain synthesis with efficient fine-tuning for few-shot classification
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
CHAMPAGNE: Learning Real-world Conversation from Large-Scale Web Videos
GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models
Mark My Words: Analyzing and Evaluating Language Model Watermarks
Unifying Multimodal Large Language Model Capabilities and Modalities via Model Merging
WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models
Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models
Science Out of Its Ivory Tower: Improving Accessibility with Reinforcement Learning
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysis
Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation
Crisis Domain Adaptation Using Sequence-to-sequence Transformers
Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation
Revisiting Challenges in Data-to-Text Generation with Fact Grounding
LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning
Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration
CodeBC: A More Secure Large Language Model for Smart Contract Code Generation in Blockchain
Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering
Pedestrian Attribute Recognition via CLIP based Prompt Vision-Language Fusion
Dynamically Allocated Interval-Based Generative Linguistic Steganography with Roulette Wheel
Intent Representation Learning with Large Language Model for Recommendation
From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network
Prot2Chat: Protein LLM with Early-Fusion of Text, Sequence and Structure
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.