https://github.com/FeiLiu36/LLM4MOEA
공식 코드Large Language Model for Multi-objective Evolutionary Optimization
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Large Language Model for Multi-objective Evolutionary Optimization
Efficient Prompt Tuning of Large Vision-Language Model for Fine-Grained Ship Classification
Diagnosing our datasets: How does my language model learn clinical information?
HonestLLM: Toward an Honest and Helpful Large Language Model
KM-BART: Knowledge Enhanced Multimodal BART for Visual Commonsense Generation
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
RNNs as psycholinguistic subjects: Syntactic state and grammatical dependency
Learning diverse attacks on large language models for robust red-teaming and safety tuning
Detection-Correction Structure via General Language Model for Grammatical Error Correction
Superscopes: Amplifying Internal Feature Representations for Language Model Interpretation
BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
CODER: Knowledge infused cross-lingual medical term embedding for term normalization
Improving Biomedical Pretrained Language Models with Knowledge
Expansion via Prediction of Importance with Contextualization
Avoiding Forgetting and Allowing Forward Transfer in Continual Learning via Sparse Networks
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs
Gla-AI4BioMed at RRG24: Visual Instruction-tuned Adaptation for Radiology Report Generation
Diffusion Sequence Models for Enhanced Protein Representation and Generation
SCOPE: Sign Language Contextual Processing with Embedding from LLMs
Retrieve and Refine: Exemplar-based Neural Comment Generation
Handwritten Mathematical Expression Recognition with Bidirectionally Trained Transformer
What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation
Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators
Sparse and Dense Approaches for the Full-rank Retrieval of Responses for Dialogues
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysis
SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.