https://github.com/cjx96/loid
공식 코드Enhancing Content-based Recommendation via Large Language Model
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Enhancing Content-based Recommendation via Large Language Model
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
UDALM: Unsupervised Domain Adaptation through Language Modeling
Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese
Cross-Modal Projection in Multimodal LLMs Doesn't Really Project Visual Attributes to Textual Space
Conceptual Learning via Embedding Approximations for Reinforcing Interpretability and Transparency
Unified Lexical Representation for Interpretable Visual-Language Alignment
Towards Interpreting Visual Information Processing in Vision-Language Models
Trade-Offs Between Fairness and Privacy in Language Modeling
M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets
BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision
Less is More: Task-aware Layer-wise Distillation for Language Model Compression
PhayaThaiBERT: Enhancing a Pretrained Thai Language Model with Unassimilated Loanwords
ClimateBert: A Pretrained Language Model for Climate-Related Text
AsymLoRA: Harmonizing Data Conflicts and Commonalities in MLLMs
TabLLM: Few-shot Classification of Tabular Data with Large Language Models
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
Topic-VQ-VAE: Leveraging Latent Codebooks for Flexible Topic-Guided Document Generation
GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.