https://github.com/nhcha6/classdistributionprediction
공식 코드Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection
Slot Induction via Pre-trained Language Model Probing and Multi-level Contrastive Learning
Controllable Citation Sentence Generation with Language Models
EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition
Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search
User-in-the-loop Adaptive Intent Detection for Instructable Digital Assistant
Spoken Language Modeling with Duration-Penalized Self-Supervised Units
Automated Privacy Information Annotation in Large Language Model Interactions
Personalized LLM for Generating Customized Responses to the Same Query from Different Users
BERTtime Stories: Investigating the Role of Synthetic Story Data in Language pre-training
LLaMEA: A Large Language Model Evolutionary Algorithm for Automatically Generating Metaheuristics
Unsupervised Contrast-Consistent Ranking with Language Models
Composed Image Retrieval for Training-Free Domain Conversion
KNN-Defense: Defense against 3D Adversarial Point Clouds using Nearest-Neighbor Search
Robust Conversational Agents against Imperceptible Toxicity Triggers
ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs
Multi-Programming Language Ensemble for Code Generation in Large Language Model
Extracting Latent Steering Vectors from Pretrained Language Models
Solving Hard Analogy Questions with Relation Embedding Chains
Farewell to Aimless Large-scale Pretraining: Influential Subset Selection for Language Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.