https://github.com/zetangforward/lcm_stack
공식 코드LOGO -- Long cOntext aliGnment via efficient preference Optimization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
LOGO -- Long cOntext aliGnment via efficient preference Optimization
ViC: Virtual Compiler Is All You Need For Assembly Code Search
Authorship Attribution Using a Neural Network Language Model
Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones
Patterns versus Characters in Subword-aware Neural Language Modeling
Beyond Text: Frozen Large Language Models in Visual Signal Comprehension
A Feasible Framework for Arbitrary-Shaped Scene Text Recognition
Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively
Improving Variational Autoencoders with Density Gap-based Regularization
OpenMaskDINO3D : Reasoning 3D Segmentation via Large Language Model
Question answering system of bridge design specification based on large language model
SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language Models
Track the Answer: Extending TextVQA from Image to Video with Spatio-Temporal Clues
Bidirectional Transformer Reranker for Grammatical Error Correction
Steel-LLM:From Scratch to Open Source -- A Personal Journey in Building a Chinese-Centric LLM
Empowering Large Language Model Agents through Action Learning
TabuLa: Harnessing Language Models for Tabular Data Synthesis
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change
Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model
REBEL: Reinforcement Learning via Regressing Relative Rewards
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models
Dependency Transformer Grammars: Integrating Dependency Structures into Transformer Language Models
ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning
ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling
Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.