https://github.com/salt-nlp/damp
공식 코드DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration
CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences
PreAlign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual Alignment
GigaAM: Efficient Self-Supervised Learner for Speech Recognition
Discovering Preference Optimization Algorithms with and for Large Language Models
L2MAC: Large Language Model Automatic Computer for Extensive Code Generation
Training Language Models to Win Debates with Self-Play Improves Judge Accuracy
Improving Large Language Model Safety with Contrastive Representation Learning
An Investigation of Language Model Interpretability via Sentence Editing
An Investigation of Language Model Interpretability via Sentence Editing
To Tell The Truth: Language of Deception and Language Models
DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining
SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials
Inheritune: Training Smaller Yet More Attentive Language Models
Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models
Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models
Exploring Language Model Generalization in Low-Resource Extractive QA
An Isotropy Analysis in the Multilingual BERT Embedding Space
HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics
Emotional Neural Language Generation Grounded in Situational Contexts
Jump to Conclusions: Short-Cutting Transformers With Linear Transformations
CoS: Enhancing Personalization and Mitigating Bias with Context Steering
Personalized Image Enhancement Featuring Masked Style Modeling
Prompt-Guided Image-Adaptive Neural Implicit Lookup Tables for Interpretable Image Enhancement
RACER: An LLM-powered Methodology for Scalable Analysis of Semi-structured Mental Health Interviews
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.