https://github.com/orhonovich/unnatural-instructions
공식 코드Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
Rank1: Test-Time Compute for Reranking in Information Retrieval
Making Retrieval-Augmented Language Models Robust to Irrelevant Context
Excuse me, sir? Your language model is leaking (information)
Fact-checking information from large language models can decrease headline discernment
RUIE: Retrieval-based Unified Information Extraction using Large Language Model
AttributionBench: How Hard is Automatic Attribution Evaluation?
Automatic Evaluation of Attribution by Large Language Models
Biomedical Language Models are Robust to Sub-optimal Tokenization
Solving the Right Problem is Key for Translational NLP: A Case Study in UMLS Vocabulary Insertion
Lossless Acceleration of Large Language Model via Adaptive N-gram Parallel Decoding
Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue
Learning Deep Semantic Model for Code Search using CodeSearchNet Corpus
AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language Models
Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements
AntiFold: Improved antibody structure-based design using inverse folding
Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods
Ternary Singular Value Decomposition as a Better Parameterized Form in Linear Mapping
Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation
Multiple-Source Domain Adaptation via Coordinated Domain Encoders and Paired Classifiers
An Explanation of In-context Learning as Implicit Bayesian Inference
Codified audio language modeling learns useful representations for music information retrieval
Fast Training of Recurrent Neural Networks with Stationary State Feedbacks
in2IN: Leveraging individual Information to Generate Human INteractions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.