https://github.com/shizhediao/automate-cot
공식 코드Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data
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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data
Write and Paint: Generative Vision-Language Models are Unified Modal Learners
Noisy Channel Language Model Prompting for Few-Shot Text Classification
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
DiffSim: Taming Diffusion Models for Evaluating Visual Similarity
Egocentric Video-Language Pretraining @ EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022
One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos
POSS: Position Specialist Generates Better Draft for Speculative Decoding
IIT (BHU) Varanasi at MSR-SRST 2018: A Language Model Based Approach for Natural Language Generation
Lexically Aware Semi-Supervised Learning for OCR Post-Correction
Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language Reasoning
Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation
Transformers for Green Semantic Communication: Less Energy, More Semantics
A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data
FIRST: Teach A Reliable Large Language Model Through Efficient Trustworthy Distillation
Enhance Incomplete Utterance Restoration by Joint Learning Token Extraction and Text Generation
Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval
The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models
Understanding Architectures Learnt by Cell-based Neural Architecture Search
Nemesis: Normalizing the Soft-prompt Vectors of Vision-Language Models
Scaling Inference-Time Search with Vision Value Model for Improved Visual Comprehension
RALLRec+: Retrieval Augmented Large Language Model Recommendation with Reasoning
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.