https://github.com/cmbagents/cmbagent
공식 코드Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery
A Masked Segmental Language Model for Unsupervised Natural Language Segmentation
Multilingual unsupervised sequence segmentation transfers to extremely low-resource languages
Orthogonal Subspace Learning for Language Model Continual Learning
MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection
LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development
Are Pretrained Multilingual Models Equally Fair Across Languages?
Factual Consistency of Multilingual Pretrained Language Models
Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing
EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate
Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code
PneumoLLM: Harnessing the Power of Large Language Model for Pneumoconiosis Diagnosis
Benchmarking Sequential Visual Input Reasoning and Prediction in Multimodal Large Language Models
Exploiting BERT For Multimodal Target Sentiment Classification Through Input Space Translation
Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning
CMD: a framework for Context-aware Model self-Detoxification
Masked Structural Growth for 2x Faster Language Model Pre-training
Open-domain Implicit Format Control for Large Language Model Generation
Fishing for Magikarp: Automatically Detecting Under-trained Tokens in Large Language Models
Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering
Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models
Effective and Imperceptible Adversarial Textual Attack via Multi-objectivization
Discovering Latent Knowledge in Language Models Without Supervision
Evaluating Language Models for Mathematics through Interactions
ScribeAgent: Towards Specialized Web Agents Using Production-Scale Workflow Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.