https://github.com/jshilong/gpt4roi
공식 코드GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest
Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP
SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA
Prompting Visual-Language Models for Efficient Video Understanding
Self-Supervised Speech Representations are More Phonetic than Semantic
Distilling a Pretrained Language Model to a Multilingual ASR Model
Uncertainty-Guided Optimization on Large Language Model Search Trees
CogBench: a large language model walks into a psychology lab
Similarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning
Do Language Models Exhibit Human-like Structural Priming Effects?
Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis
Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler
Content-Based Collaborative Generation for Recommender Systems
Being Comes from Not-being: Open-vocabulary Text-to-Motion Generation with Wordless Training
AugSumm: towards generalizable speech summarization using synthetic labels from large language model
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients
Low-Rank Constraints for Fast Inference in Structured Models
BERTnesia: Investigating the capture and forgetting of knowledge in BERT
WirelessAgent: Large Language Model Agents for Intelligent Wireless Networks
Multi-modal Instruction Tuned LLMs with Fine-grained Visual Perception
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.