https://github.com/kaistai/instructir
공식 코드INSTRUCTIR: A Benchmark for Instruction Following of Information Retrieval Models
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
INSTRUCTIR: A Benchmark for Instruction Following of Information Retrieval Models
Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge Acquisition
Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation
Selective Token Generation for Few-shot Natural Language Generation
Non-Exchangeable Conformal Language Generation with Nearest Neighbors
Adaptive KalmanNet: Data-Driven Kalman Filter with Fast Adaptation
Speculative Ensemble: Fast Large Language Model Ensemble via Speculation
A segmental framework for fully-unsupervised large-vocabulary speech recognition
Sequential Large Language Model-Based Hyper-parameter Optimization
Teaching Specific Scientific Knowledge into Large Language Models through Additional Training
Zebra-Llama: A Context-Aware Large Language Model for Democratizing Rare Disease Knowledge
Data augmentation using prosody and false starts to recognize non-native children's speech
Neural sentence embedding models for semantic similarity estimation in the biomedical domain
ChamaleonLLM: Batch-Aware Dynamic Low-Rank Adaptation via Inference-Time Clusters
Training and Generating Neural Networks in Compressed Weight Space
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning
Counterfactual Language Model Adaptation for Suggesting Phrases
Video Enriched Retrieval Augmented Generation Using Aligned Video Captions
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering
A context-aware knowledge transferring strategy for CTC-based ASR
Period-LLM: Extending the Periodic Capability of Multimodal Large Language Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.