https://github.com/hanyangzhong/situational_planning_datasets
공식 코드LLM-SAP: Large Language Models Situational Awareness Based Planning
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
LLM-SAP: Large Language Models Situational Awareness Based Planning
DRG-LLaMA : Tuning LLaMA Model to Predict Diagnosis-related Group for Hospitalized Patients
DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference Acceleration
Self-Bootstrapped Visual-Language Model for Knowledge Selection and Question Answering
Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing
Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and Reaction
Unified Modeling Language Code Generation from Diagram Images Using Multimodal Large Language Models
PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting
Typing to Listen at the Cocktail Party: Text-Guided Target Speaker Extraction
VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models
CAFe: Unifying Representation and Generation with Contrastive-Autoregressive Finetuning
SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models
Towards Efficient Exact Optimization of Language Model Alignment
MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
Language Model Decomposition: Quantifying the Dependency and Correlation of Language Models
GraphextQA: A Benchmark for Evaluating Graph-Enhanced Large Language Models
LION: Linear Group RNN for 3D Object Detection in Point Clouds
ParsiNorm: A Persian Toolkit for Speech Processing Normalization
Explaining Natural Language Processing Classifiers with Occlusion and Language Modeling
AdaptVision: Dynamic Input Scaling in MLLMs for Versatile Scene Understanding
Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs
Encoder-Agnostic Adaptation for Conditional Language Generation
Estimating Large Language Model Capabilities without Labeled Test Data
An Empirical Analysis of Uncertainty in Large Language Model Evaluations
Labels Generated by Large Language Model Helps Measuring People's Empathy in Vitro
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.