https://github.com/azreasoners/llm-asp
공식 코드Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text
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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 They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model.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.
Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text
M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models
Progressive Text-to-Image Diffusion with Soft Latent Direction
Reference Free Domain Adaptation for Translation of Noisy Questions with Question Specific Rewards
Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models
Emotional RAG: Enhancing Role-Playing Agents through Emotional Retrieval
Baichuan-Audio: A Unified Framework for End-to-End Speech Interaction
Measuring Implicit Bias in Explicitly Unbiased Large Language Models
Agentic Society: Merging skeleton from real world and texture from Large Language Model
EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG Model
ViTHSD: Exploiting Hatred by Targets for Hate Speech Detection on Vietnamese Social Media Texts
All Should Be Equal in the Eyes of Language Models: Counterfactually Aware Fair Text Generation
Advancing Beyond Identification: Multi-bit Watermark for Large Language Models
Recoverable Compression: A Multimodal Vision Token Recovery Mechanism Guided by Text Information
Recoverable Compression: A Multimodal Vision Token Recovery Mechanism Guided by Text Information
Vid-Morp: Video Moment Retrieval Pretraining from Unlabeled Videos in the Wild
Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations
Establishing baselines for generative discovery of inorganic crystals
On the Efficiency of NLP-Inspired Methods for Tabular Deep Learning
Mirostat: A Neural Text Decoding Algorithm that Directly Controls Perplexity
Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
Follow-Up Differential Descriptions: Language Models Resolve Ambiguities for Image Classification
Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages
Scene Text Recognition with Permuted Autoregressive Sequence Models
Discovering Knowledge-Critical Subnetworks in Pretrained Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.