Papers with Code 아카이브

Large Language Model

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/zetangforward/lcm_stack

L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?

https://github.com/zhaoolee/garss

AgentOps: Enabling Observability of LLM Agents

https://github.com/zhengli97/promptkd

ATPrompt: Textual Prompt Learning with Embedded Attributes

https://github.com/zhengxujosh/awesome-rag-vision

Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook

https://github.com/zhenqincn/fedkseed

Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes

https://github.com/zihanzhaosjtu/librisqa

PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering

https://github.com/ziyuguo99/image-generation-cot

MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine

https://github.com/zrrskywalker/point-bind

Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

https://github.com/ztcoalson/if-guide

Studying Large Language Model Generalization with Influence Functions

https://github.com/zyang1580/binllm

MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

https://huggingface.co/datasets/alykassem/DSC-Benchmark

How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities

https://huggingface.co/datasets/jjjjh/MM-Instruct

MM-Instruct: Generated Visual Instructions for Large Multimodal Model Alignment

https://huggingface.co/datasets/liuhuijie6410/OmniDish-Edit-35K

Omni-Dish: Photorealistic and Faithful Image Generation and Editing for Arbitrary Chinese Dishes

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.