Papers with Code 아카이브
Instruction Following
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Instruction following is the basic task of the model. This task is dedicated to evaluating the ability of the large model to follow human instructions. It is hoped that the model can generate controllable and safe answers.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Vill-Lab/forkOfAlchemistCoder
AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data
https://github.com/Xieyangxinyu/Unbiased-Watermark-via-Maximal-Coupling
WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models
https://github.com/ZHZisZZ/emulated-disalignment
An Emulator for Fine-Tuning Large Language Models using Small Language Models
https://github.com/ZhangYiqun018/StickerConv
https://github.com/agentcoord/agentcoord
ExpertPrompting: Instructing Large Language Models to be Distinguished Experts
https://github.com/albertotestoni/ndq_visual_objects
https://github.com/ali-vilab/FlashFace
FlashFace: Human Image Personalization with High-fidelity Identity Preservation
https://github.com/alibaba-damo-academy/FunASR
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
https://github.com/allenai/allenact
https://github.com/allenai/natural-instructions-expansion
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
https://github.com/alpha-vllm/llama2-accessory
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
https://github.com/aqweteddy/ChatVector
https://github.com/augmxnt/deccp
https://github.com/baai-zlab/coig
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
https://github.com/baaivision/eva
https://github.com/baichuan-inc/Baichuan-Omni-1.5
https://github.com/baichuan-inc/baichuan-audio
https://github.com/beomi/koalpaca
Self-Instruct: Aligning Language Models with Self-Generated Instructions
https://github.com/bhiziroglu/Language-as-an-Abstraction-for-Hierarchical-Deep-Reinforcement-Learning
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
https://github.com/brains-group/openfedllm
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
https://github.com/brandon3964/multimodal-task-vector
https://github.com/bytedance/lynx-llm
What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?
https://github.com/camel-ai/owl
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
https://github.com/camenduru/llava-colab
https://github.com/clic-lab/chalet
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
https://github.com/computer-vision-in-the-wild/cvinw_readings
https://github.com/cornell-zhang/llm-datatypes
https://github.com/daniel-furman/sft-demos
Self-Instruct: Aligning Language Models with Self-Generated Instructions
https://github.com/databrickslabs/dolly
Self-Instruct: Aligning Language Models with Self-Generated Instructions
https://github.com/davidkim205/komt
https://github.com/davidnvq/lwit-alfred
Look Wide and Interpret Twice: Improving Performance on Interactive Instruction-following Tasks
https://github.com/declare-lab/red-instruct
https://github.com/deepcs233/visual-cot
LMDrive: Closed-Loop End-to-End Driving with Large Language Models
https://github.com/deepmind/streetlearn
https://github.com/deepseek-ai/deepseek-llm
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.