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

https://github.com/thorhildurt/dynamic-human-evaluation

공식 코드

Dynamic Human Evaluation for Relative Model Comparisons

https://github.com/thu-coai/critiquellm

공식 코드

CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation

https://github.com/thu-ml/spargeattn

공식 코드

SpargeAttention: Accurate and Training-free Sparse Attention Accelerating Any Model Inference

https://github.com/thuccslab/mergeguard

공식 코드

Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model Merging

https://github.com/thudm/cogvlm

공식 코드

CogAgent: A Visual Language Model for GUI Agents

https://github.com/thudm/glm-130b

공식 코드

GLM-130B: An Open Bilingual Pre-trained Model

https://github.com/thunlp-mt/modelcompose

공식 코드

Model Composition for Multimodal Large Language Models

https://github.com/thunlp/MultiRD

공식 코드

Multi-channel Reverse Dictionary Model

https://github.com/tianheyu927/mopo

공식 코드

MOPO: Model-based Offline Policy Optimization

https://github.com/tianyunyoung/model-fingerprint

공식 코드

Model Synthesis for Zero-Shot Model Attribution

https://github.com/tigerchen52/Biomedical-Entity-Linking

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A Lightweight Neural Model for Biomedical Entity Linking

https://github.com/tigerresearch/tigerbot

공식 코드

SoftTiger: A Clinical Foundation Model for Healthcare Workflows

https://github.com/tim283/smpc_example

공식 코드

(Stochastic) Model Predictive Control -- a Simulation Example

https://github.com/tirtho109/vanillapinnsvsfbpinns

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Towards Model Discovery Using Domain Decomposition and PINNs

https://github.com/tjunlp-lab/awesome-llm-safety-papers

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Large Language Model Safety: A Holistic Survey

https://github.com/tlc4418/llm_optimization

공식 코드

Reward Model Ensembles Help Mitigate Overoptimization

https://github.com/tmlr-group/deepinception

공식 코드

DeepInception: Hypnotize Large Language Model to Be Jailbreaker

https://github.com/tmlr-group/memorization-discrepancy

공식 코드

Exploring Model Dynamics for Accumulative Poisoning Discovery

https://github.com/todejong/deep-operator-neural-network-model-predictive-control

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Deep Operator Neural Network Model Predictive Control

https://github.com/tomlimi/dama

공식 코드

Debiasing Algorithm through Model Adaptation

https://github.com/tommasomncttn/mergenetic

공식 코드

Mergenetic: a Simple Evolutionary Model Merging Library

https://github.com/tomo835g/Deep-Learning-to-find-Superconductors

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Deep Learning Model for Finding New Superconductors

https://github.com/tomsheng21/adaptguard

공식 코드

AdaptGuard: Defending Against Universal Attacks for Model Adaptation

https://github.com/tonychenxyz/selfie

공식 코드

SelfIE: Self-Interpretation of Large Language Model Embeddings

https://github.com/torchmoe/moe-infinity

공식 코드

MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache

https://github.com/torchrwkv/flash-linear-attention

공식 코드

Cross-attention for State-based model RWKV-7

https://github.com/tranbahien/bae-prior

공식 코드

Model Selection for Bayesian Autoencoders

https://github.com/tranluan/Nonlinear_Face_3DMM

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Nonlinear 3D Face Morphable Model

https://github.com/transfergraph/transfergraph

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Model Selection with Model Zoo via Graph Learning

https://github.com/trentonwei/dorlingmap

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Generating grid maps via the snake model

https://github.com/tridungduong16/counterfactual_fairness_game_theoretic

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Achieving Counterfactual Fairness with Imperfect Structural Causal Model

https://github.com/tsachiblau/Threat-Model-Agnostic-Adversarial-Defense-using-Diffusion-Models

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Threat Model-Agnostic Adversarial Defense using Diffusion Models

https://github.com/tsotsoslab/smiler

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SMILER: Saliency Model Implementation Library for Experimental Research

https://github.com/tsukuba-websci/psilmn

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Problem-Solving in Language Model Networks

https://github.com/tubehao/sinkt

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SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model

https://github.com/txh-mercury/cosa

공식 코드

COSA: Concatenated Sample Pretrained Vision-Language Foundation Model

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