Papers with Code 아카이브

Instruction Following

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 1
보존된 평가 기록: 4

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/google-research/google-research/tree/master/instruction_following_eval

공식 코드

Instruction-Following Evaluation for Large Language Models

https://github.com/gpt4life/alpagasus

공식 코드

AlpaGasus: Training A Better Alpaca with Fewer Data

https://github.com/graphpku/pissa

공식 코드

MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM Finetuning

https://github.com/greshake/llm-security

공식 코드

Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

https://github.com/greshake/lm-safety

공식 코드

Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection

https://github.com/guruprasad93/flexiblemachinelearning

공식 코드

Engineering flexible machine learning systems by traversing functionally-invariant paths

https://github.com/hamishivi/easylm

공식 코드

Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback

https://github.com/hamishivi/tess-2

공식 코드

TESS 2: A Large-Scale Generalist Diffusion Language Model

https://github.com/hanqingwangai/ccc-vln

공식 코드

Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in Vision-Language Navigation

https://github.com/hao840/adem-vl

공식 코드

ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning

https://github.com/haotian-liu/LLaVA

공식 코드

Visual Instruction Tuning

https://github.com/harrytea/tgdoc

공식 코드

Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMs

https://github.com/helloelwin/iterative-label-refinement

공식 코드

Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision

https://github.com/hiyouga/llama-factory

공식 코드

Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic

https://github.com/hkust-knowcomp/absinstruct

공식 코드

AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation

https://github.com/hlzhang109/causal-eval

공식 코드

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning

https://github.com/hope-rita/eifbench

공식 코드

EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language Models

https://github.com/hplt-project/monolingual-multilingual-instruction-tuning

공식 코드

Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca

https://github.com/huihuichyan/musc

공식 코드

MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training

https://github.com/humainlab/personalized_rlhf

공식 코드

Personalized Language Modeling from Personalized Human Feedback

https://github.com/hyeonseokk/ioinst

공식 코드

Find the Intention of Instruction: Comprehensive Evaluation of Instruction Understanding for Large Language Models

https://github.com/hyn2028/llm-cxr

공식 코드

LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation

https://github.com/hypjudy/sparkles

공식 코드

Sparkles: Unlocking Chats Across Multiple Images for Multimodal Instruction-Following Models

https://github.com/ibm/KCIF

공식 코드

Evaluating the Instruction-following Abilities of Language Models using Knowledge Tasks

https://github.com/iboing/corda

공식 코드

CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuning

https://github.com/ictnlp/bayling

공식 코드

BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language Models

https://github.com/ictnlp/llama-omni2

공식 코드

LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech Synthesis

https://github.com/infly-ai/inf-mllm

공식 코드

InfMLLM: A Unified Framework for Visual-Language Tasks

https://github.com/inst-it/inst-it

공식 코드

Inst-IT: Boosting Multimodal Instance Understanding via Explicit Visual Prompt Instruction Tuning

https://github.com/intellisensing/rs-agent

공식 코드

RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent

https://github.com/internlm/alchemistcoder

공식 코드

AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data

https://github.com/internlm/internlm-xcomposer

공식 코드

InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model

https://github.com/iodmitri/gradelab

공식 코드

Grade Score: Quantifying LLM Performance in Option Selection

https://github.com/iq-kaist/rm-lingual-transfer

공식 코드

Cross-lingual Transfer of Reward Models in Multilingual Alignment

https://github.com/jacklishufan/instructany2pix

공식 코드

InstructAny2Pix: Flexible Visual Editing via Multimodal Instruction Following

https://github.com/jacklishufan/lavida

공식 코드

LaViDa: A Large Diffusion Language Model for Multimodal Understanding

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