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Instruction Following

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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/wang-research-lab/mlan

공식 코드

MLAN: Language-Based Instruction Tuning Improves Zero-Shot Generalization of Multimodal Large Language Models

https://github.com/wangclnlp/deepspeed-chat-extension

공식 코드

Hybrid Alignment Training for Large Language Models

https://github.com/wbbeyourself/scm4llms

공식 코드

Enhancing Large Language Model with Self-Controlled Memory Framework

https://github.com/weitaikang/robin3d

공식 코드

Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning

https://github.com/williambankes/robust-multi-objective-decoding

공식 코드

Robust Multi-Objective Controlled Decoding of Large Language Models

https://github.com/wishever/mmidr

공식 코드

MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge Distillation

https://github.com/wivizhang/earthgpt

공식 코드

EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain

https://github.com/wivizhang/earthmarker

공식 코드

EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote Sensing

https://github.com/wujwyi/cmc

공식 코드

Cross-model Control: Improving Multiple Large Language Models in One-time Training

https://github.com/wusize/f-lmm

공식 코드

F-LMM: Grounding Frozen Large Multimodal Models

https://github.com/wxh1996/lana-vln

공식 코드

Lana: A Language-Capable Navigator for Instruction Following and Generation

https://github.com/wxjiao/parrot

공식 코드

ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback

https://github.com/wxy3596/asma-tune

공식 코드

ASMA-Tune: Unlocking LLMs' Assembly Code Comprehension via Structural-Semantic Instruction Tuning

https://github.com/wzhouad/wpo

공식 코드

WPO: Enhancing RLHF with Weighted Preference Optimization

https://github.com/x2fd/lvis-instruct4v

공식 코드

To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning

https://github.com/xfgao/dialfred

공식 코드

DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following

https://github.com/xhmy/autodefense

공식 코드

AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks

https://github.com/xhz0809/x-shot

공식 코드

X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification

https://github.com/xiaomi/toolplanner

공식 코드

ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback

https://github.com/xiaoyu-sz/llmasevaluator

공식 코드

Large Language Models as Evaluators for Recommendation Explanations

https://github.com/xinykou/safety_realignment

공식 코드

A safety realignment framework via subspace-oriented model fusion for large language models

https://github.com/xqlin98/INSTINCT

공식 코드

Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers

https://github.com/xt-cyh/codi-eval

공식 코드

Benchmarking Large Language Models on Controllable Generation under Diversified Instructions

https://github.com/xubuvd/phasedsft

공식 코드

Phased Instruction Fine-Tuning for Large Language Models

https://github.com/xuetianci/pacit

공식 코드

PACIT: Unlocking the Power of Examples for Better In-Context Instruction Tuning

https://github.com/xuliu-cyber/rsunivlm

공식 코드

RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of Experts

https://github.com/xxyQwQ/ComfyBench

공식 코드

ComfyBench: Benchmarking LLM-based Agents in ComfyUI for Autonomously Designing Collaborative AI Systems

https://github.com/xzhang97666/alpacare

공식 코드

AlpaCare:Instruction-tuned Large Language Models for Medical Application

https://github.com/yafuly/tpo

공식 코드

Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback

https://github.com/yale-nlp/ReIFE

공식 코드

ReIFE: Re-evaluating Instruction-Following Evaluation

https://github.com/yale-nlp/bay-calibration-llm-evaluators

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Bayesian Calibration of Win Rate Estimation with LLM Evaluators

https://github.com/yale-nlp/instrusum

공식 코드

Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization

https://github.com/yale-nlp/mdcure

공식 코드

MDCure: A Scalable Pipeline for Multi-Document Instruction-Following

https://github.com/yangling0818/editworld

공식 코드

EditWorld: Simulating World Dynamics for Instruction-Following Image Editing

https://github.com/yeates/promptfix

공식 코드

PromptFix: You Prompt and We Fix the Photo

https://github.com/yingchaojiefeng/gstransform

공식 코드

Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space Transformation

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