SOTAVerified

Instruction Following

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.

Papers

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Showing 751–800 of 1135 papers

TitleStatusHype
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-TuningCode3
Answer is All You Need: Instruction-following Text Embedding via Answering the QuestionCode1
Multi-Query Focused Disaster Summarization via Instruction-Based Prompting—0
Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation—0
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs—0
Policy Improvement using Language Feedback ModelsCode0
AIR-Bench: Benchmarking Large Audio-Language Models via Generative ComprehensionCode2
GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended TasksCode2
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated LearningCode3
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction FollowingCode2
Aya Dataset: An Open-Access Collection for Multilingual Instruction TuningCode1
Personalized Language Modeling from Personalized Human FeedbackCode1
DistiLLM: Towards Streamlined Distillation for Large Language ModelsCode3
Vision-Language Models Provide Promptable Representations for Reinforcement Learning—0
Nevermind: Instruction Override and Moderation in Large Language Models—0
A Survey on Data Selection for LLM Instruction TuningCode1
Diversity Measurement and Subset Selection for Instruction Tuning Datasets—0
Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language ModelsCode2
Instruction Makes a DifferenceCode0
IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias IndicatorsCode0
Mitigating the Influence of Distractor Tasks in LMs with Prior-Aware Decoding—0
LongAlign: A Recipe for Long Context Alignment of Large Language ModelsCode3
Taking Action Towards Graceful Interaction: The Effects of Performing Actions on Modelling Policies for Instruction Clarification RequestsCode0
EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing DomainCode2
KAUCUS: Knowledge Augmented User Simulators for Training Language Model Assistants—0
SelectLLM: Can LLMs Select Important Instructions to Annotate?Code1
EAGLE: Speculative Sampling Requires Rethinking Feature UncertaintyCode7
F-Eval: Assessing Fundamental Abilities with Refined Evaluation MethodsCode1
Towards 3D Molecule-Text Interpretation in Language ModelsCode2
Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-AlignmentCode3
AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents—0
Self-Rewarding Language ModelsCode1
SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language ModelCode2
COCO is "ALL'' You Need for Visual Instruction Fine-tuning—0
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective AnalysisCode2
PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities—0
Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-TranslationCode1
InFoBench: Evaluating Instruction Following Ability in Large Language ModelsCode2
Human-Instruction-Free LLM Self-Alignment with Limited Samples—0
Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models—0
ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction TuningCode2
LLaMA Pro: Progressive LLaMA with Block ExpansionCode4
Multilingual Instruction Tuning With Just a Pinch of Multilinguality—0
SSP: A Simple and Safe automatic Prompt engineering method towards realistic image synthesis on LVM—0
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action—0
Generate Subgoal Images before Act: Unlocking the Chain-of-Thought Reasoning in Diffusion Model for Robot Manipulation with Multimodal Prompts—0
MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene UnderstandingCode2
Benchmarking Large Language Models on Controllable Generation under Diversified InstructionsCode1
Jatmo: Prompt Injection Defense by Task-Specific FinetuningCode1
Visual Instruction Tuning towards General-Purpose Multimodal Model: A Survey—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AutoIF (Llama3 70B)Inst-level loose-accuracy90.4—Unverified
2AutoIF (Qwen2 72B)Inst-level loose-accuracy88—Unverified
3GPT-4Inst-level loose-accuracy85.37—Unverified
4PaLM 2 SInst-level loose-accuracy59.11—Unverified