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 801–850 of 1135 papers

TitleStatusHype
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-ScalingCode3
Aurora:Activating Chinese chat capability for Mixtral-8x7B sparse Mixture-of-Experts through Instruction-TuningCode2
T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by StepCode2
LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding—0
Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning—0
An In-depth Look at Gemini's Language AbilitiesCode1
M3DBench: Let's Instruct Large Models with Multi-modal 3D PromptsCode1
Rethinking the Instruction Quality: LIFT is What You Need—0
LMDrive: Closed-Loop End-to-End Driving with Large Language ModelsCode2
ThinkBot: Embodied Instruction Following with Thought Chain Reasoning—0
InstructAny2Pix: Flexible Visual Editing via Multimodal Instruction FollowingCode0
Aligner: One Global Token is Worth Millions of Parameters When Aligning Large Language Models—0
Localized Symbolic Knowledge Distillation for Visual Commonsense ModelsCode0
Text as Image: Learning Transferable Adapter for Multi-Label Classification—0
MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following—0
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
InstructBooth: Instruction-following Personalized Text-to-Image Generation—0
MedXChat: A Unified Multimodal Large Language Model Framework towards CXRs Understanding and Generation—0
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video UnderstandingCode2
SeaLLMs -- Large Language Models for Southeast AsiaCode1
Generative Parameter-Efficient Fine-TuningCode1
FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, ToxicityCode0
Contrastive Vision-Language Alignment Makes Efficient Instruction LearnerCode1
Text as Images: Can Multimodal Large Language Models Follow Printed Instructions in Pixels?Code1
Ranni: Taming Text-to-Image Diffusion for Accurate Instruction FollowingCode5
Releasing the CRaQAn (Coreference Resolution in Question-Answering): An open-source dataset and dataset creation methodology using instruction-following models—0
MoDS: Model-oriented Data Selection for Instruction TuningCode1
Towards Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs—0
GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation—0
GeoChat: Grounded Large Vision-Language Model for Remote SensingCode2
CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction TuningCode1
Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMsCode1
HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction DataCode1
LIMIT: Less Is More for Instruction Tuning Across Evaluation Paradigms—0
Data Diversity Matters for Robust Instruction Tuning—0
RecExplainer: Aligning Large Language Models for Explaining Recommendation Models—0
Traffic Sign Interpretation in Real Road Scene—0
WatME: Towards Lossless Watermarking Through Lexical Redundancy—0
Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination—0
DocLens: Multi-aspect Fine-grained Evaluation for Medical Text GenerationCode1
FollowEval: A Multi-Dimensional Benchmark for Assessing the Instruction-Following Capability of Large Language Models—0
How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their VulnerabilitiesCode0
Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?—0
PLUG: Leveraging Pivot Language in Cross-Lingual Instruction TuningCode1
Defending Large Language Models Against Jailbreaking Attacks Through Goal PrioritizationCode1
MAP's not dead yet: Uncovering true language model modes by conditioning away degeneracy—0
Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable SummarizationCode1
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language ModelsCode3
How You Prompt Matters! Even Task-Oriented Constraints in Instructions Affect LLM-Generated Text DetectionCode0
Self-Evolved Diverse Data Sampling for Efficient Instruction TuningCode1
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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