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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 326–350 of 1135 papers

TitleStatusHype
AlignFormer: Modality Matching Can Achieve Better Zero-shot Instruction-Following Speech-LLM—0
MiningGPT -- A Domain-Specific Large Language Model for the Mining Industry—0
Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation—0
VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation—0
InsightEdit: Towards Better Instruction Following for Image Editing—0
ShowUI: One Vision-Language-Action Model for GUI Visual AgentCode5
Parameter Efficient Instruction Tuning: An Empirical StudyCode4
Gaussian Scenes: Pose-Free Sparse-View Scene Reconstruction using Depth-Enhanced Diffusion Priors—0
From MTEB to MTOB: Retrieval-Augmented Classification for Descriptive GrammarsCode0
Enhancing Instruction-Following Capability of Visual-Language Models by Reducing Image Redundancy—0
Separable Mixture of Low-Rank Adaptation for Continual Visual Instruction Tuning—0
GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual GroundingCode2
MpoxVLM: A Vision-Language Model for Diagnosing Skin Lesions from Mpox Virus InfectionCode0
MLAN: Language-Based Instruction Tuning Improves Zero-Shot Generalization of Multimodal Large Language ModelsCode0
Adaptive Decoding via Latent Preference Optimization—0
LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language InterpretationCode2
Zero-shot Object-Centric Instruction Following: Integrating Foundation Models with Traditional Navigation—0
LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context ScenariosCode0
SetLexSem Challenge: Using Set Operations to Evaluate the Lexical and Semantic Robustness of Language ModelsCode1
Stronger Models are NOT Stronger Teachers for Instruction Tuning—0
MrSteve: Instruction-Following Agents in Minecraft with What-Where-When Memory—0
IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization—0
Fox-1 Technical Report—0
Bayesian Calibration of Win Rate Estimation with LLM EvaluatorsCode0
Multi-Reward as Condition for Instruction-based Image Editing—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