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

Showing 10011010 of 1135 papers

TitleStatusHype
LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-SteeringCode0
Bayesian Calibration of Win Rate Estimation with LLM EvaluatorsCode0
Teaching Llama a New Language Through Cross-Lingual Knowledge TransferCode0
DoG-Instruct: Towards Premium Instruction-Tuning Data via Text-Grounded Instruction WrappingCode0
LLM as Dataset Analyst: Subpopulation Structure Discovery with Large Language ModelCode0
HalLoc: Token-level Localization of Hallucinations for Vision Language ModelsCode0
FALCON: Feedback-driven Adaptive Long/short-term memory reinforced Coding Optimization systemCode0
Empowering Persian LLMs for Instruction Following: A Novel Dataset and Training ApproachCode0
Towards Robust Instruction Tuning on Multimodal Large Language ModelsCode0
MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive TrainingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AutoIF (Llama3 70B)Inst-level loose-accuracy90.4Unverified
2AutoIF (Qwen2 72B)Inst-level loose-accuracy88Unverified
3GPT-4Inst-level loose-accuracy85.37Unverified
4PaLM 2 SInst-level loose-accuracy59.11Unverified