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 776–800 of 1135 papers

TitleStatusHype
Assessing Robustness to Spurious Correlations in Post-Training Language Models—0
Robust Anti-Backdoor Instruction Tuning in LVLMs—0
Robust Instruction-Following in a Situated Agent via Transfer-Learning from Text—0
Robust Learning of Diverse Code Edits—0
Role-Play Zero-Shot Prompting with Large Language Models for Open-Domain Human-Machine Conversation—0
3D-MoE: A Mixture-of-Experts Multi-modal LLM for 3D Vision and Pose Diffusion via Rectified Flow—0
DPO Kernels: A Semantically-Aware, Kernel-Enhanced, and Divergence-Rich Paradigm for Direct Preference Optimization—0
Rethinking the Instruction Quality: LIFT is What You Need—0
VeRA: Vector-based Random Matrix Adaptation—0
Verifiable Format Control for Large Language Model Generations—0
AC/DC: LLM-based Audio Comprehension via Dialogue Continuation—0
VideoExpert: Augmented LLM for Temporal-Sensitive Video Understanding—0
S2S-Arena, Evaluating Speech2Speech Protocols on Instruction Following with Paralinguistic Information—0
SAG: Style-Aligned Article Generation via Model Collaboration—0
SAIF: A Sparse Autoencoder Framework for Interpreting and Steering Instruction Following of Language Models—0
SAIL: Search-Augmented Instruction Learning—0
SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation—0
SaulLM-54B & SaulLM-141B: Scaling Up Domain Adaptation for the Legal Domain—0
Scalable Ensembling For Mitigating Reward Overoptimisation—0
Scalable Vision Language Model Training via High Quality Data Curation—0
ScaleBiO: Scalable Bilevel Optimization for LLM Data Reweighting—0
Video Instruction Tuning With Synthetic Data—0
Video Unlearning via Low-Rank Refusal Vector—0
Argument Quality Assessment in the Age of Instruction-Following Large Language Models—0
VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding—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