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 26–50 of 1135 papers

TitleStatusHype
Alzheimer's Dementia Detection Using Perplexity from Paired Large Language Models—0
VerIF: Verification Engineering for Reinforcement Learning in Instruction FollowingCode2
LLaVA-c: Continual Improved Visual Instruction Tuning—0
RHealthTwin: Towards Responsible and Multimodal Digital Twins for Personalized Well-being—0
EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language ModelsCode0
LeVo: High-Quality Song Generation with Multi-Preference AlignmentCode5
Video Unlearning via Low-Rank Refusal Vector—0
Aligning Text, Images, and 3D Structure Token-by-Token—0
Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated TextCode1
Audio-Aware Large Language Models as Judges for Speaking Styles—0
Being Strong Progressively! Enhancing Knowledge Distillation of Large Language Models through a Curriculum Learning FrameworkCode0
RELIC: Evaluating Compositional Instruction Following via Language Recognition—0
Unleashing Hour-Scale Video Training for Long Video-Language Understanding—0
SeedEdit 3.0: Fast and High-Quality Generative Image Editing—0
Identifying Reliable Evaluation Metrics for Scientific Text RevisionCode0
On the Mechanism of Reasoning Pattern Selection in Reinforcement Learning for Language Models—0
Robust Anti-Backdoor Instruction Tuning in LVLMs—0
RewardAnything: Generalizable Principle-Following Reward ModelsCode1
MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching—0
TIIF-Bench: How Does Your T2I Model Follow Your Instructions?—0
Incentivizing Reasoning for Advanced Instruction-Following of Large Language ModelsCode1
RewardBench 2: Advancing Reward Model EvaluationCode4
MoDA: Modulation Adapter for Fine-Grained Visual Grounding in Instructional MLLMs—0
FusionAudio-1.2M: Towards Fine-grained Audio Captioning with Multimodal Contextual FusionCode2
PersianMedQA: Language-Centric Evaluation of LLMs in the Persian Medical Domain—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