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 151–175 of 1135 papers

TitleStatusHype
Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement LearningCode2
Pay More Attention to the Robustness of Prompt for Instruction Data Mining—0
Effectively Controlling Reasoning Models through Thinking Intervention—0
Learning to Instruct for Visual Instruction Tuning—0
Qwen2.5-Omni Technical ReportCode7
InsViE-1M: Effective Instruction-based Video Editing with Elaborate Dataset ConstructionCode1
Gemma 3 Technical Report—0
SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the WildCode7
OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence—0
Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual SettingsCode0
LLaVA-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction TuningCode2
ThinkPatterns-21k: A Systematic Study on the Impact of Thinking Patterns in LLMs—0
Can Language Models Follow Multiple Turns of Entangled Instructions?Code1
ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control—0
D3: Diversity, Difficulty, and Dependability-Aware Data Selection for Sample-Efficient LLM Instruction Tuning—0
ASMA-Tune: Unlocking LLMs' Assembly Code Comprehension via Structural-Semantic Instruction TuningCode0
Compositional Subspace Representation Fine-tuning for Adaptive Large Language Models—0
Got Compute, but No Data: Lessons From Post-training a Finnish LLM—0
Exo2Ego: Exocentric Knowledge Guided MLLM for Egocentric Video Understanding—0
DAFE: LLM-Based Evaluation Through Dynamic Arbitration for Free-Form Question-Answering—0
Open-World Skill Discovery from Unsegmented Demonstrations—0
Robust Multi-Objective Controlled Decoding of Large Language ModelsCode0
Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation ModelCode2
XIFBench: Evaluating Large Language Models on Multilingual Instruction Following—0
DistiLLM-2: A Contrastive Approach Boosts the Distillation of LLMsCode2
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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