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 851–875 of 1135 papers

TitleStatusHype
EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM—0
DrivingDojo Dataset: Advancing Interactive and Knowledge-Enriched Driving World Model—0
Dr Genre: Reinforcement Learning from Decoupled LLM Feedback for Generic Text Rewriting—0
Effectively Controlling Reasoning Models through Thinking Intervention—0
Efficient Finetuning Large Language Models For Vietnamese Chatbot—0
Sparse Activation Editing for Reliable Instruction Following in Narratives—0
Efficient Telecom Specific LLM: TSLAM-Mini with QLoRA and Digital Twin Data—0
Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation—0
Embodied Concept Learner: Self-supervised Learning of Concepts and Mapping through Instruction Following—0
Embodied Instruction Following in Unknown Environments—0
Emo-DPO: Controllable Emotional Speech Synthesis through Direct Preference Optimization—0
D-Rax: Domain-specific Radiologic assistant leveraging multi-modal data and eXpert model predictions—0
Empirical Analysis of Large Vision-Language Models against Goal Hijacking via Visual Prompt Injection—0
Empowering LLMs to Understand and Generate Complex Vector Graphics—0
Draw Me a Flower: Processing and Grounding Abstraction in Natural Language—0
Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment—0
VL-Trojan: Multimodal Instruction Backdoor Attacks against Autoregressive Visual Language Models—0
Enhancing Complex Instruction Following for Large Language Models with Mixture-of-Contexts Fine-tuning—0
Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation—0
Enhancing Instruction-Following Capability of Visual-Language Models by Reducing Image Redundancy—0
Enhancing Low-Resource Language and Instruction Following Capabilities of Audio Language Models—0
Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling—0
Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity—0
ETHER: Aligning Emergent Communication for Hindsight Experience Replay—0
SpeechGuard: Exploring the Adversarial Robustness of Multimodal Large Language Models—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