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 51–75 of 1135 papers

TitleStatusHype
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One DayCode4
Otter: A Multi-Modal Model with In-Context Instruction TuningCode4
Instruction Tuning with GPT-4Code4
Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt InjectionCode4
LaViDa: A Large Diffusion Language Model for Multimodal UnderstandingCode3
IFEval-Audio: Benchmarking Instruction-Following Capability in Audio-based Large Language ModelsCode3
LLaMA-Omni2: LLM-based Real-time Spoken Chatbot with Autoregressive Streaming Speech SynthesisCode3
VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement LearningCode3
The Breeze 2 Herd of Models: Traditional Chinese LLMs Based on Llama with Vision-Aware and Function-Calling CapabilitiesCode3
VARGPT: Unified Understanding and Generation in a Visual Autoregressive Multimodal Large Language ModelCode3
Meta-Chunking: Learning Text Segmentation and Semantic Completion via Logical PerceptionCode3
ASFT: Aligned Supervised Fine-Tuning through Absolute LikelihoodCode3
ComfyBench: Benchmarking LLM-based Agents in ComfyUI for Autonomously Designing Collaborative AI SystemsCode3
1.5-Pints Technical Report: Pretraining in Days, Not Months -- Your Language Model Thrives on Quality DataCode3
NavGPT-2: Unleashing Navigational Reasoning Capability for Large Vision-Language ModelsCode3
AudioBench: A Universal Benchmark for Audio Large Language ModelsCode3
Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language ModelsCode3
Refusal in Language Models Is Mediated by a Single DirectionCode3
MiLoRA: Harnessing Minor Singular Components for Parameter-Efficient LLM FinetuningCode3
FlashFace: Human Image Personalization with High-fidelity Identity PreservationCode3
ShapeLLM: Universal 3D Object Understanding for Embodied InteractionCode3
SongComposer: A Large Language Model for Lyric and Melody Generation in Song CompositionCode3
EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language ModelsCode3
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-TuningCode3
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated LearningCode3
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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