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

Showing 101125 of 1135 papers

TitleStatusHype
LLaSM: Large Language and Speech ModelCode2
LLM-RG4: Flexible and Factual Radiology Report Generation across Diverse Input ContextsCode2
MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language ModelsCode2
LITA: Language Instructed Temporal-Localization AssistantCode2
LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language InterpretationCode2
Learning to Decode Collaboratively with Multiple Language ModelsCode2
CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language ModelCode2
DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil EngineeringCode2
Autonomous Improvement of Instruction Following Skills via Foundation ModelsCode2
Lion: Adversarial Distillation of Proprietary Large Language ModelsCode2
Dual-Space Knowledge Distillation for Large Language ModelsCode2
BayLing: Bridging Cross-lingual Alignment and Instruction Following through Interactive Translation for Large Language ModelsCode2
Aligning Modalities in Vision Large Language Models via Preference Fine-tuningCode2
Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task ArithmeticCode2
A Critical Evaluation of AI Feedback for Aligning Large Language ModelsCode2
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free LunchCode2
Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You WantCode2
LLaVA-MORE: A Comparative Study of LLMs and Visual Backbones for Enhanced Visual Instruction TuningCode2
AutoDefense: Multi-Agent LLM Defense against Jailbreak AttacksCode2
EditWorld: Simulating World Dynamics for Instruction-Following Image EditingCode2
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective AnalysisCode2
CorDA: Context-Oriented Decomposition Adaptation of Large Language Models for Task-Aware Parameter-Efficient Fine-tuningCode2
Aurora:Activating Chinese chat capability for Mixtral-8x7B sparse Mixture-of-Experts through Instruction-TuningCode2
Long-Context Language Modeling with Parallel Context EncodingCode2
Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language ModelsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AutoIF (Llama3 70B)Inst-level loose-accuracy90.4Unverified
2AutoIF (Qwen2 72B)Inst-level loose-accuracy88Unverified
3GPT-4Inst-level loose-accuracy85.37Unverified
4PaLM 2 SInst-level loose-accuracy59.11Unverified