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 401–450 of 1135 papers

TitleStatusHype
IHEval: Evaluating Language Models on Following the Instruction HierarchyCode1
Contrastive Vision-Language Alignment Makes Efficient Instruction LearnerCode1
CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction TuningCode1
LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMsCode1
LoGU: Long-form Generation with Uncertainty ExpressionsCode1
A Recipe For Building a Compliant Real Estate ChatbotCode1
Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small ScorerCode1
LLaMo: Large Language Model-based Molecular Graph AssistantCode1
Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated TextCode1
Efficient Inference of Vision Instruction-Following Models with Elastic CacheCode1
LLaSA: A Multimodal LLM for Human Activity Analysis Through Wearable and Smartphone SensorsCode1
M-IFEval: Multilingual Instruction-Following EvaluationCode1
LIONs: An Empirically Optimized Approach to Align Language ModelsCode1
Can Language Models Follow Multiple Turns of Entangled Instructions?Code1
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote SensingCode1
Lexicon Learning for Few Shot Sequence ModelingCode1
Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play ApproachCode1
Lexicon Learning for Few-Shot Neural Sequence ModelingCode1
Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated FlightCode1
Creative Agents: Empowering Agents with Imagination for Creative TasksCode1
Answer is All You Need: Instruction-following Text Embedding via Answering the QuestionCode1
Large Language Models as Evaluators for Recommendation ExplanationsCode1
An In-depth Look at Gemini's Language AbilitiesCode1
LASeR: Learning to Adaptively Select Reward Models with Multi-Armed BanditsCode1
Language Imbalance Driven Rewarding for Multilingual Self-improvingCode1
Bridging and Modeling Correlations in Pairwise Data for Direct Preference OptimizationCode1
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space TransformationCode1
Sparkles: Unlocking Chats Across Multiple Images for Multimodal Instruction-Following ModelsCode1
Cross-model Control: Improving Multiple Large Language Models in One-time TrainingCode1
Speech-IFEval: Evaluating Instruction-Following and Quantifying Catastrophic Forgetting in Speech-Aware Language ModelsCode1
Lana: A Language-Capable Navigator for Instruction Following and GenerationCode1
Language-Conditioned Reinforcement Learning to Solve Misunderstandings with Action CorrectionsCode1
Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-TranslationCode1
BotChat: Evaluating LLMs' Capabilities of Having Multi-Turn DialoguesCode1
Do LLMs "know" internally when they follow instructions?Code1
A Dual-Space Framework for General Knowledge Distillation of Large Language ModelsCode1
An Emulator for Fine-Tuning Large Language Models using Small Language ModelsCode1
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical StudyCode1
Jatmo: Prompt Injection Defense by Task-Specific FinetuningCode1
ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language TuningCode1
Diversify and Conquer: Diversity-Centric Data Selection with Iterative RefinementCode1
IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code GeneratorsCode1
Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy InstructionsCode1
IDA-Bench: Evaluating LLMs on Interactive Guided Data AnalysisCode1
Is In-Context Learning Sufficient for Instruction Following in LLMs?Code1
KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language ModelsCode1
DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models—0
Distilling Internet-Scale Vision-Language Models into Embodied Agents—0
Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning—0
Beyond Instruction Following: Evaluating Inferential Rule Following of 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