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

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Showing 226–250 of 1135 papers

TitleStatusHype
Defending Large Language Models against Jailbreak Attacks via Semantic SmoothingCode1
Lexicon Learning for Few-Shot Neural Sequence ModelingCode1
Defending Large Language Models Against Jailbreaking Attacks Through Goal PrioritizationCode1
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate HallucinationsCode1
LASeR: Learning to Adaptively Select Reward Models with Multi-Armed BanditsCode1
AlpaGasus: Training A Better Alpaca with Fewer DataCode1
Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated FlightCode1
DANLI: Deliberative Agent for Following Natural Language InstructionsCode1
Demystifying Domain-adaptive Post-training for Financial LLMsCode1
AlpaCare:Instruction-tuned Large Language Models for Medical ApplicationCode1
GIE-Bench: Towards Grounded Evaluation for Text-Guided Image EditingCode1
Large Language Models as Evaluators for Recommendation ExplanationsCode1
DialFRED: Dialogue-Enabled Agents for Embodied Instruction FollowingCode1
Lana: A Language-Capable Navigator for Instruction Following and GenerationCode1
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
Language-Conditioned Reinforcement Learning to Solve Misunderstandings with Action CorrectionsCode1
EarthMarker: A Visual Prompting Multi-modal Large Language Model for Remote SensingCode1
AllenAct: A Framework for Embodied AI ResearchCode1
CrowdSelect: Synthetic Instruction Data Selection with Multi-LLM WisdomCode1
Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank AdaptationCode1
Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-TranslationCode1
Language Imbalance Driven Rewarding for Multilingual Self-improvingCode1
Cross-model Control: Improving Multiple Large Language Models in One-time TrainingCode1
Back to the Future: Towards Explainable Temporal Reasoning with Large Language ModelsCode1
Zero-Shot Compositional Policy Learning via Language GroundingCode1
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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