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 476–500 of 1135 papers

TitleStatusHype
How You Prompt Matters! Even Task-Oriented Constraints in Instructions Affect LLM-Generated Text DetectionCode0
How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their VulnerabilitiesCode0
How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning PerspectiveCode0
Phased Instruction Fine-Tuning for Large Language ModelsCode0
Policy Improvement using Language Feedback ModelsCode0
Cuckoo: An IE Free Rider Hatched by Massive Nutrition in LLM's NestCode0
CS4: Measuring the Creativity of Large Language Models Automatically by Controlling the Number of Story-Writing ConstraintsCode0
AdaPPA: Adaptive Position Pre-Fill Jailbreak Attack Approach Targeting LLMsCode0
PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language ModelsCode0
POROver: Improving Safety and Reducing Overrefusal in Large Language Models with Overgeneration and Preference OptimizationCode0
Hierarchical Modular Framework for Long Horizon Instruction FollowingCode0
Order Matters: Investigate the Position Bias in Multi-constraint Instruction FollowingCode0
Cross-lingual Transfer of Reward Models in Multilingual AlignmentCode0
Optimal Transport-Based Token Weighting scheme for Enhanced Preference OptimizationCode0
Alignment-based compositional semantics for instruction followingCode0
HalLoc: Token-level Localization of Hallucinations for Vision Language ModelsCode0
Guiding Policies with Language via Meta-LearningCode0
Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language ModelsCode0
On the Loss of Context-awareness in General Instruction Fine-tuningCode0
Aligning Large Language Models by On-Policy Self-JudgmentCode0
CORDIAL: Can Multimodal Large Language Models Effectively Understand Coherence Relationships?Code0
Grade Score: Quantifying LLM Performance in Option SelectionCode0
MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive TrainingCode0
GoalNet: Inferring Conjunctive Goal Predicates from Human Plan Demonstrations for Robot Instruction FollowingCode0
Automated curriculum generation for Policy Gradients from DemonstrationsCode0
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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