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 351–375 of 1135 papers

TitleStatusHype
On the Exploitability of Instruction TuningCode1
Improving Translation Faithfulness of Large Language Models via Augmenting InstructionsCode1
ChartInstruct: Instruction Tuning for Chart Comprehension and ReasoningCode1
Incentivizing Reasoning for Advanced Instruction-Following of Large Language ModelsCode1
DocLens: Multi-aspect Fine-grained Evaluation for Medical Text GenerationCode1
CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code GenerationCode1
Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction FollowingCode1
Instruct and Extract: Instruction Tuning for On-Demand Information ExtractionCode1
Enhancing Cross-Tokenizer Knowledge Distillation with Contextual Dynamical MappingCode1
CB2: Collaborative Natural Language Interaction Research PlatformCode1
Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form PlanningCode1
Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation SystemsCode1
NPHardEval4V: A Dynamic Reasoning Benchmark of Multimodal Large Language ModelsCode1
OmniGenBench: A Benchmark for Omnipotent Multimodal Generation across 50+ TasksCode1
On the Multi-turn Instruction Following for Conversational Web AgentsCode1
Engineering flexible machine learning systems by traversing functionally-invariant pathsCode1
Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly DetectionCode1
Natural Language Embedded Programs for Hybrid Language Symbolic ReasoningCode1
Are Emergent Abilities in Large Language Models just In-Context Learning?Code1
Hybrid Alignment Training for Large Language ModelsCode1
MergeBench: A Benchmark for Merging Domain-Specialized LLMsCode1
IDA-Bench: Evaluating LLMs on Interactive Guided Data AnalysisCode1
Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated FlightCode1
ReALFRED: An Embodied Instruction Following Benchmark in Photo-Realistic EnvironmentsCode1
Multi-modal Preference Alignment Remedies Degradation of Visual Instruction Tuning on Language ModelsCode1
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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