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 51–75 of 1135 papers

TitleStatusHype
When Large Multimodal Models Confront Evolving Knowledge:Challenges and PathwaysCode2
Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space TransformationCode1
Differential Information: An Information-Theoretic Perspective on Preference Optimization—0
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering—0
ARC: Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs—0
LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents—0
Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing—0
Let Them Talk: Audio-Driven Multi-Person Conversational Video GenerationCode7
A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMsCode0
PartInstruct: Part-level Instruction Following for Fine-grained Robot Manipulation—0
Leveraging Importance Sampling to Detach Alignment Modules from Large Language Models—0
From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data—0
Evaluating Robustness of Large Audio Language Models to Audio Injection: An Empirical Study—0
StyleAR: Customizing Multimodal Autoregressive Model for Style-Aligned Text-to-Image Generation—0
RECAST: Strengthening LLMs' Complex Instruction Following with Constraint-Verifiable Data—0
Speech-IFEval: Evaluating Instruction-Following and Quantifying Catastrophic Forgetting in Speech-Aware Language ModelsCode1
STRICT: Stress Test of Rendering Images Containing TextCode1
Optimal Transport-Based Token Weighting scheme for Enhanced Preference OptimizationCode0
OmniGenBench: A Benchmark for Omnipotent Multimodal Generation across 50+ TasksCode1
MIDB: Multilingual Instruction Data Booster for Enhancing Multilingual Instruction Synthesis—0
IDA-Bench: Evaluating LLMs on Interactive Guided Data AnalysisCode1
LIFEBench: Evaluating Length Instruction Following in Large Language ModelsCode0
CASTILLO: Characterizing Response Length Distributions of Large Language ModelsCode0
In-Context Watermarks for Large Language Models—0
IFEval-Audio: Benchmarking Instruction-Following Capability in Audio-based Large Language ModelsCode3
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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