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
RELIC: Evaluating Compositional Instruction Following via Language Recognition—0
Unleashing Hour-Scale Video Training for Long Video-Language Understanding—0
SeedEdit 3.0: Fast and High-Quality Generative Image Editing—0
On the Mechanism of Reasoning Pattern Selection in Reinforcement Learning for Language Models—0
Robust Anti-Backdoor Instruction Tuning in LVLMs—0
MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching—0
MoDA: Modulation Adapter for Fine-Grained Visual Grounding in Instructional MLLMs—0
TIIF-Bench: How Does Your T2I Model Follow Your Instructions?—0
PersianMedQA: Language-Centric Evaluation of LLMs in the Persian Medical Domain—0
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
Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing—0
LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents—0
PartInstruct: Part-level Instruction Following for Fine-grained Robot Manipulation—0
A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMsCode0
StyleAR: Customizing Multimodal Autoregressive Model for Style-Aligned Text-to-Image Generation—0
From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data—0
Leveraging Importance Sampling to Detach Alignment Modules from Large Language Models—0
Evaluating Robustness of Large Audio Language Models to Audio Injection: An Empirical Study—0
RECAST: Strengthening LLMs' Complex Instruction Following with Constraint-Verifiable Data—0
Optimal Transport-Based Token Weighting scheme for Enhanced Preference OptimizationCode0
MIDB: Multilingual Instruction Data Booster for Enhancing Multilingual Instruction Synthesis—0
In-Context Watermarks for Large Language Models—0
Sparse Activation Editing for Reliable Instruction Following in Narratives—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