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 501–550 of 1135 papers

TitleStatusHype
CASTILLO: Characterizing Response Length Distributions of Large Language ModelsCode0
ToDi: Token-wise Distillation via Fine-Grained Divergence Control—0
ManipLVM-R1: Reinforcement Learning for Reasoning in Embodied Manipulation with Large Vision-Language Models—0
LIFEBench: Evaluating Length Instruction Following in Large Language ModelsCode0
Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective—0
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought—0
ThinkLess: A Training-Free Inference-Efficient Method for Reducing Reasoning Redundancy—0
FlowKV: Enhancing Multi-Turn Conversational Coherence in LLMs via Isolated Key-Value Cache Management—0
Joint Flashback Adaptation for Forgetting-Resistant Instruction Tuning—0
Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training—0
DecIF: Improving Instruction-Following through Meta-Decomposition—0
Ground-V: Teaching VLMs to Ground Complex Instructions in Pixels—0
Domain Adaptation of VLM for Soccer Video Understanding—0
Multi-Level Aware Preference Learning: Enhancing RLHF for Complex Multi-Instruction Tasks—0
Rethinking Predictive Modeling for LLM Routing: When Simple kNN Beats Complex Learned Routers—0
What Prompts Don't Say: Understanding and Managing Underspecification in LLM PromptsCode0
Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers—0
KIT's Offline Speech Translation and Instruction Following Submission for IWSLT 2025—0
CompBench: Benchmarking Complex Instruction-guided Image Editing—0
Enhancing Complex Instruction Following for Large Language Models with Mixture-of-Contexts Fine-tuning—0
Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution BehaviorsCode0
Navigating the Alpha Jungle: An LLM-Powered MCTS Framework for Formulaic Factor Mining—0
HelpSteer3-Preference: Open Human-Annotated Preference Data across Diverse Tasks and Languages—0
When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMs—0
GuideBench: Benchmarking Domain-Oriented Guideline Following for LLM Agents—0
UniEval: Unified Holistic Evaluation for Unified Multimodal Understanding and Generation—0
Tests as Prompt: A Test-Driven-Development Benchmark for LLM Code Generation—0
Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning?Code0
Efficient Telecom Specific LLM: TSLAM-Mini with QLoRA and Digital Twin Data—0
Assessing Robustness to Spurious Correlations in Post-Training Language Models—0
T2VTextBench: A Human Evaluation Benchmark for Textual Control in Video Generation Models—0
Incentivizing Inclusive Contributions in Model Sharing Markets—0
PIPA: A Unified Evaluation Protocol for Diagnosing Interactive Planning Agents—0
T2VPhysBench: A First-Principles Benchmark for Physical Consistency in Text-to-Video Generation—0
UAV-VLN: End-to-End Vision Language guided Navigation for UAVs—0
Ask, Fail, Repeat: Meeseeks, an Iterative Feedback Benchmark for LLMs' Multi-turn Instruction-Following Ability—0
TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language ModelsCode0
CachePrune: Neural-Based Attribution Defense Against Indirect Prompt Injection Attacks—0
Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs—0
ManipDreamer: Boosting Robotic Manipulation World Model with Action Tree and Visual Guidance—0
ParamΔ for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost—0
Case Study: Fine-tuning Small Language Models for Accurate and Private CWE Detection in Python Code—0
DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models—0
Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling EvaluatorsCode0
Improving Instruct Models for Free: A Study on Partial Adaptation—0
SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning—0
Playpen: An Environment for Exploring Learning Through Conversational InteractionCode0
VideoExpert: Augmented LLM for Temporal-Sensitive Video Understanding—0
Capybara-OMNI: An Efficient Paradigm for Building Omni-Modal Language Models—0
Holistic Capability Preservation: Towards Compact Yet Comprehensive Reasoning Models—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