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

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Showing 301–350 of 1135 papers

TitleStatusHype
SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language ModelsCode1
LLM-RG4: Flexible and Factual Radiology Report Generation across Diverse Input ContextsCode2
ChipAlign: Instruction Alignment in Large Language Models for Chip Design via Geodesic Interpolation—0
Leveraging Large Vision-Language Model as User Intent-aware Encoder for Composed Image Retrieval—0
Empowering LLMs to Understand and Generate Complex Vector Graphics—0
VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation—0
EasyRef: Omni-Generalized Group Image Reference for Diffusion Models via Multimodal LLM—0
SmolTulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in SLMs—0
LLaVA-Zip: Adaptive Visual Token Compression with Intrinsic Image Information—0
LLMs for Generalizable Language-Conditioned Policy Learning under Minimal Data Requirements—0
PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language ModelsCode0
Sloth: scaling laws for LLM skills to predict multi-benchmark performance across familiesCode0
KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language ModelsCode1
GROOT-2: Weakly Supervised Multi-Modal Instruction Following Agents—0
Compositional Image Retrieval via Instruction-Aware Contrastive LearningCode0
RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of ExpertsCode1
EXAONE 3.5: Series of Large Language Models for Real-world Use Cases—0
LLM-Align: Utilizing Large Language Models for Entity Alignment in Knowledge Graphs—0
If You Can't Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs—0
VidHalluc: Evaluating Temporal Hallucinations in Multimodal Large Language Models for Video Understanding—0
From Words to Workflows: Automating Business Processes—0
PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following Models Need for Efficient GenerationCode1
Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and DiseasesCode1
Optimizing Latent Goal by Learning from Trajectory Preference—0
T-REG: Preference Optimization with Token-Level Reward RegularizationCode0
AlignFormer: Modality Matching Can Achieve Better Zero-shot Instruction-Following Speech-LLM—0
MiningGPT -- A Domain-Specific Large Language Model for the Mining Industry—0
Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation—0
VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation—0
InsightEdit: Towards Better Instruction Following for Image Editing—0
ShowUI: One Vision-Language-Action Model for GUI Visual AgentCode5
Parameter Efficient Instruction Tuning: An Empirical StudyCode4
Gaussian Scenes: Pose-Free Sparse-View Scene Reconstruction using Depth-Enhanced Diffusion Priors—0
From MTEB to MTOB: Retrieval-Augmented Classification for Descriptive GrammarsCode0
Enhancing Instruction-Following Capability of Visual-Language Models by Reducing Image Redundancy—0
Separable Mixture of Low-Rank Adaptation for Continual Visual Instruction Tuning—0
GeoGround: A Unified Large Vision-Language Model for Remote Sensing Visual GroundingCode2
MpoxVLM: A Vision-Language Model for Diagnosing Skin Lesions from Mpox Virus InfectionCode0
MLAN: Language-Based Instruction Tuning Improves Zero-Shot Generalization of Multimodal Large Language ModelsCode0
Adaptive Decoding via Latent Preference Optimization—0
LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language InterpretationCode2
Zero-shot Object-Centric Instruction Following: Integrating Foundation Models with Traditional Navigation—0
LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context ScenariosCode0
SetLexSem Challenge: Using Set Operations to Evaluate the Lexical and Semantic Robustness of Language ModelsCode1
Stronger Models are NOT Stronger Teachers for Instruction Tuning—0
MrSteve: Instruction-Following Agents in Minecraft with What-Where-When Memory—0
IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference OptimizationCode0
Fox-1 Technical Report—0
Bayesian Calibration of Win Rate Estimation with LLM EvaluatorsCode0
Multi-Reward as Condition for Instruction-based Image Editing—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