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 651–700 of 1135 papers

TitleStatusHype
SLADE: Shielding against Dual Exploits in Large Vision-Language Models—0
HSI-GPT: A General-Purpose Large Scene-Motion-Language Model for Human Scene Interaction—0
Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following—0
Find the Intention of Instruction: Comprehensive Evaluation of Instruction Understanding for Large Language ModelsCode0
Internalized Self-Correction for Large Language Models—0
LearnLM: Improving Gemini for Learning—0
HREF: Human Response-Guided Evaluation of Instruction Following in Language ModelsCode0
Systematic Evaluation of Long-Context LLMs on Financial Concepts—0
Length Controlled Generation for Black-box LLMs—0
Pipeline Analysis for Developing Instruct LLMs in Low-Resource Languages: A Case Study on Basque—0
MetaMorph: Multimodal Understanding and Generation via Instruction Tuning—0
A Systematic Examination of Preference Learning through the Lens of Instruction-Following—0
Question: How do Large Language Models perform on the Question Answering tasks? Answer:—0
LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-SteeringCode0
Empowering LLMs to Understand and Generate Complex Vector Graphics—0
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
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
LLaVA-Zip: Adaptive Visual Token Compression with Intrinsic Image Information—0
SmolTulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in SLMs—0
Sloth: scaling laws for LLM skills to predict multi-benchmark performance across familiesCode0
PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language ModelsCode0
LLMs for Generalizable Language-Conditioned Policy Learning under Minimal Data Requirements—0
GROOT-2: Weakly Supervised Multi-Modal Instruction Following Agents—0
Compositional Image Retrieval via Instruction-Aware Contrastive LearningCode0
LLM-Align: Utilizing Large Language Models for Entity Alignment in Knowledge Graphs—0
EXAONE 3.5: Series of Large Language Models for Real-world Use Cases—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
Optimizing Latent Goal by Learning from Trajectory Preference—0
T-REG: Preference Optimization with Token-Level Reward RegularizationCode0
Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation—0
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
VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation—0
InsightEdit: Towards Better Instruction Following for Image Editing—0
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
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
Zero-shot Object-Centric Instruction Following: Integrating Foundation Models with Traditional Navigation—0
Stronger Models are NOT Stronger Teachers for Instruction Tuning—0
MrSteve: Instruction-Following Agents in Minecraft with What-Where-When Memory—0
LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context ScenariosCode0
IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization—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