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 301–325 of 1135 papers

TitleStatusHype
LLM-RG4: Flexible and Factual Radiology Report Generation across Diverse Input ContextsCode2
SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language ModelsCode1
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
PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language ModelsCode0
LLMs for Generalizable Language-Conditioned Policy Learning under Minimal Data Requirements—0
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
RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of ExpertsCode1
Compositional Image Retrieval via Instruction-Aware Contrastive LearningCode0
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
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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