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 301–310 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
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
PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language ModelsCode0
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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