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 651–675 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
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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