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 701–750 of 1135 papers

TitleStatusHype
Conversational Search: From Fundamentals to Frontiers in the LLM Era—0
Space-LLaVA: a Vision-Language Model Adapted to Extraterrestrial Applications—0
Large Language Models for Autonomous Driving (LLM4AD): Concept, Benchmark, Experiments, and Challenges—0
WildChat: 1M ChatGPT Interaction Logs in the Wild—0
Controllable Text-to-Image Generation with GPT-4—0
Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge—0
Learning Human Perception Dynamics for Informative Robot Communication—0
Learning Instruction-Following Policies through Open-Ended Instruction Relabeling with Large Language Models—0
Continual Skill and Task Learning via Dialogue—0
Learning to Instruct for Visual Instruction Tuning—0
Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding—0
Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding—0
Learning to Navigate the Web—0
LearnLM: Improving Gemini for Learning—0
Length Controlled Generation for Black-box LLMs—0
Preference Consistency Matters: Enhancing Preference Learning in Language Models with Automated Self-Curation of Training Corpora—0
Less is More: Generating Grounded Navigation Instructions from Landmarks—0
Compositional Subspace Representation Fine-tuning for Adaptive Large Language Models—0
Leveraging Importance Sampling to Detach Alignment Modules from Large Language Models—0
Leveraging Large Vision-Language Model as User Intent-aware Encoder for Composed Image Retrieval—0
LeVERB: Humanoid Whole-Body Control with Latent Vision-Language Instruction—0
Compositional pre-training for neural semantic parsing—0
LEWIS (LayEr WIse Sparsity) -- A Training Free Guided Model Merging Approach—0
ThinkBot: Embodied Instruction Following with Thought Chain Reasoning—0
Thinking LLMs: General Instruction Following with Thought Generation—0
LIDAO: Towards Limited Interventions for Debiasing (Large) Language Models—0
LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding—0
LIMIT: Less Is More for Instruction Tuning Across Evaluation Paradigms—0
Compositional Instruction Following with Language Models and Reinforcement Learning—0
ThinkLess: A Training-Free Inference-Efficient Method for Reducing Reasoning Redundancy—0
Compositional Data and Task Augmentation for Instruction Following—0
CompBench: Benchmarking Complex Instruction-guided Image Editing—0
Comparison Visual Instruction Tuning—0
LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following—0
LLaMA-Excitor: General Instruction Tuning via Indirect Feature Interaction—0
Collaborative decoding of critical tokens for boosting factuality of large language models—0
ThinkPatterns-21k: A Systematic Study on the Impact of Thinking Patterns in LLMs—0
CoGenesis: A Framework Collaborating Large and Small Language Models for Secure Context-Aware Instruction Following—0
LLaVA-c: Continual Improved Visual Instruction Tuning—0
LLaVA-Critic: Learning to Evaluate Multimodal Models—0
CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation—0
CodecLM: Aligning Language Models with Tailored Synthetic Data—0
LLaVA-Ultra: Large Chinese Language and Vision Assistant for Ultrasound—0
LLaVA-Zip: Adaptive Visual Token Compression with Intrinsic Image Information—0
LLM-AD: Large Language Model based Audio Description System—0
LLM-Align: Utilizing Large Language Models for Entity Alignment in Knowledge Graphs—0
LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?—0
Large Language Model Compression with Neural Architecture Search—0
TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and Generation—0
COCO is "ALL'' You Need for Visual Instruction Fine-tuning—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