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 701–725 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
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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