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 501–525 of 1135 papers

TitleStatusHype
Order Matters: Investigate the Position Bias in Multi-constraint Instruction FollowingCode0
Semantic Graphs for Syntactic Simplification: A Revisit from the Age of LLMCode0
GoalNet: Inferring Conjunctive Goal Predicates from Human Plan Demonstrations for Robot Instruction FollowingCode0
Automated curriculum generation for Policy Gradients from DemonstrationsCode0
Generative Visual Instruction TuningCode0
Continual Learning for Instruction Following from Realtime FeedbackCode0
On the Loss of Context-awareness in General Instruction Fine-tuningCode0
Generalization Analogies: A Testbed for Generalizing AI Oversight to Hard-To-Measure DomainsCode0
A Course Correction in Steerability Evaluation: Revealing Miscalibration and Side Effects in LLMsCode0
Repairs in a Block World: A New Benchmark for Handling User Corrections with Multi-Modal Language ModelsCode0
Compositionality as Lexical SymmetryCode0
Phased Instruction Fine-Tuning for Large Language ModelsCode0
Compositional Image Retrieval via Instruction-Aware Contrastive LearningCode0
MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive TrainingCode0
Aligners: Decoupling LLMs and AlignmentCode0
From MTEB to MTOB: Retrieval-Augmented Classification for Descriptive GrammarsCode0
Multi-Level Compositional Reasoning for Interactive Instruction FollowingCode0
NatSGLD: A Dataset with Speech, Gesture, Logic, and Demonstration for Robot Learning in Natural Human-Robot InteractionCode0
From Loops to Oops: Fallback Behaviors of Language Models Under UncertaintyCode0
CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data PartitionsCode0
Monolingual or Multilingual Instruction Tuning: Which Makes a Better AlpacaCode0
Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to GiantCode0
MpoxVLM: A Vision-Language Model for Diagnosing Skin Lesions from Mpox Virus InfectionCode0
Toward Zero-Shot Instruction FollowingCode0
Align^2LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction CurationCode0
Show:102550
← PrevPage 21 of 46Next →

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