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 501–550 of 1135 papers

TitleStatusHype
Beyond Content Relevance: Evaluating Instruction Following in Retrieval ModelsCode0
ProgCo: Program Helps Self-Correction of Large Language ModelsCode0
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
Preference-Guided Reflective Sampling for Aligning Language ModelsCode0
Generalization Analogies: A Testbed for Generalizing AI Oversight to Hard-To-Measure DomainsCode0
POROver: Improving Safety and Reducing Overrefusal in Large Language Models with Overgeneration and Preference OptimizationCode0
Playpen: An Environment for Exploring Learning Through Conversational InteractionCode0
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
Policy Improvement using Language Feedback ModelsCode0
Pre-Learning Environment Representations for Data-Efficient Neural Instruction FollowingCode0
Rate, Explain and Cite (REC): Enhanced Explanation and Attribution in Automatic Evaluation by Large Language ModelsCode0
Compositional Image Retrieval via Instruction-Aware Contrastive LearningCode0
Order Matters: Investigate the Position Bias in Multi-constraint Instruction FollowingCode0
Aligners: Decoupling LLMs and AlignmentCode0
From MTEB to MTOB: Retrieval-Augmented Classification for Descriptive GrammarsCode0
Optimal Transport-Based Token Weighting scheme for Enhanced Preference OptimizationCode0
From Loops to Oops: Fallback Behaviors of Language Models Under UncertaintyCode0
CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data PartitionsCode0
On the Loss of Context-awareness in General Instruction Fine-tuningCode0
Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to GiantCode0
PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language ModelsCode0
Toward Zero-Shot Instruction FollowingCode0
Align^2LLaVA: Cascaded Human and Large Language Model Preference Alignment for Multi-modal Instruction CurationCode0
CoEvol: Constructing Better Responses for Instruction Finetuning through Multi-Agent CooperationCode0
NatSGLD: A Dataset with Speech, Gesture, Logic, and Demonstration for Robot Learning in Natural Human-Robot InteractionCode0
FMDLlama: Financial Misinformation Detection based on Large Language ModelsCode0
MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive TrainingCode0
MpoxVLM: A Vision-Language Model for Diagnosing Skin Lesions from Mpox Virus InfectionCode0
CoDe: Blockwise Control for Denoising Diffusion ModelsCode0
Find the Intention of Instruction: Comprehensive Evaluation of Instruction Understanding for Large Language ModelsCode0
CoDa: Constrained Generation based Data Augmentation for Low-Resource NLPCode0
FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, ToxicityCode0
Multi-Level Compositional Reasoning for Interactive Instruction FollowingCode0
Empowering Persian LLMs for Instruction Following: A Novel Dataset and Training ApproachCode0
Monolingual or Multilingual Instruction Tuning: Which Makes a Better AlpacaCode0
FALCON: Feedback-driven Adaptive Long/short-term memory reinforced Coding Optimization systemCode0
ASMA-Tune: Unlocking LLMs' Assembly Code Comprehension via Structural-Semantic Instruction TuningCode0
Mitigating the Bias of Large Language Model EvaluationCode0
MLAN: Language-Based Instruction Tuning Improves Zero-Shot Generalization of Multimodal Large Language ModelsCode0
A safety realignment framework via subspace-oriented model fusion for large language modelsCode0
MIMO: A Medical Vision Language Model with Visual Referring Multimodal Input and Pixel Grounding Multimodal OutputCode0
MM-R5: MultiModal Reasoning-Enhanced ReRanker via Reinforcement Learning for Document RetrievalCode0
Phased Instruction Fine-Tuning for Large Language ModelsCode0
MDCure: A Scalable Pipeline for Multi-Document Instruction-FollowingCode0
Evaluating the Instruction-following Abilities of Language Models using Knowledge TasksCode0
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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