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 1101–1125 of 1135 papers

TitleStatusHype
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
TIIF-Bench: How Does Your T2I Model Follow Your Instructions?—0
Towards LLM-guided Causal Explainability for Black-box Text Classifiers—0
LLMs can be easily Confused by Instructional Distractions—0
LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints—0
LLMs for Generalizable Language-Conditioned Policy Learning under Minimal Data Requirements—0
CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction Following—0
Closed-Loop Open-Vocabulary Mobile Manipulation with GPT-4V—0
CLIP-Nav: Using CLIP for Zero-Shot Vision-and-Language Navigation—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