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 251–275 of 1135 papers

TitleStatusHype
KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language ModelsCode1
RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of ExpertsCode1
PrefixKV: Adaptive Prefix KV Cache is What Vision Instruction-Following Models Need for Efficient GenerationCode1
Agri-LLaVA: Knowledge-Infused Large Multimodal Assistant on Agricultural Pests and DiseasesCode1
SetLexSem Challenge: Using Set Operations to Evaluate the Lexical and Semantic Robustness of Language ModelsCode1
Constraint Back-translation Improves Complex Instruction Following of Large Language ModelsCode1
LLaMo: Large Language Model-based Molecular Graph AssistantCode1
DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate HallucinationsCode1
Cross-model Control: Improving Multiple Large Language Models in One-time TrainingCode1
ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language TuningCode1
GATEAU: Selecting Influential Samples for Long Context AlignmentCode1
LoGU: Long-form Generation with Uncertainty ExpressionsCode1
Do LLMs "know" internally when they follow instructions?Code1
RuleRAG: Rule-guided retrieval-augmented generation with language models for question answeringCode1
Language Imbalance Driven Rewarding for Multilingual Self-improvingCode1
CoPESD: A Multi-Level Surgical Motion Dataset for Training Large Vision-Language Models to Co-Pilot Endoscopic Submucosal DissectionCode1
Reward-Augmented Data Enhances Direct Preference Alignment of LLMsCode1
A Recipe For Building a Compliant Real Estate ChatbotCode1
LASeR: Learning to Adaptively Select Reward Models with Multi-Armed BanditsCode1
MedQA-CS: Benchmarking Large Language Models Clinical Skills Using an AI-SCE FrameworkCode1
Ruler: A Model-Agnostic Method to Control Generated Length for Large Language ModelsCode1
Infer Human's Intentions Before Following Natural Language InstructionsCode1
EventHallusion: Diagnosing Event Hallucinations in Video LLMsCode1
MM-CamObj: A Comprehensive Multimodal Dataset for Camouflaged Object ScenariosCode1
ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and FeedbackCode1
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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