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 351–400 of 1135 papers

TitleStatusHype
Contrastive Vision-Language Alignment Makes Efficient Instruction LearnerCode1
Text as Images: Can Multimodal Large Language Models Follow Printed Instructions in Pixels?Code1
MoDS: Model-oriented Data Selection for Instruction TuningCode1
HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction DataCode1
Towards Improving Document Understanding: An Exploration on Text-Grounding via MLLMsCode1
CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction TuningCode1
DocLens: Multi-aspect Fine-grained Evaluation for Medical Text GenerationCode1
Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable SummarizationCode1
Defending Large Language Models Against Jailbreaking Attacks Through Goal PrioritizationCode1
PLUG: Leveraging Pivot Language in Cross-Lingual Instruction TuningCode1
Self-Evolved Diverse Data Sampling for Efficient Instruction TuningCode1
WaterBench: Towards Holistic Evaluation of Watermarks for Large Language ModelsCode1
InfMLLM: A Unified Framework for Visual-Language TasksCode1
Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small ScorerCode1
u-LLaVA: Unifying Multi-Modal Tasks via Large Language ModelCode1
FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language ModelsCode1
Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy InstructionsCode1
FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language ModelsCode1
Making Large Language Models Better Data CreatorsCode1
Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly DetectionCode1
Instruct and Extract: Instruction Tuning for On-Demand Information ExtractionCode1
AlpaCare:Instruction-tuned Large Language Models for Medical ApplicationCode1
Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst DesignCode1
Democratizing Reasoning Ability: Tailored Learning from Large Language ModelCode1
BotChat: Evaluating LLMs' Capabilities of Having Multi-Turn DialoguesCode1
An Emulator for Fine-Tuning Large Language Models using Small Language ModelsCode1
Evaluating Large Language Models at Evaluating Instruction FollowingCode1
TRACE: A Comprehensive Benchmark for Continual Learning in Large Language ModelsCode1
Understanding the Effects of RLHF on LLM Generalisation and DiversityCode1
Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New LanguagesCode1
Back to the Future: Towards Explainable Temporal Reasoning with Large Language ModelsCode1
Fool Your (Vision and) Language Model With Embarrassingly Simple PermutationsCode1
Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with TransformersCode1
Beyond Task Performance: Evaluating and Reducing the Flaws of Large Multimodal Models with In-Context LearningCode1
From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction TuningCode1
AceGPT, Localizing Large Language Models in ArabicCode1
Natural Language Embedded Programs for Hybrid Language Symbolic ReasoningCode1
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the WildCode1
Are Emergent Abilities in Large Language Models just In-Context Learning?Code1
Sparkles: Unlocking Chats Across Multiple Images for Multimodal Instruction-Following ModelsCode1
Improving Translation Faithfulness of Large Language Models via Augmenting InstructionsCode1
Instruction Position Matters in Sequence Generation with Large Language ModelsCode1
InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4Code1
Context-Aware Planning and Environment-Aware Memory for Instruction Following Embodied AgentsCode1
VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World UseCode1
Self-Alignment with Instruction BacktranslationCode1
Evaluating Correctness and Faithfulness of Instruction-Following Models for Question AnsweringCode1
AlpaGasus: Training A Better Alpaca with Fewer DataCode1
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical StudyCode1
Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generatorsCode1
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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