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 776–800 of 1135 papers

TitleStatusHype
SelectLLM: Can LLMs Select Important Instructions to Annotate?Code1
EAGLE: Speculative Sampling Requires Rethinking Feature UncertaintyCode7
F-Eval: Assessing Fundamental Abilities with Refined Evaluation MethodsCode1
Towards 3D Molecule-Text Interpretation in Language ModelsCode2
Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-AlignmentCode3
AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents—0
Self-Rewarding Language ModelsCode1
SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language ModelCode2
COCO is "ALL'' You Need for Visual Instruction Fine-tuning—0
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective AnalysisCode2
PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities—0
Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-TranslationCode1
InFoBench: Evaluating Instruction Following Ability in Large Language ModelsCode2
Human-Instruction-Free LLM Self-Alignment with Limited Samples—0
Incorporating Visual Experts to Resolve the Information Loss in Multimodal Large Language Models—0
ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction TuningCode2
LLaMA Pro: Progressive LLaMA with Block ExpansionCode4
Multilingual Instruction Tuning With Just a Pinch of Multilinguality—0
SSP: A Simple and Safe automatic Prompt engineering method towards realistic image synthesis on LVM—0
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action—0
Generate Subgoal Images before Act: Unlocking the Chain-of-Thought Reasoning in Diffusion Model for Robot Manipulation with Multimodal Prompts—0
MAPLM: A Real-World Large-Scale Vision-Language Benchmark for Map and Traffic Scene UnderstandingCode2
Benchmarking Large Language Models on Controllable Generation under Diversified InstructionsCode1
Jatmo: Prompt Injection Defense by Task-Specific FinetuningCode1
Visual Instruction Tuning towards General-Purpose Multimodal Model: A Survey—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