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 701–725 of 1135 papers

TitleStatusHype
Open-World Skill Discovery from Unsegmented Demonstrations—0
OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following—0
Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants—0
Optimizing Instruction Synthesis: Effective Exploration of Evolutionary Space with Tree Search—0
Optimizing Latent Goal by Learning from Trajectory Preference—0
OPTune: Efficient Online Preference Tuning—0
Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models—0
Better Instruction-Following Through Minimum Bayes Risk—0
PanGEA: The Panoramic Graph Environment Annotation Toolkit—0
ParamΔ for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost—0
Efficient Prompt Optimization Through the Lens of Best Arm Identification—0
Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis—0
Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models—0
Unbounded: A Generative Infinite Game of Character Life Simulation—0
PartInstruct: Part-level Instruction Following for Fine-grained Robot Manipulation—0
Semantic Skill Grounding for Embodied Instruction-Following in Cross-Domain Environments—0
Pay More Attention to the Robustness of Prompt for Instruction Data Mining—0
Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition and Program of Thought Verification—0
PersianMedQA: Language-Centric Evaluation of LLMs in the Persian Medical Domain—0
UniDoc: A Universal Large Multimodal Model for Simultaneous Text Detection, Recognition, Spotting and Understanding—0
Benchmarking and Improving Generator-Validator Consistency of Language Models—0
PIPA: A Unified Evaluation Protocol for Diagnosing Interactive Planning Agents—0
Pipeline Analysis for Developing Instruct LLMs in Low-Resource Languages: A Case Study on Basque—0
UniEval: Unified Holistic Evaluation for Unified Multimodal Understanding and Generation—0
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs—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