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 876–900 of 1135 papers

TitleStatusHype
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
Plan, Eliminate, and Track -- Language Models are Good Teachers for Embodied Agents—0
Plug-and-Play Grounding of Reasoning in Multimodal Large Language Models—0
Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision Language Audio and Action—0
Unified Mind Model: Reimagining Autonomous Agents in the LLM Era—0
Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning—0
The Comparative Trap: Pairwise Comparisons Amplifies Biased Preferences of LLM Evaluators—0
Privately Aligning Language Models with Reinforcement Learning—0
Prompt Baking—0
Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following—0
Unleashing Hour-Scale Video Training for Long Video-Language Understanding—0
PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities—0
PUMGPT: A Large Vision-Language Model for Product Understanding—0
BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation—0
Quantifying and Attributing the Hallucination of Large Language Models via Association Analysis—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