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 1076–1100 of 1135 papers

TitleStatusHype
LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction FollowingCode0
Chasing Ghosts: Instruction Following as Bayesian State TrackingCode0
Repairs in a Block World: A New Benchmark for Handling User Corrections with Multi-Modal Language ModelsCode0
Evaluating the Instruction-Following Robustness of Large Language Models to Prompt InjectionCode0
Language as an Abstraction for Hierarchical Deep Reinforcement LearningCode0
Language-Conditioned Change-point Detection to Identify Sub-Tasks in Robotics DomainsCode0
URL: Universal Referential Knowledge Linking via Task-instructed Representation CompressionCode0
A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following AgentsCode0
Grade Score: Quantifying LLM Performance in Option SelectionCode0
Order Matters: Investigate the Position Bias in Multi-constraint Instruction FollowingCode0
Don't Copy the Teacher: Data and Model Challenges in Embodied DialogueCode0
Beyond Content Relevance: Evaluating Instruction Following in Retrieval ModelsCode0
CASTILLO: Characterizing Response Length Distributions of Large Language ModelsCode0
HREF: Human Response-Guided Evaluation of Instruction Following in Language ModelsCode0
MIMO: A Medical Vision Language Model with Visual Referring Multimodal Input and Pixel Grounding Multimodal OutputCode0
Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual SettingsCode0
CORDIAL: Can Multimodal Large Language Models Effectively Understand Coherence Relationships?Code0
From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language ModelsCode0
FMDLlama: Financial Misinformation Detection based on Large Language ModelsCode0
X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in ClassificationCode0
Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation LearningCode0
Mitigating the Bias of Large Language Model EvaluationCode0
Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal LanguagesCode0
Self-Judge: Selective Instruction Following with Alignment Self-EvaluationCode0
Learning by Correction: Efficient Tuning Task for Zero-Shot Generative Vision-Language ReasoningCode0
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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