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 501–550 of 1135 papers

TitleStatusHype
Instruction-Following Pruning for Large Language Models—0
Instruction-Following Speech Recognition—0
Space-LLaVA: a Vision-Language Model Adapted to Extraterrestrial Applications—0
DataMan: Data Manager for Pre-training Large Language Models—0
LLaVA-Critic: Learning to Evaluate Multimodal Models—0
ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control—0
Data Extraction Attacks in Retrieval-Augmented Generation via Backdoors—0
VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought—0
HyperCLOVA X Technical Report—0
Data Diversity Matters for Robust Instruction Tuning—0
Hypencoder: Hypernetworks for Information Retrieval—0
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought—0
Human-Instruction-Free LLM Self-Alignment with Limited Samples—0
DAFE: LLM-Based Evaluation Through Dynamic Arbitration for Free-Form Question-Answering—0
Towards LLM-guided Causal Explainability for Black-box Text Classifiers—0
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text—0
HSI-GPT: A General-Purpose Large Scene-Motion-Language Model for Human Scene Interaction—0
D3: Diversity, Difficulty, and Dependability-Aware Data Selection for Sample-Efficient LLM Instruction Tuning—0
BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation—0
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment—0
LLaMA-Excitor: General Instruction Tuning via Indirect Feature Interaction—0
How well can LLMs Grade Essays in Arabic?—0
BAP v2: An Enhanced Task Framework for Instruction Following in Minecraft Dialogues—0
How Many Instructions Can LLMs Follow at Once?—0
LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following—0
How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignment—0
Balancing Continuous Pre-Training and Instruction Fine-Tuning: Optimizing Instruction-Following in LLMs—0
HelpSteer3-Preference: Open Human-Annotated Preference Data across Diverse Tasks and Languages—0
Holistic Capability Preservation: Towards Compact Yet Comprehensive Reasoning Models—0
Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models—0
Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following—0
Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language—0
Active Reasoning in an Open-World Environment—0
HERM: Benchmarking and Enhancing Multimodal LLMs for Human-Centric Understanding—0
HELPER-X: A Unified Instructable Embodied Agent to Tackle Four Interactive Vision-Language Domains with Memory-Augmented Language Models—0
SteP: Stacked LLM Policies for Web Actions—0
HAPFI: History-Aware Planning based on Fused Information—0
CROME: Cross-Modal Adapters for Efficient Multimodal LLM—0
Aligning Text, Images, and 3D Structure Token-by-Token—0
Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination—0
Guided Adaptive Credit Assignment for Sample Efficient Policy Optimization—0
Ground-V: Teaching VLMs to Ground Complex Instructions in Pixels—0
Creating Arabic LLM Prompts at Scale—0
Ground-level Viewpoint Vision-and-Language Navigation in Continuous Environments—0
Language Conditioned Imitation Learning over Unstructured Data—0
AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents—0
Grounding Language by Continuous Observation of Instruction Following—0
CoTBal: Comprehensive Task Balancing for Multi-Task Visual Instruction Tuning—0
GROOT: Learning to Follow Instructions by Watching Gameplay Videos—0
GROOT-2: Weakly Supervised Multi-Modal Instruction Following Agents—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