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 801–850 of 1135 papers

TitleStatusHype
HelpSteer3-Preference: Open Human-Annotated Preference Data across Diverse Tasks and Languages—0
"Are you telling me to put glasses on the dog?'' Content-Grounded Annotation of Instruction Clarification Requests in the CoDraw Dataset—0
X-VILA: Cross-Modality Alignment for Large Language Model—0
SeedEdit 3.0: Fast and High-Quality Generative Image Editing—0
Are You Human? An Adversarial Benchmark to Expose LLMs—0
Vision-Language Models Provide Promptable Representations for Reinforcement Learning—0
Are We There Yet? Learning to Localize in Embodied Instruction Following—0
VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation—0
Self-Boosting Large Language Models with Synthetic Preference Data—0
Self-Corrected Multimodal Large Language Model for End-to-End Robot Manipulation—0
Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning—0
Self-Educated Language Agent with Hindsight Experience Replay for Instruction Following—0
HIGhER : Improving instruction following with Hindsight Generation for Experience Replay—0
VisualCritic: Making LMMs Perceive Visual Quality Like Humans—0
ARC: Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs—0
Visual Fact Checker: Enabling High-Fidelity Detailed Caption Generation—0
AnyCap Project: A Unified Framework, Dataset, and Benchmark for Controllable Omni-modal Captioning—0
An Incomplete Loop: Deductive, Inductive, and Abductive Learning in Large Language Models—0
When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMs—0
Self-Specialization: Uncovering Latent Expertise within Large Language Models—0
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models—0
Separable Mixture of Low-Rank Adaptation for Continual Visual Instruction Tuning—0
Separator Injection Attack: Uncovering Dialogue Biases in Large Language Models Caused by Role Separators—0
Sequence-level Large Language Model Training with Contrastive Preference Optimization—0
SeRA: Self-Reviewing and Alignment of Large Language Models using Implicit Reward Margins—0
Visual Instruction Tuning towards General-Purpose Multimodal Model: A Survey—0
SFR-RAG: Towards Contextually Faithful LLMs—0
SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe—0
A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learning—0
Analyzing Multilingual Competency of LLMs in Multi-Turn Instruction Following: A Case Study of Arabic—0
SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning—0
SIFToM: Robust Spoken Instruction Following through Theory of Mind—0
A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction—0
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains—0
Situated Instruction Following—0
Sketch-Plan-Generalize: Learning and Planning with Neuro-Symbolic Programmatic Representations for Inductive Spatial Concepts—0
Skill Induction and Planning with Latent Language—0
Alzheimer's Dementia Detection Using Perplexity from Paired Large Language Models—0
SLADE: Shielding against Dual Exploits in Large Vision-Language Models—0
SlideChat: A Large Vision-Language Assistant for Whole-Slide Pathology Image Understanding—0
SLM: Bridge the thin gap between speech and text foundation models—0
Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks—0
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model—0
SmolTulu: Higher Learning Rate to Batch Size Ratios Can Lead to Better Reasoning in SLMs—0
Socratic Planner: Self-QA-Based Zero-Shot Planning for Embodied Instruction Following—0
Aligning Text, Images, and 3D Structure Token-by-Token—0
Sorted LLaMA: Unlocking the Potential of Intermediate Layers of Large Language Models for Dynamic Inference—0
VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation—0
EAGLE: Towards Efficient Arbitrary Referring Visual Prompts Comprehension for Multimodal Large Language Models—0
E2LVLM:Evidence-Enhanced Large Vision-Language Model for Multimodal Out-of-Context Misinformation Detection—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