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 901–950 of 1135 papers

TitleStatusHype
Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations—0
Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking—0
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning—0
FLAME: Factuality-Aware Alignment for Large Language Models—0
Don't Half-listen: Capturing Key-part Information in Continual Instruction Tuning—0
Domain Adaptation of VLM for Soccer Video Understanding—0
FlowKV: Enhancing Multi-Turn Conversational Coherence in LLMs via Isolated Key-Value Cache Management—0
GuideBench: Benchmarking Domain-Oriented Guideline Following for LLM Agents—0
FollowEval: A Multi-Dimensional Benchmark for Assessing the Instruction-Following Capability of Large Language Models—0
StyleAR: Customizing Multimodal Autoregressive Model for Style-Aligned Text-to-Image Generation—0
Following Instructions by Imagining and Reaching Visual Goals—0
Following Length Constraints in Instructions—0
Summarizing a virtual robot's past actions in natural language—0
ForgeryGPT: Multimodal Large Language Model For Explainable Image Forgery Detection and Localization—0
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption—0
Fox-1 Technical Report—0
From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models—0
From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data—0
From “Before” to “After”: Generating Natural Language Instructions from Image Pairs in a Simple Visual Domain—0
Superficial Safety Alignment Hypothesis—0
From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following—0
From Persona to Personalization: A Survey on Role-Playing Language Agents—0
Surgical-LLaVA: Toward Surgical Scenario Understanding via Large Language and Vision Models—0
From Role-Play to Drama-Interaction: An LLM Solution—0
From Words to Workflows: Automating Business Processes—0
Frustrated with Code Quality Issues? LLMs can Help!—0
Who Taught You That? Tracing Teachers in Model Distillation—0
DNA 1.0 Technical Report—0
Diversity Measurement and Subset Selection for Instruction Tuning Datasets—0
Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis—0
Diverse and Fine-Grained Instruction-Following Ability Exploration with Synthetic Data—0
Gaussian Scenes: Pose-Free Sparse-View Scene Reconstruction using Depth-Enhanced Diffusion Priors—0
Gemma 3 Technical Report—0
DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models—0
Distilling Internet-Scale Vision-Language Models into Embodied Agents—0
Generalization in Instruction Following Systems—0
Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?—0
Generate Subgoal Images before Act: Unlocking the Chain-of-Thought Reasoning in Diffusion Model for Robot Manipulation with Multimodal Prompts—0
Conversational Code Generation: a Case Study of Designing a Dialogue System for Generating Driving Scenarios for Testing Autonomous Vehicles—0
SWITCH: Studying with Teacher for Knowledge Distillation of Large Language Models—0
Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning—0
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models—0
Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control—0
Got Compute, but No Data: Lessons From Post-training a Finnish LLM—0
Disentangling Length Bias In Preference Learning Via Response-Conditioned Modeling—0
GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation—0
DiscreteSLU: A Large Language Model with Self-Supervised Discrete Speech Units for Spoken Language Understanding—0
Griffon-G: Bridging Vision-Language and Vision-Centric Tasks via Large Multimodal Models—0
GROOT-2: Weakly Supervised Multi-Modal Instruction Following Agents—0
GROOT: Learning to Follow Instructions by Watching Gameplay Videos—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