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 601–650 of 1135 papers

TitleStatusHype
MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive TrainingCode0
How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning PerspectiveCode0
How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their VulnerabilitiesCode0
Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation PredictionCode0
How You Prompt Matters! Even Task-Oriented Constraints in Instructions Affect LLM-Generated Text DetectionCode0
HREF: Human Response-Guided Evaluation of Instruction Following in Language ModelsCode0
IWISDM: Assessing instruction following in multimodal models at scaleCode0
A Framework for Fine-Tuning LLMs using Heterogeneous Feedback—0
M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation—0
Magistral—0
Active Reasoning in an Open-World Environment—0
ManipDreamer: Boosting Robotic Manipulation World Model with Action Tree and Visual Guidance—0
ManipLVM-R1: Reinforcement Learning for Reasoning in Embodied Manipulation with Large Vision-Language Models—0
ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning—0
MAP's not dead yet: Uncovering true language model modes by conditioning away degeneracy—0
MART: Improving LLM Safety with Multi-round Automatic Red-Teaming—0
MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching—0
Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning—0
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records—0
ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities—0
MedXChat: A Unified Multimodal Large Language Model Framework towards CXRs Understanding and Generation—0
Ask, Fail, Repeat: Meeseeks, an Iterative Feedback Benchmark for LLMs' Multi-turn Instruction-Following Ability—0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context—0
ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models—0
MetaMorph: Multimodal Understanding and Generation via Instruction Tuning—0
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering—0
Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers—0
Towards Better Evaluation of Instruction-Following: A Case-Study in Summarization—0
MIDB: Multilingual Instruction Data Booster for Enhancing Multilingual Instruction Synthesis—0
A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model—0
Migician: Revealing the Magic of Free-Form Multi-Image Grounding in Multimodal Large Language Models—0
Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large Language Models—0
Case Study: Fine-tuning Small Language Models for Accurate and Private CWE Detection in Python Code—0
MiningGPT -- A Domain-Specific Large Language Model for the Mining Industry—0
MinMo: A Multimodal Large Language Model for Seamless Voice Interaction—0
Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning—0
Mitigating the Influence of Distractor Tasks in LMs with Prior-Aware Decoding—0
Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning—0
Mixture of Weight-shared Heterogeneous Group Attention Experts for Dynamic Token-wise KV Optimization—0
Capybara-OMNI: An Efficient Paradigm for Building Omni-Modal Language Models—0
WoLF: Wide-scope Large Language Model Framework for CXR Understanding—0
Towards Understanding the Fragility of Multilingual LLMs against Fine-Tuning Attacks—0
MMMT-IF: A Challenging Multimodal Multi-Turn Instruction Following Benchmark—0
CantTalkAboutThis: Aligning Language Models to Stay on Topic in Dialogues—0
Towards Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs—0
MMTEB: Massive Multilingual Text Embedding Benchmark—0
TOWER: Tree Organized Weighting for Evaluating Complex Instructions—0
MoDA: Modulation Adapter for Fine-Grained Visual Grounding in Instructional MLLMs—0
Zero-shot Task Adaptation using Natural Language—0
Modular Framework for Visuomotor Language Grounding—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