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 826–850 of 1135 papers

TitleStatusHype
Bridging Offline and Online Reinforcement Learning for LLMs—0
Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis—0
Transformer-based Causal Language Models Perform Clustering—0
Multimodal Sequential Generative Models for Semi-Supervised Language Instruction Following—0
Multimodal Situational Safety—0
Multimodal Web Navigation with Instruction-Finetuned Foundation Models—0
Multi-Query Focused Disaster Summarization via Instruction-Based Prompting—0
Multi-Reward as Condition for Instruction-based Image Editing—0
Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance—0
Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers—0
Nadine: An LLM-driven Intelligent Social Robot with Affective Capabilities and Human-like Memory—0
Natural Language-conditioned Reinforcement Learning with Inside-out Task Language Development and Translation—0
Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs—0
Boosting LLM Translation Skills without General Ability Loss via Rationale Distillation—0
NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation—0
Navigating the Alpha Jungle: An LLM-Powered MCTS Framework for Formulaic Factor Mining—0
Neural Semantic Parsing—0
Nevermind: Instruction Override and Moderation in Large Language Models—0
Zero-shot Object-Centric Instruction Following: Integrating Foundation Models with Traditional Navigation—0
Triple Phase Transitions: Understanding the Learning Dynamics of Large Language Models from a Neuroscience Perspective—0
TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models—0
Nudging: Inference-time Alignment of LLMs via Guided Decoding—0
BLSP-KD: Bootstrapping Language-Speech Pre-training via Knowledge Distillation—0
TuneShield: Mitigating Toxicity in Conversational AI while Fine-tuning on Untrusted Data—0
OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence—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