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 801–825 of 1135 papers

TitleStatusHype
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
Modular Networks for Compositional Instruction Following—0
Can Large Language Models Understand Symbolic Graphics Programs?—0
Traffic Sign Interpretation in Real Road Scene—0
CamelEval: Advancing Culturally Aligned Arabic Language Models and Benchmarks—0
Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons—0
CorNav: Autonomous Agent with Self-Corrected Planning for Zero-Shot Vision-and-Language Navigation—0
MrSteve: Instruction-Following Agents in Minecraft with What-Where-When Memory—0
MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following—0
CachePrune: Neural-Based Attribution Defense Against Indirect Prompt Injection Attacks—0
Multi-Level Aware Preference Learning: Enhancing RLHF for Complex Multi-Instruction Tasks—0
Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper—0
Multi-lingual Functional Evaluation for Large Language Models—0
Multilingual Instruction Tuning With Just a Pinch of Multilinguality—0
Multilingual Multimodal Software Developer for Code Generation—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