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 601–625 of 1135 papers

TitleStatusHype
Investigating Non-Transitivity in LLM-as-a-Judge—0
Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh—0
TALKPLAY: Multimodal Music Recommendation with Large Language Models—0
MMTEB: Massive Multilingual Text Embedding Benchmark—0
Integrating Arithmetic Learning Improves Mathematical Reasoning in Smaller Models—0
RoleMRC: A Fine-Grained Composite Benchmark for Role-Playing and Instruction-FollowingCode0
Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding—0
MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive TrainingCode0
SAIF: A Sparse Autoencoder Framework for Interpreting and Steering Instruction Following of Language Models—0
Do we Really Need Visual Instructions? Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models—0
CORDIAL: Can Multimodal Large Language Models Effectively Understand Coherence Relationships?Code0
Rewrite to Jailbreak: Discover Learnable and Transferable Implicit Harmfulness InstructionCode0
Cuckoo: An IE Free Rider Hatched by Massive Nutrition in LLM's NestCode0
E2LVLM:Evidence-Enhanced Large Vision-Language Model for Multimodal Out-of-Context Misinformation Detection—0
Who Taught You That? Tracing Teachers in Model Distillation—0
Temporal Representation Alignment: Successor Features Enable Emergent Compositionality in Robot Instruction Following—0
Hypencoder: Hypernetworks for Information Retrieval—0
Verifiable Format Control for Large Language Model Generations—0
LLMs can be easily Confused by Instructional Distractions—0
Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons—0
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model—0
Shuttle Between the Instructions and the Parameters of Large Language Models—0
CoDe: Blockwise Control for Denoising Diffusion ModelsCode0
Learning Human Perception Dynamics for Informative Robot Communication—0
BARE: Leveraging Base Language Models for Few-Shot Synthetic Data 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