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 401–425 of 1135 papers

TitleStatusHype
Language Imbalance Driven Rewarding for Multilingual Self-improvingCode1
CoPESD: A Multi-Level Surgical Motion Dataset for Training Large Vision-Language Models to Co-Pilot Endoscopic Submucosal DissectionCode1
Reward-Augmented Data Enhances Direct Preference Alignment of LLMsCode1
Evolutionary Contrastive Distillation for Language Model Alignment—0
Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchy—0
LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints—0
HERM: Benchmarking and Enhancing Multimodal LLMs for Human-Centric Understanding—0
Large Language Model Compression with Neural Architecture Search—0
ReIFE: Re-evaluating Instruction-Following EvaluationCode0
Self-Boosting Large Language Models with Synthetic Preference Data—0
Direct Preference Optimization for LLM-Enhanced Recommendation Systems—0
Multimodal Situational Safety—0
Aria: An Open Multimodal Native Mixture-of-Experts ModelCode5
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation DataCode2
TOWER: Tree Organized Weighting for Evaluating Complex Instructions—0
A Recipe For Building a Compliant Real Estate ChatbotCode1
Superficial Safety Alignment Hypothesis—0
On Instruction-Finetuning Neural Machine Translation Models—0
RevisEval: Improving LLM-as-a-Judge via Response-Adapted References—0
Only-IF:Revealing the Decisive Effect of Instruction Diversity on Generalization—0
SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe—0
CS4: Measuring the Creativity of Large Language Models Automatically by Controlling the Number of Story-Writing ConstraintsCode0
Self-Powered LLM Modality Expansion for Large Speech-Text ModelsCode0
SAG: Style-Aligned Article Generation via Model Collaboration—0
TICKing All the Boxes: Generated Checklists Improve LLM Evaluation and 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