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 651–700 of 1135 papers

TitleStatusHype
Instruction-Following Speech Recognition—0
Aligner: One Global Token is Worth Millions of Parameters When Aligning Large Language Models—0
Instruction Mining: Instruction Data Selection for Tuning Large Language Models—0
Tests as Prompt: A Test-Driven-Development Benchmark for LLM Code Generation—0
Instruction Tuning on Public Government and Cultural Data for Low-Resource Language: a Case Study in Kazakh—0
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment—0
Text as Image: Learning Transferable Adapter for Multi-Label Classification—0
Only-IF:Revealing the Decisive Effect of Instruction Diversity on Generalization—0
Integrating Arithmetic Learning Improves Mathematical Reasoning in Smaller Models—0
Failures to Find Transferable Image Jailbreaks Between Vision-Language Models—0
Internalized Self-Correction for Large Language Models—0
InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model—0
Refine Large Language Model Fine-tuning via Instruction Vector—0
Inverse Reinforcement Learning with Natural Language Goals—0
Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions?—0
Investigating Non-Transitivity in LLM-as-a-Judge—0
Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL Translation—0
IOPO: Empowering LLMs with Complex Instruction Following via Input-Output Preference Optimization—0
Advancing Mathematical Reasoning in Language Models: The Impact of Problem-Solving Data, Data Synthesis Methods, and Training Stages—0
Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis—0
Iterative Value Function Optimization for Guided Decoding—0
Iterative Vision-and-Language Navigation—0
JarvisArt: Liberating Human Artistic Creativity via an Intelligent Photo Retouching Agent—0
Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing—0
Joint Embeddings for Graph Instruction Tuning—0
Joint Flashback Adaptation for Forgetting-Resistant Instruction Tuning—0
The Hidden Space of Safety: Understanding Preference-Tuned LLMs in Multilingual context—0
KAUCUS: Knowledge Augmented User Simulators for Training Language Model Assistants—0
Keypoints-Integrated Instruction-Following Data Generation for Enhanced Human Pose Understanding in Multimodal Models—0
KITE: Keypoint-Conditioned Policies for Semantic Manipulation—0
KIT's Offline Speech Translation and Instruction Following Submission for IWSLT 2025—0
KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions—0
CROME: Cross-Modal Adapters for Efficient Multimodal LLM—0
Knowledge-enhanced Agents for Interactive Text Games—0
Kubrick: Multimodal Agent Collaborations for Synthetic Video Generation—0
Mitigating Biases for Instruction-following Language Models via Bias Neurons Elimination—0
Creating Arabic LLM Prompts at Scale—0
LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents—0
Language and Planning in Robotic Navigation: A Multilingual Evaluation of State-of-the-Art Models—0
Language-Conditioned Goal Generation: a New Approach to Language Grounding for RL—0
Language-Conditioned Goal Generation: a New Approach to Language Grounding in RL—0
The Perfect Blend: Redefining RLHF with Mixture of Judges—0
Language-guided Semantic Mapping and Mobile Manipulation in Partially Observable Environments—0
Adaptive Decoding via Latent Preference Optimization—0
Language Models are General-Purpose Interfaces—0
CoTBal: Comprehensive Task Balancing for Multi-Task Visual Instruction Tuning—0
COSMIC: Data Efficient Instruction-tuning For Speech In-Context Learning—0
Language Models Benefit from Preparation with Elicited Knowledge—0
AdaGrad under Anisotropic Smoothness—0
Large Language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Course—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