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

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Showing 851–900 of 1135 papers

TitleStatusHype
Instruction-Following Evaluation for Large Language ModelsCode5
MART: Improving LLM Safety with Multi-round Automatic Red-Teaming—0
Generalization Analogies: A Testbed for Generalizing AI Oversight to Hard-To-Measure DomainsCode0
To See is to Believe: Prompting GPT-4V for Better Visual Instruction TuningCode2
WaterBench: Towards Holistic Evaluation of Watermarks for Large Language ModelsCode1
InfMLLM: A Unified Framework for Visual-Language TasksCode1
Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small ScorerCode1
DialMAT: Dialogue-Enabled Transformer with Moment-Based Adversarial TrainingCode0
u-LLaVA: Unifying Multi-Modal Tasks via Large Language ModelCode1
LLaVA-Plus: Learning to Use Tools for Creating Multimodal AgentsCode2
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free LunchCode2
PhoGPT: Generative Pre-training for VietnameseCode2
ChEF: A Comprehensive Evaluation Framework for Standardized Assessment of Multimodal Large Language Models—0
COSMIC: Data Efficient Instruction-tuning For Speech In-Context Learning—0
Active Reasoning in an Open-World Environment—0
FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language ModelsCode1
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game—0
Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy InstructionsCode1
FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language ModelsCode1
Making Large Language Models Better Data CreatorsCode1
Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly DetectionCode1
Privately Aligning Language Models with Reinforcement Learning—0
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment—0
Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper—0
Instruct and Extract: Instruction Tuning for On-Demand Information ExtractionCode1
Analyzing Multilingual Competency of LLMs in Multi-Turn Instruction Following: A Case Study of Arabic—0
AlpaCare:Instruction-tuned Large Language Models for Medical ApplicationCode1
Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst DesignCode1
BotChat: Evaluating LLMs' Capabilities of Having Multi-Turn DialoguesCode1
Democratizing Reasoning Ability: Tailored Learning from Large Language ModelCode1
An Emulator for Fine-Tuning Large Language Models using Small Language ModelsCode1
LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction FollowingCode0
LoHoRavens: A Long-Horizon Language-Conditioned Benchmark for Robotic Tabletop Manipulation—0
Quantifying Self-diagnostic Atomic Knowledge in Chinese Medical Foundation Model: A Computational AnalysisCode0
VeRA: Vector-based Random Matrix Adaptation—0
Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis—0
Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning—0
Towards Better Evaluation of Instruction-Following: A Case-Study in Summarization—0
GROOT: Learning to Follow Instructions by Watching Gameplay Videos—0
Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models—0
Evaluating Large Language Models at Evaluating Instruction FollowingCode1
From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language ModelsCode0
LLark: A Multimodal Instruction-Following Language Model for MusicCode2
TRACE: A Comprehensive Benchmark for Continual Learning in Large Language ModelsCode1
Understanding the Effects of RLHF on LLM Generalisation and DiversityCode1
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data CompositionCode3
Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New LanguagesCode1
SteP: Stacked LLM Policies for Web Actions—0
Benchmarking and Improving Generator-Validator Consistency of Language Models—0
Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with TransformersCode1
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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