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 1001–1025 of 1135 papers

TitleStatusHype
DialMAT: Dialogue-Enabled Transformer with Moment-Based Adversarial TrainingCode0
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
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game—0
Multilingual Coarse Political Stance Classification of Media. The Editorial Line of a ChatGPT and Bard Newspaper—0
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment—0
Privately Aligning Language Models with Reinforcement Learning—0
Analyzing Multilingual Competency of LLMs in Multi-Turn Instruction Following: A Case Study of Arabic—0
LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction FollowingCode0
Quantifying Self-diagnostic Atomic Knowledge in Chinese Medical Foundation Model: A Computational AnalysisCode0
LoHoRavens: A Long-Horizon Language-Conditioned Benchmark for Robotic Tabletop Manipulation—0
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
GROOT: Learning to Follow Instructions by Watching Gameplay Videos—0
Towards Better Evaluation of Instruction-Following: A Case-Study in Summarization—0
From Supervised to Generative: A Novel Paradigm for Tabular Deep Learning with Large Language ModelsCode0
Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models—0
SteP: Stacked LLM Policies for Web Actions—0
Benchmarking and Improving Generator-Validator Consistency of Language Models—0
PACIT: Unlocking the Power of Examples for Better In-Context Instruction TuningCode0
SLM: Bridge the thin gap between speech and text foundation models—0
Self-Specialization: Uncovering Latent Expertise within Large Language Models—0
Towards LLM-guided Causal Explainability for Black-box Text Classifiers—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