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
HAPFI: History-Aware Planning based on Fused Information—0
DiffChat: Learning to Chat with Text-to-Image Synthesis Models for Interactive Image Creation—0
SteP: Stacked LLM Policies for Web Actions—0
HELPER-X: A Unified Instructable Embodied Agent to Tackle Four Interactive Vision-Language Domains with Memory-Augmented Language Models—0
HERM: Benchmarking and Enhancing Multimodal LLMs for Human-Centric Understanding—0
Procedures as Programs: Hierarchical Control of Situated Agents through Natural Language—0
Hindsight Planner: A Closed-Loop Few-Shot Planner for Embodied Instruction Following—0
Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models—0
Holistic Capability Preservation: Towards Compact Yet Comprehensive Reasoning Models—0
DeSTA: Enhancing Speech Language Models through Descriptive Speech-Text Alignment—0
DEM: Distribution Edited Model for Training with Mixed Data Distributions—0
How Far Can In-Context Alignment Go? Exploring the State of In-Context Alignment—0
DecIF: Improving Instruction-Following through Meta-Decomposition—0
How Many Instructions Can LLMs Follow at Once?—0
How well can LLMs Grade Essays in Arabic?—0
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text—0
DataMan: Data Manager for Pre-training Large Language Models—0
IFIR: A Comprehensive Benchmark for Evaluating Instruction-Following in Expert-Domain Information Retrieval—0
If You Can't Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs—0
Data Extraction Attacks in Retrieval-Augmented Generation via Backdoors—0
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning—0
Improving Instruction-Following in Language Models through Activation Steering—0
Improving Instruct Models for Free: A Study on Partial Adaptation—0
Improving Multilingual Instruction Finetuning via Linguistically Natural and Diverse Datasets—0
Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty—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