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 976–1000 of 1135 papers

TitleStatusHype
Human-Instruction-Free LLM Self-Alignment with Limited Samples—0
Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought—0
T2VPhysBench: A First-Principles Benchmark for Physical Consistency in Text-to-Video Generation—0
Hypencoder: Hypernetworks for Information Retrieval—0
HyperCLOVA X Technical Report—0
VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought—0
ICCO: Learning an Instruction-conditioned Coordinator for Language-guided Task-aligned Multi-robot Control—0
T2VTextBench: A Human Evaluation Benchmark for Textual Control in Video Generation Models—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
Improving Reward Models with Synthetic Critiques—0
Improving the Robustness of Large Language Models via Consistency Alignment—0
Improving the Robustness to Variations of Objects and Instructions with a Neuro-Symbolic Approach for Interactive Instruction Following—0
Incentivizing Inclusive Contributions in Model Sharing Markets—0
TALKPLAY: Multimodal Music Recommendation with Large Language Models—0
In-context Learning vs. Instruction Tuning: The Case of Small and Multilingual Language Models—0
In-Context Watermarks for Large Language Models—0
Leveraging LLMs for Influence Path Planning in Proactive Recommendation—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