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 551–575 of 1135 papers

TitleStatusHype
AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language ModelsCode1
Evaluation of Instruction-Following Ability for Large Language Models on Story-Ending Generation—0
Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMsCode1
AudioBench: A Universal Benchmark for Audio Large Language ModelsCode3
RuleR: Improving LLM Controllability by Rule-based Data RecyclingCode1
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization—0
DEM: Distribution Edited Model for Training with Mixed Data Distributions—0
Hybrid Alignment Training for Large Language ModelsCode1
AdaGrad under Anisotropic Smoothness—0
VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought—0
LLaSA: A Multimodal LLM for Human Activity Analysis Through Wearable and Smartphone SensorsCode1
IWISDM: Assessing instruction following in multimodal models at scaleCode0
Finding Blind Spots in Evaluator LLMs with Interpretable ChecklistsCode1
Biomedical Visual Instruction Tuning with Clinician Preference AlignmentCode0
Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language ModelsCode3
The Comparative Trap: Pairwise Comparisons Amplifies Biased Preferences of LLM Evaluators—0
Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models—0
ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All ToolsCode14
Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal TransportCode0
RS-GPT4V: A Unified Multimodal Instruction-Following Dataset for Remote Sensing Image UnderstandingCode1
Refine Large Language Model Fine-tuning via Instruction Vector—0
ChatBug: A Common Vulnerability of Aligned LLMs Induced by Chat TemplatesCode1
Grade Score: Quantifying LLM Performance in Option SelectionCode0
Generative Visual Instruction TuningCode0
Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants—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