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 526–550 of 1135 papers

TitleStatusHype
UniEval: Unified Holistic Evaluation for Unified Multimodal Understanding and Generation—0
Tests as Prompt: A Test-Driven-Development Benchmark for LLM Code Generation—0
Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning?Code0
Efficient Telecom Specific LLM: TSLAM-Mini with QLoRA and Digital Twin Data—0
Assessing Robustness to Spurious Correlations in Post-Training Language Models—0
T2VTextBench: A Human Evaluation Benchmark for Textual Control in Video Generation Models—0
Incentivizing Inclusive Contributions in Model Sharing Markets—0
PIPA: A Unified Evaluation Protocol for Diagnosing Interactive Planning Agents—0
T2VPhysBench: A First-Principles Benchmark for Physical Consistency in Text-to-Video Generation—0
UAV-VLN: End-to-End Vision Language guided Navigation for UAVs—0
Ask, Fail, Repeat: Meeseeks, an Iterative Feedback Benchmark for LLMs' Multi-turn Instruction-Following Ability—0
TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language ModelsCode0
CachePrune: Neural-Based Attribution Defense Against Indirect Prompt Injection Attacks—0
Breaking the Modality Barrier: Universal Embedding Learning with Multimodal LLMs—0
ManipDreamer: Boosting Robotic Manipulation World Model with Action Tree and Visual Guidance—0
ParamΔ for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost—0
Case Study: Fine-tuning Small Language Models for Accurate and Private CWE Detection in Python Code—0
DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models—0
Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling EvaluatorsCode0
Improving Instruct Models for Free: A Study on Partial Adaptation—0
SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuning—0
Playpen: An Environment for Exploring Learning Through Conversational InteractionCode0
VideoExpert: Augmented LLM for Temporal-Sensitive Video Understanding—0
Capybara-OMNI: An Efficient Paradigm for Building Omni-Modal Language Models—0
Holistic Capability Preservation: Towards Compact Yet Comprehensive Reasoning Models—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