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 776–800 of 1135 papers

TitleStatusHype
MAP's not dead yet: Uncovering true language model modes by conditioning away degeneracy—0
MART: Improving LLM Safety with Multi-round Automatic Red-Teaming—0
MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching—0
Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning—0
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records—0
ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities—0
MedXChat: A Unified Multimodal Large Language Model Framework towards CXRs Understanding and Generation—0
Ask, Fail, Repeat: Meeseeks, an Iterative Feedback Benchmark for LLMs' Multi-turn Instruction-Following Ability—0
A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context—0
ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models—0
MetaMorph: Multimodal Understanding and Generation via Instruction Tuning—0
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering—0
Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers—0
Towards Better Evaluation of Instruction-Following: A Case-Study in Summarization—0
MIDB: Multilingual Instruction Data Booster for Enhancing Multilingual Instruction Synthesis—0
A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Instruction Following Large Language Model—0
Migician: Revealing the Magic of Free-Form Multi-Image Grounding in Multimodal Large Language Models—0
Mimicking User Data: On Mitigating Fine-Tuning Risks in Closed Large Language Models—0
Case Study: Fine-tuning Small Language Models for Accurate and Private CWE Detection in Python Code—0
MiningGPT -- A Domain-Specific Large Language Model for the Mining Industry—0
MinMo: A Multimodal Large Language Model for Seamless Voice Interaction—0
Mitigating Dialogue Hallucination for Large Vision Language Models via Adversarial Instruction Tuning—0
Mitigating the Influence of Distractor Tasks in LMs with Prior-Aware Decoding—0
Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning—0
Mixture of Weight-shared Heterogeneous Group Attention Experts for Dynamic Token-wise KV Optimization—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