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

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Showing 481–490 of 1135 papers

TitleStatusHype
Parameter-Efficient Quantized Mixture-of-Experts Meets Vision-Language Instruction Tuning for Semiconductor Electron Micrograph Analysis—0
Foundational Model for Electron Micrograph Analysis: Instruction-Tuning Small-Scale Language-and-Vision Assistant for Enterprise Adoption—0
Preference Consistency Matters: Enhancing Preference Learning in Language Models with Automated Self-Curation of Training Corpora—0
Jamba-1.5: Hybrid Transformer-Mamba Models at ScaleCode5
Preference-Guided Reflective Sampling for Aligning Language ModelsCode0
Kubrick: Multimodal Agent Collaborations for Synthetic Video Generation—0
Ex3: Automatic Novel Writing by Extracting, Excelsior and ExpandingCode1
LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMsCode1
FuseChat: Knowledge Fusion of Chat ModelsCode4
Can Large Language Models Understand Symbolic Graphics Programs?—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