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Found 7 papers, 4 papers with code
Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models
The composition of pre-training datasets for large language models (LLMs) remains largely undisclosed, hindering transparency and efforts to optimize data quality, a critical driver of model performance.
Unsupervised Topic Models are Data Mixers for Pre-training Language Models
The performance of large language models (LLMs) is significantly affected by the quality and composition of their pre-training data, which is inherently diverse, spanning various domains, sources, and topics.
Multi-Agent Collaborative Data Selection for Efficient LLM Pretraining
Efficient data selection is crucial to accelerate the pretraining of large language models (LLMs).
DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI Data
In the era of artificial intelligence, the diversity of data modalities and annotation formats often renders data unusable directly, requiring understanding and format conversion before it can be used by researchers or developers with different needs.
FoundaBench: Evaluating Chinese Fundamental Knowledge Capabilities of Large Language Models
In the burgeoning field of large language models (LLMs), the assessment of fundamental knowledge remains a critical challenge, particularly for models tailored to Chinese language and culture.
Advanced Unstructured Data Processing for ESG Reports: A Methodology for Structured Transformation and Enhanced Analysis
In the evolving field of corporate sustainability, analyzing unstructured Environmental, Social, and Governance (ESG) reports is a complex challenge due to their varied formats and intricate content.
VIGC: Visual Instruction Generation and Correction
A practical solution to this problem would be to utilize the available multimodal large language models (MLLMs) to generate instruction data for vision-language tasks.
