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Found 4 papers, 3 papers with code
BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline
The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several institutions.
Aligning Explanations for Recommendation with Rating and Feature via Maximizing Mutual Information
However, as current explanation generation methods are commonly trained with an objective to mimic existing user reviews, the generated explanations are often not aligned with the predicted ratings or some important features of the recommended items, and thus, are suboptimal in helping users make informed decision on the recommendation platform.
YuLan: An Open-source Large Language Model
This paper presents the development of YuLan, a series of open-source LLMs with $12$ billion parameters.
An Integrated Data Processing Framework for Pretraining Foundation Models
The ability of the foundation models heavily relies on large-scale, diverse, and high-quality pretraining data.
