Multimodal Large Language Models (MLLMs) have seen growing adoption across
various scientific disciplines. These advancements encourage the investigation
of molecule-text modeling within synthetic chemistry, a field dedicated to
designing and conducting chemical reactions to synthesize new compounds with
desired properties and applications. Current approaches, however, often neglect
the critical role of multiple molecule graph interaction in understanding
chemical reactions, leading to suboptimal performance in synthetic chemistry
tasks. This study introduces PRESTO(Progressive Pretraining Enhances Synthetic
Chemistry Outcomes), a new framework that bridges the molecule-text modality
gap by integrating a comprehensive benchmark of pretraining strategies and
dataset configurations. It progressively improves multimodal LLMs through
cross-modal alignment and multi-graph understanding. Our extensive experiments
demonstrate that PRESTO offers competitive results in downstream synthetic
chemistry tasks. The code can be found at this https URL

PRESTO 是一种新的框架，通过集成全面的预训练策略和数据集配置，逐步改进多模式 LLM，并通过跨模态对齐和多图理解来弥合分子 - 文本形态差距，从而在合成化学任务中提供了竞争性的结果。

PRESTO：渐进预训练改善合成化学结果

PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes

Research interest in task-oriented dialogs has increased as systems such as
Google Assistant, Alexa and Siri have become ubiquitous in everyday life.
However, the impact of academic research in this area has been limited by the
lack of datasets that realistically capture the wide array of user pain points.
To enable research on some of the more challenging aspects of parsing realistic
conversations, we introduce PRESTO, a public dataset of over 550K contextual
multilingual conversations between humans and virtual assistants. PRESTO
contains a diverse array of challenges that occur in real-world NLU tasks such
as disfluencies, code-switching, and revisions. It is the only large scale
human generated conversational parsing dataset that provides structured context
such as a user's contacts and lists for each example. Our mT5 model based
baselines demonstrate that the conversational phenomenon present in PRESTO are
challenging to model, which is further pronounced in a low-resource setup.

本研究介绍了 PRESTO，这是一个基于 550K 个虚拟助手和人之间的上下文多语言对话的公共数据集，用于研究语言理解的挑战性问题，包括流畅性、代码切换和修订等。mT5 模型在该数据集上的表现证明了其中的语言现象具有挑战性，尤其在低资源情况下。