<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>LLM for Science | Yiqiao Jin CS PhD @ Georgia Tech</title><link>https://ahren09.github.io/tags/llm-for-science/</link><atom:link href="https://ahren09.github.io/tags/llm-for-science/index.xml" rel="self" type="application/rss+xml"/><description>LLM for Science</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sat, 01 Nov 2025 00:00:00 +0000</lastBuildDate><image><url>https://ahren09.github.io/media/icon_hu_eee6347cbdb2cc3f.png</url><title>LLM for Science</title><link>https://ahren09.github.io/tags/llm-for-science/</link></image><item><title>Protein Large Language Models: A Comprehensive Survey</title><link>https://ahren09.github.io/publication/emnlp25_protein_llm_survey/</link><pubDate>Sat, 01 Nov 2025 00:00:00 +0000</pubDate><guid>https://ahren09.github.io/publication/emnlp25_protein_llm_survey/</guid><description>&lt;h2 id="abstract">Abstract&lt;/h2>
&lt;p>Protein large language models (Protein LLMs) have rapidly emerged as a transformative paradigm for protein understanding, generation, and design. This survey provides a comprehensive overview of Protein LLMs, organizing the field along architectures, training objectives, datasets, downstream tasks, and applications across biology, chemistry, and medicine.&lt;/p>
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&lt;p>ProteinGPT is a multimodal LLM for protein property prediction and structure understanding. It integrates sequence and structural representations within a unified generative interface.&lt;/p>
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&lt;/ul></description></item><item><title>RNA-GPT: Multimodal Generative System for RNA Sequence Understanding</title><link>https://ahren09.github.io/publication/neurips24_rnagpt/</link><pubDate>Fri, 13 Dec 2024 00:00:00 +0000</pubDate><guid>https://ahren09.github.io/publication/neurips24_rnagpt/</guid><description>&lt;h2 id="abstract">Abstract&lt;/h2>
&lt;p>RNA-GPT is a multimodal generative system for RNA sequence understanding. It combines sequence-level reasoning with structural cues to support property prediction, retrieval, and natural-language interaction over RNA data.&lt;/p>
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