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- Beijing Academy of Artificial IntelligenceCN102 papers
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5 papers · 2 filters
Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation
Jiatong Li, Junxian Li, Weida Wang +6
Recently, Large Language Models (LLMs) have demonstrated great potential in natural language-driven molecule discovery. However, existing datasets and benchmarks for molecule-text…
MolReFlect: Towards In-Context Fine-grained Alignments between Molecules and Texts
Jiatong Li, Yunqing Liu, Wei Liu +6
Molecule discovery is a pivotal research field, impacting everything from medicine to materials. Recently, Large Language Models (LLMs) have been widely adopted in molecular unders…
Large Language Models are In-Context Molecule Learners
Jiatong Li, Wei Liu, Zhihao Ding +3
Large Language Models (LLMs) have demonstrated exceptional performance in biochemical tasks, especially the molecule caption translation task, which aims to bridge the gap between…
MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues
Ge Bai, Jie Liu, Xingyuan Bu +8
The advent of Large Language Models (LLMs) has drastically enhanced dialogue systems. However, comprehensively evaluating the dialogue abilities of LLMs remains a challenge. Previo…
SEER: Facilitating Structured Reasoning and Explanation via Reinforcement Learning
Guoxin Chen, Kexin Tang, Chao Yang +3
Elucidating the reasoning process with structured explanations from question to answer is crucial, as it significantly enhances the interpretability, traceability, and trustworthin…