20 citations · 68 across the 10 of their papers we have counts for
11 papers
From Retrieval to Reasoning: Agentic Mechanism Prediction from Cell Painting Profiles
Jiayuan Chen, Botao Yu, Tianyu Liu +3
Cell Painting is a high-content morphological profiling assay widely used for phenotype-based biological inference, with mechanism of action (MOA) prediction as a central applicati…
Accelerating Scientific Discovery with Autonomous Goal-evolving Agents
Yuanqi Du, Botao Yu, Tianyu Liu +25
There has been unprecedented interest in developing agents that expand the boundary of scientific discovery, primarily by optimizing quantitative objective functions specified by s…
ChemToolAgent: The Impact of Tools on Language Agents for Chemistry Problem Solving
Botao Yu, Frazier N. Baker, Ziru Chen +5
To enhance large language models (LLMs) for chemistry problem solving, several LLM-based agents augmented with tools have been proposed, such as ChemCrow and Coscientist. However,…
ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
Ziru Chen, Shijie Chen, Yuting Ning +17
The advancements of large language models (LLMs) have piqued growing interest in developing LLM-based language agents to automate scientific discovery end-to-end, which has sparked…
MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark
Xiang Yue, Tianyu Zheng, Yuansheng Ni +10
This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. MMMU-Pro rigorously assesses multimodal mo…
LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset
Botao Yu, Frazier N. Baker, Ziqi Chen +2
Chemistry plays a crucial role in many domains, such as drug discovery and material science. While large language models (LLMs) such as GPT-4 exhibit remarkable capabilities on nat…
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
Xiang Yue, Yuansheng Ni, Kai Zhang +19
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU…
EmoGen: Eliminating Subjective Bias in Emotional Music Generation
Chenfei Kang, Peiling Lu, Botao Yu +4
Music is used to convey emotions, and thus generating emotional music is important in automatic music generation. Previous work on emotional music generation directly uses annotate…
MuseCoco: Generating Symbolic Music from Text
Peiling Lu, Xin Xu, Chenfei Kang +4
Generating music from text descriptions is a user-friendly mode since the text is a relatively easy interface for user engagement. While some approaches utilize texts to control mu…
Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation
Botao Yu, Peiling Lu, Rui Wang +6
Symbolic music generation aims to generate music scores automatically. A recent trend is to use Transformer or its variants in music generation, which is, however, suboptimal, beca…
Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction
Kailong Hao, Botao Yu, Wei Hu
Distantly supervised relation extraction (RE) automatically aligns unstructured text with relation instances in a knowledge base (KB). Due to the incompleteness of current KBs, sen…