15 citations · 24 across the 14 of their papers we have counts for
14 papers
DiffER: Categorical Diffusion for Chemical Retrosynthesis
Sean Current, Ziqi Chen, Daniel Adu-Ampratwum +2
Methods for automatic chemical retrosynthesis have found recent success through the application of models traditionally built for natural language processing, primarily through tra…
Analyzing Fine-Grained Alignment and Enhancing Vision Understanding in Multimodal Language Models
Jiachen Jiang, Jinxin Zhou, Bo Peng +2
Achieving better alignment between vision embeddings and Large Language Models (LLMs) is crucial for enhancing the abilities of Multimodal LLMs (MLLMs), particularly for recent mod…
Planning with Diffusion Models for Target-Oriented Dialogue Systems
Hanwen Du, Bo Peng, Xia Ning
Target-Oriented Dialogue (TOD) remains a significant challenge in the LLM era, where strategic dialogue planning is crucial for directing conversations toward specific targets. How…
Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models with Guidance
Ziqi Chen, Bo Peng, Tianhua Zhai +2
Drug development is a critical but notoriously resource- and time-consuming process. In this manuscript, we develop a novel generative artificial intelligence (genAI) method DiffSM…
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,…
log-RRIM: Yield Prediction via Local-to-global Reaction Representation Learning and Interaction Modeling
Xiao Hu, Ziqi Chen, Bo Peng +2
Accurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of arti…
Captions Speak Louder than Images: Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Data
Xinyi Ling, Hanwen Du, Bo Peng +2
Leveraging multimodal data to drive breakthroughs in e-commerce applications through Multimodal Foundation Models (MFMs) is gaining increasing attention from the research community…
SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search
Hanwen Du, Bo Peng, Xia Ning
Conversational Recommender Systems (CRS) proactively engage users in interactive dialogues to elicit user preferences and provide personalized recommendations. Existing methods tra…
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…
eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data
Bo Peng, Xinyi Ling, Ziru Chen +2
With tremendous efforts on developing effective e-commerce models, conventional e-commerce models show limited success in generalist e-commerce modeling, and suffer from unsatisfac…
Modeling Sequences as Star Graphs to Address Over-smoothing in Self-attentive Sequential Recommendation
Bo Peng, Ziqi Chen, Srinivasan Parthasarathy +1
Self-attention (SA) mechanisms have been widely used in developing sequential recommendation (SR) methods, and demonstrated state-of-the-art performance. However, in this paper, we…
Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation
Bo Peng, Ben Burns, Ziqi Chen +2
In recent years, with large language models (LLMs) achieving state-of-the-art performance in context understanding, increasing efforts have been dedicated to developing LLM-enhance…
Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation
Bo Peng, Srinivasan Parthasarathy, Xia Ning
Learning effective recommendation models from sparse user interactions represents a fundamental challenge in developing sequential recommendation methods. Recently, pre-training-ba…
RLSynC: Offline-Online Reinforcement Learning for Synthon Completion
Frazier N. Baker, Ziqi Chen, Daniel Adu-Ampratwum +1
Retrosynthesis is the process of determining the set of reactant molecules that can react to form a desired product. Semi-template-based retrosynthesis methods, which imitate the r…