Papers by Dian Shen
LiGen: Active Lipid Generation via a Molecular Language Model (2026.acl-long)
Copied to clipboard
| Challenge: | Lipid nanoparticles (LNPs) can deliver cargos to tumor and immune cells . traditional approaches rely on experimental screening and expert judgment . |
| Approach: | They propose a method to generate lipid molecules efficiently and actively using deep learning. |
| Outcome: | The proposed method outperforms baseline methods on multiple cell lines and achieves a 30% improvement over the current methods. |
Invocation Refiner: A Plug-and-Play Module for Rectifying LLM Tool Invocations (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in Tool-Integrated Reasoning (TIR) however, the practical application is often hindered by frequent errors in tool invocations, such as incorrect tool names, invalid parameters, wrong tool-call order, or malformed invocation formats. |
| Approach: | They propose a specialized post-processing module that performs independent reasoning on the input of a frozen upstream LLM and an advanced RL algorithm to improve the tool-use reliability of base LLMs. |
| Outcome: | The proposed module improves task completion rates and invocation accuracy over the raw outputs of various upstream LLMs on a diverse set of tool-use and reasoning benchmarks. |
Compressing LLM Knowledge into Graph Representations for Text-attributed Graphs Learning (2026.acl-long)
Copied to clipboard
| Challenge: | Existing GNN-LLM approaches use large language models at inference time for processing text attributes, resulting in costly deployment. |
| Approach: | They propose a framework that internalizes LLM knowledge within graph models and supports inference-efficient TAG learning. |
| Outcome: | The proposed framework internalizes LLM knowledge within graph models and supports inference-efficient TAG learning. |
Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension (2020.acl-main)
Copied to clipboard
| Challenge: | Existing approaches to machine reading comprehension (MRC) on long texts typically chunk text into equally-spaced segments without considering information from other segments. |
| Approach: | They propose to let a model learn to chunk in a more flexible way via reinforcement learning. |
| Outcome: | The proposed model extracts a text span from document and query as answer . previous models can only take a fixed-length (e.g., 512) text as input . |