Papers by Junhan Liu

3 papers
M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning (2024.emnlp-main)

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Challenge: Multimodal Large Language Models (MLLMs) exhibit remarkable performance across a wide range of domains.
Approach: They propose a multimodal prompt tuning approach for efficient instruction tuning of MLLMs.
Outcome: The proposed approach shows superior performance on multimodal evaluation datasets compared to state-of-the-art methods.
Longtriever: a Pre-trained Long Text Encoder for Dense Document Retrieval (2023.emnlp-main)

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Challenge: Existing PLMs are infeasible for processing long documents due to computational costs and incomprehensive document understanding.
Approach: They propose a retrieval model that models local semantics and global context semantics in a tightly-coupled manner.
Outcome: The proposed model overcomes three core challenges of long document retrieval: substantial computational cost, incomprehensive document understanding, and scarce annotations.
Gardener: An Agentic AI System for Single-Cell RNA Sequence Analysis (2026.acl-demo)

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Challenge: Existing large language models encode workflow progress as conversational state and rely on cloud-centric execution, which hinders traceability and auditability.
Approach: Gardener is an open-source desktop application for macOS and windows under the Apache License 2.0.
Outcome: Gardener is released as an open-source desktop application for macOS and Windows under the Apache License 2.0.

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