Papers by Hongwei Du

4 papers
SLIM: Let LLM Learn More and Forget Less with Soft LoRA and Identity Mixture (2025.naacl-long)

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Challenge: balancing the training budget, downstream performance, and general capabilities of large language models remains a challenge in many applications.
Approach: They propose a mixture of expert framework based on Soft LoRA and Identity Mixture . SLIM allows dynamic routing between LoRA adapters and identity layers .
Outcome: The proposed framework reduces training cost while maintaining general capabilities . it can be open-sourced upon publication.
MIRAGE: Exploring How Large Language Models Perform in Complex Social Interactive Environments (2025.acl-short)

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Challenge: Large Language Models (LLMs) have shown remarkable capabilities in environmental perception, reasoning-based decision-making, and simulating complex human behaviors, particularly in interactive role-playing contexts.
Approach: They propose a framework to assess LLMs' proficiency in portraying advanced human behaviors through murder mystery games using eight intricately crafted scripts.
Outcome: The framework evaluates LLMs' performance in portraying advanced human behaviors through murder mystery games.
Building the Directed Semantic Graph for Coherent Long Text Generation (2021.emnlp-main)

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Challenge: Existing methods for conditional long text generation ignore the coherence issue of the generated texts.
Approach: They propose a two-stage approach to generate coherent long text based on short input text . they first build a document-level path for each output text with each sentence embedding as its node .
Outcome: The proposed approach is superior to state-of-the-art approaches on three real-world datasets.

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