Papers by Shanbao Qiao
GenPoE: Generative Passage-level Mixture of Experts for Knowledge Enhancement of LLMs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | GenPoE is a passage-level mixture of experts for enhancing knowledge of large language models. |
| Approach: | They propose a novel “generative” passage-level mixture of experts (MoEs) that takes in-context retrieved passages and generates their “expert” parameters. |
| Outcome: | The proposed system is based on a novel hypernetwork which takes in-context retrieved passages and generates their "expert'' parameters. |
COMEM: In-Context Retrieval-Augmented Mass-Editing Memory in Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for "knowledge editing" in large language models are inadequate . authors propose a method that can be used to update outdated information or correct false information . |
| Approach: | They propose a unified knowledge editing method called in-COntext retrieval-augmented Mass-Editing Memory . it incorporates retrieval augmented IKE, a novel extension of IKE designed for massive editing tasks . |
| Outcome: | The proposed method outperforms existing methods on the zsRE and CounterFact datasets. |
DistillMIKE: Editing Distillation of Massive In-Context Knowledge Editing in Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | In-context knowledge editing has shown respectable abilities on knowledge editing in terms of generalization and specificity. |
| Approach: | They propose a novel extension of in-context knowledge editing (IKE) that allows for massive edits to be injected into large language models. |
| Outcome: | The proposed method shows state-of-the-art perfomrances and comparable performance with MIKE. |
DiffusionRet: Diffusion-Enhanced Generative Retriever using Constrained Decoding (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Generative retrieval methods have suffered from the lack of the intermediate reasoning step . generative retrieval uses sequence-to-sequence diffusion models to map a query to relevant docids . |
| Approach: | They propose a novel method that uses query as an intermediate step before retrieval . they propose to use sequence-to-sequence diffusion models to map a query to relevant docids . |
| Outcome: | Experiments show that proposed method outperforms existing methods on MARCO and Natural Questions datasets. |
SeqMMR: Sequential Model Merging and LLM Routing for Enhanced Batched Sequential Knowledge Editing (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing research has demonstrated strong performance in single-instance or few-instantial sequential editing and one-time massive editing scenarios, but the batched sequential editing paradigm remains a significant challenge. |
| Approach: | They propose a framework for batched sequential knowledge editing that leverages **SeqMMR** and a model router to merge parameters from current batch-edited models with those of their predecessors. |
| Outcome: | The proposed framework iteratively merges parameters from current batch-edited models with those of their predecessors, ensuring that newly emerging knowledge is integrated while mitigating the forgetting of previously edited knowledge. |