Papers by Wenzhao Qiu
MDBench: A Synthetic Multi-Document Reasoning Benchmark Generated with Knowledge Guidance (2025.findings-acl)
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| Challenge: | Multi-document reasoning is an area of increasing relevance given LLM capabilities in handling longer-context inputs, but few benchmarks exist to rigorously examine model behavior in this setting. |
| Approach: | They propose a new dataset for evaluating LLMs on the task of multi-document reasoning that uses condensed structured seed knowledge to modify it through LLM-assisted edits. |
| Outcome: | The proposed method generates document sets and QA examples on a multi-document reasoning task using a synthetic generation process. |
PELMS: Pre-training for Effective Low-Shot Multi-Document Summarization (2024.naacl-long)
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| Challenge: | Existing methods for abstractive multi-document summarization fail to generate concise, reflective summaries. |
| Approach: | They propose a pre-trained abstractive multi-document summarization model that uses unlabeled multi-doctoral inputs to generate concise, reflective summaries. |
| Outcome: | The proposed model outperforms competing models on a wide range of MDS datasets. |
Shoes-ACOSI: A Dataset for Aspect-Based Sentiment Analysis with Implicit Opinion Extraction (2024.findings-emnlp)
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| Challenge: | Prior work in ABSA has investigated opinion extraction as an important subtask, but these works only label concise, *explicitly*-stated opinion spans. |
| Approach: | They propose a new ABSA dataset with implicit opinion span annotations . they use paragraph-length inputs and prompted-LLM baselines to evaluate the dataset . |
| Outcome: | The proposed dataset presents significant challenges for fully-supervised models and LLMs. |