Papers by Xinyun Zhang
Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to meeting summarization are limited due to noise, lengthy transcripts, and scattered salient information. |
| Approach: | They propose a two-step framework for meeting summarization that leverages a self-supervised paradigm to reconstruct transcripts and a relative positional bucketing algorithm to equip models to generate the summary. |
| Outcome: | The proposed method significantly reduces memory consumption and processing time on two meeting summarization datasets. |
SWE-QA: Can Language Models Answer Repository-level Code Questions? (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks for understanding and reasoning about entire soft-ware repositories focus on small, self-contained code snippets. |
| Approach: | They propose a repository-level code question answering benchmark to facilitate research on automated QA systems in real-world repositories. |
| Outcome: | The proposed benchmarks are designed to facilitate research on automated QA systems in real-world repositories. |
Exploring Label Hierarchy in a Generative Way for Hierarchical Text Classification (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for hierarchical text classification are lacking in the field of natural language processing. |
| Approach: | They propose a hierarchy-aware T5 model with path-adaptive attention mechanism to exploit hierarchical dependency across different levels. |
| Outcome: | The proposed model outperforms state-of-the-art models especially in Macro-F1 and low Macro. |
Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing research has focused on enhancing graph reasoning capabilities of LLMs by supervised fine-tuning on synthetic graph data. |
| Approach: | They propose to unlock generalizable learning of graph with post-training alignment with synthetic graph data by aligning off-the-shelf LLMs and LLM fine-tuned on synthetic graphs. |
| Outcome: | The proposed algorithm improves on synthetic graph problems and out-of-domain tasks with implicit graph structures. |
Natural SQL: Making SQL Easier to Infer from Natural Language Specifications (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models that do not support executable SQL generation can generate executable queries. |
| Approach: | They propose an SQL intermediate representation called Natural SQL (NatSQL) they propose to preserve the core functionalities of SQL while simplifying the queries . |
| Outcome: | The proposed model outperforms existing models on a text-to-SQL benchmark . it significantly improves the performance of previous models on the same dataset . |