Papers by Weijin Zou
R2D2: Robust Data-to-Text with Replacement Detection (2022.emnlp-main)
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| Challenge: | Existing methods to mitigate unfaithful text generation are inadequate . data-to-text generation requires a structured input format . |
| Approach: | They propose a training framework that addresses unfaithful Data-to-Text generation by training a system as a generator and faithfulness discriminator with additional replacement detection and unlikelihood learning tasks. |
| Outcome: | The proposed training framework improves FeTaQA, LogicNLG, and ToTTo fidelity on D2T systems. |
OMG-QA: Building Open-Domain Multi-Modal Generative Question Answering Systems (2024.emnlp-industry)
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| Challenge: | Existing approaches to QA require multiple modalities and a broad pool of information sources to generate coherent answers. |
| Approach: | They propose a new resource to evaluate the effectiveness of question answering systems that perform retrieval augmented generation in scenarios that demand reasoning on multi-modal, multi-document contexts. |
| Outcome: | The proposed method evaluates question answering systems that perform retrieval augmented generation (RAG) in open-domain questions . it requires systems to navigate diverse modalities and a broad pool of information sources, making it uniquely challenging. |
On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering (2024.findings-naacl)
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| Challenge: | a new study evaluates how Large Language Models interact with a SQL interpreter . the model is limited in context and is stochastic, making it less suited for tasks requiring high precision and extensive computations. |
| Approach: | They propose and evaluate two interaction strategies to evaluate how LLMs interact with a SQL interpreter. |
| Outcome: | The proposed framework improves the accuracy and reliability of the evaluations. |
Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies (2023.findings-emnlp)
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Linyong Nan, Yilun Zhao, Weijin Zou, Narutatsu Ri, Jaesung Tae, Ellen Zhang, Arman Cohan, Dragomir Radev
| Challenge: | In-context learning (ICL) is a new approach to natural language processing tasks that rely on large language models to make predictions based on context . recent studies have shown that neural symbolic design is the preferred choice for question answering systems because of its limited working memory and unreliable long-term memory. |
| Approach: | They propose to extend in-context learning to question answering tasks that utilize structured knowledge sources and to explore various prompt design strategies for employing LLMs. |
| Outcome: | The proposed approach outperforms the state-of-the-art system by 2.5 points and the best fine-tuned system by 5.1 points on the Spider dataset. |
QTSumm: Query-Focused Summarization over Tabular Data (2023.emnlp-main)
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Yilun Zhao, Zhenting Qi, Linyong Nan, Boyu Mi, Yixin Liu, Weijin Zou, Simeng Han, Ruizhe Chen, Xiangru Tang, Yumo Xu, Dragomir Radev, Arman Cohan
| Challenge: | Existing text generation systems that can provide accurate table summaries can facilitate more efficient access to relevant data insights. |
| Approach: | They propose a query-focused task where text generation models have to perform human-like reasoning and analysis over the given table to generate a tailored table summary. |
| Outcome: | The proposed method improves existing baselines on table-to-text generation and large language models by concatenating generated facts to the model input. |