Papers by Haitian Sun
Differentiable Open-Ended Commonsense Reasoning (2021.naacl-main)
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| Challenge: | Existing commonsense reasoning models work by scoring a question-candidate pair, but new approaches are needed to answer multiple-choice questions. |
| Approach: | They propose to use a corpus of commonsense facts to answer a commonsensical question without any pre-defined choices as a resource. |
| Outcome: | The proposed model outperforms baseline methods by a large margin in the open-ended commonsense reasoning task. |
Over-Searching in Search-Augmented Large Language Models (2026.eacl-long)
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| Challenge: | Search-augmented large language models (LLMs) excel at knowledge-intensive tasks by integrating external retrieval. |
| Approach: | They conduct a systematic evaluation of over-searching across multiple dimensions including query types, model categories, retrieval conditions, and multi-turn conversations. |
| Outcome: | The proposed model improves answer accuracy on answerable queries but harms abstention on unanswerable ones . |
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers (2022.acl-long)
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| Challenge: | Existing datasets for reading comprehension have deterministic answers, but questions in the real world do not always have definite answers. |
| Approach: | They propose a Question Answering (QA) dataset that contains complex questions with conditional answers. |
| Outcome: | The proposed dataset will motivate further research in answering complex questions over long documents. |
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions? (2023.emnlp-main)
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| Challenge: | Pre-trained vision and language models have demonstrated state-of-the-art capabilities over existing tasks involving images and texts. |
| Approach: | They analyze a visual question answering dataset tailored for info-seeking questions . they show that pre-trained visual and language models can use fine-grained knowledge . |
| Outcome: | The proposed dataset elicits models to use fine-grained knowledge learned during pre-training. |
SEMQA: Semi-Extractive Multi-Source Question Answering (2024.naacl-long)
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| Challenge: | Recent proposed long-form question answering systems have shown promising capabilities, but attributing and verifying their generated abstractive answers can be difficult. |
| Approach: | They propose a task that summarises multiple sources in a semi-extractive fashion . they create a dataset with human-written semi-extractive answers to natural and generated questions . |
| Outcome: | The proposed task summarizes multiple sources in a semi-extractive fashion and produces fine in-line attributions by-design that are easy to verify, interpret, and evaluate. |
PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text (D19-1)
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| Challenge: | Experimentally PullNet improves over the prior state-of-the-art open domain question answering systems. |
| Approach: | They propose a framework for learning what to retrieve and reasoning with heterogeneous information to find the best answer. |
| Outcome: | The proposed framework improves over the prior state-of-the-art in open domain question answering . it is weakly supervised, requiring question-answer pairs but not gold inference paths . |
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text (D18-1)
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| Challenge: | Specialized neural models have been developed for extracting answers from text alone or Knowledge Bases (KBs) alone. |
| Approach: | They propose a novel model for extracting answers from a question-specific subgraph containing text and KB entities and relations. |
| Outcome: | The proposed model outperforms existing methods in a combination of a KB and entity-linked text in QA over a large text corpus. |
Adaptable and Interpretable Neural MemoryOver Symbolic Knowledge (2021.naacl-main)
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| Challenge: | Past research has shown that large neural language models encode surprising amounts of factual information, but augmenting or modifying this information requires modifying a corpus and retraining, which is computationally expensive. |
| Approach: | They propose a neural LM that includes an interpretable neuro-symbolic KB in the form of a "fact memory" their LM improves performance on knowledge-intensive question-answering tasks, sometimes dramatically . |
| Outcome: | The proposed model improves on knowledge-intensive question-answering tasks, including a 27 point increase in one setting of WebQuestionsSP over a state-of-the-art open-book model. |