@misc{lal2023size,
  title={One Size Does Not Fit All: Customizing Open-Domain Procedures}, 
  author={Yash Kumar Lal and Li Zhang and Faeze Brahman and Bodhisattwa Prasad Majumder and Peter Clark and Niket Tandon},
  year={2023},
  eprint={2311.09510},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
          

@misc{majumder2023clin,
  title={CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization}, 
  author={Bodhisattwa Prasad Majumder and Bhavana Dalvi Mishra and Peter Jansen and Oyvind Tafjord and Niket Tandon and Li Zhang and Chris Callison-Burch and Peter Clark},
  year={2023},
  eprint={2310.10134},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}
          

@misc{hou2023choice75,
  title={Choice-75: A Dataset on Decision Branching in Script Learning}, 
  author={Zhaoyi Joey Hou and Li Zhang and Chris Callison-Burch},
  year={2023},
  eprint={2309.11737},
  archivePrefix={arXiv},
  primaryClass={cs.AI}
}
        

@inproceedings{zhang-etal-2023-human,
  title = "Human-in-the-loop Schema Induction",
  author = "Zhang, Tianyi  and
    Tham, Isaac  and
    Hou, Zhaoyi  and
    Ren, Jiaxuan  and
    Zhou, Leon  and
    Xu, Hainiu  and
    Zhang, Li  and
    Martin, Lara  and
    Dror, Rotem  and
    Li, Sha  and
    Ji, Heng  and
    Palmer, Martha  and
    Brown, Susan Windisch  and
    Suchocki, Reece  and
    Callison-Burch, Chris",
  booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
  month = jul,
  year = "2023",
  address = "Toronto, Canada",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2023.acl-demo.1",
  pages = "1--10",
  abstract = "Schema induction builds a graph representation explaining how events unfold in a scenario. Existing approaches have been based on information retrieval (IR) and information extraction (IE), often with limited human curation. We demonstrate a human-in-the-loop schema induction system powered by GPT-3. We first describe the different modules of our system, including prompting to generate schematic elements, manual edit of those elements, and conversion of those into a schema graph. By qualitatively comparing our system to previous ones, we show that our system not only transfers to new domains more easily than previous approaches, but also reduces efforts of human curation thanks to our interactive interface.",
}
    

@inproceedings{cho-etal-2022-unsupervised,
title = "Unsupervised Entity Linking with Guided Summarization and Multiple-Choice Selection",
author = "Cho, Young Min  and
Zhang, Li  and
Callison-Burch, Chris",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.638",
pages = "9394--9401",
abstract = "Entity linking, the task of linking potentially ambiguous mentions in texts to corresponding knowledge-base entities, is an important component for language understanding. We address two challenge in entity linking: how to leverage wider contexts surrounding a mention, and how to deal with limited training data. We propose a fully unsupervised model called SumMC that first generates a guided summary of the contexts conditioning on the mention, and then casts the task to a multiple-choice problem where the model chooses an entity from a list of candidates. In addition to evaluating our model on existing datasets that focus on named entities, we create a new dataset that links noun phrases from WikiHow to Wikidata. We show that our SumMC model achieves state-of-the-art unsupervised performance on our new dataset and on exiting datasets.",
}
    

@Inproceedings{Pennsylvania2022,
author = {Panagopoulou, Artemis and Arora, Manni and Zhang, Li and Cugini, Dimitri and You, Weiqiu and Yang, Yue and Zhou, Liyang and Wang, Yuxuan and Hou, Zhaoyi and Hwang, Alyssa and Martin, Lara and Shi, Sherry and Callison-Burch, Chris and Yatskar, Mark},
title = {QuakerBot: A household dialog system powered by large language models},
year = {2022},
url = {https://www.amazon.science/alexa-prize/proceedings/quakerbot-a-household-dialog-system-powered-by-large-language-models},
booktitle = {Alexa Prize TaskBot Challenge Proceedings},
}

@inproceedings{zhang-etal-2023-exploring,
title = "Exploring the Curious Case of Code Prompts",
author = "Zhang, Li  and
Dugan, Liam  and
Xu, Hainiu  and
Callison-burch, Chris",
booktitle = "Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)",
month = jun,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.nlrse-1.2",
pages = "9--17",
abstract = "Recent work has shown that prompting language models with code-like representations of natural language leads to performance improvements on structured reasoning tasks. However, such tasks comprise only a small subset of all natural language tasks. In our work, we seek to answer whether or not code-prompting is the preferred way of interacting with language models in general. We compare code and text prompts across three popular GPT models (davinci, code-davinci-002, and text-davinci-002) on a broader selection of tasks (e.g., QA, sentiment, summarization) and find that with few exceptions, code prompts do not consistently outperform text prompts. Furthermore, we show that the style of code prompt has a large effect on performance for some (but not all) tasks and that fine-tuning on text instructions leads to better relative performance of code prompts.",
}
  

@misc{lyu2024calibrating,
  title={Calibrating Large Language Models with Sample Consistency}, 
  author={Qing Lyu and Kumar Shridhar and Chaitanya Malaviya and Li Zhang and Yanai Elazar and Niket Tandon and Marianna Apidianaki and Mrinmaya Sachan and Chris Callison-Burch},
  year={2024},
  eprint={2402.13904},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}

@inproceedings{lyu-etal-2023-faithful,
  title = "Faithful Chain-of-Thought Reasoning",
  author = "Lyu, Qing  and
    Havaldar, Shreya  and
    Stein, Adam  and
    Zhang, Li  and
    Rao, Delip  and
    Wong, Eric  and
    Apidianaki, Marianna  and
    Callison-Burch, Chris",
  editor = "Park, Jong C.  and
    Arase, Yuki  and
    Hu, Baotian  and
    Lu, Wei  and
    Wijaya, Derry  and
    Purwarianti, Ayu  and
    Krisnadhi, Adila Alfa",
  booktitle = "Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
  month = nov,
  year = "2023",
  address = "Nusa Dua, Bali",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2023.ijcnlp-main.20",
  pages = "305--329",
}
      

@inproceedings{zhang-etal-2022-label-definitions,
    title = "Label Definitions Improve Semantic Role Labeling",
    author = "Zhang, Li  and
      Jindal, Ishan  and
      Li, Yunyao",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.411",
    pages = "5613--5620",
    abstract = "Argument classification is at the core of Semantic Role Labeling. Given a sentence and the predicate, a semantic role label is assigned to each argument of the predicate. While semantic roles come with meaningful definitions, existing work has treated them as symbolic. Learning symbolic labels usually requires ample training data, which is frequently unavailable due to the cost of annotation. We instead propose to retrieve and leverage the definitions of these labels from the annotation guidelines. For example, the verb predicate {``}work{''} has arguments defined as {``}worker{''}, {``}job{''}, {``}employer{''}, etc. Our model achieves state-of-the-art performance on the CoNLL09 dataset injected with label definitions given the predicate senses. The performance improvement is even more pronounced in low-resource settings when training data is scarce.",
}
          

@inproceedings{lyu-etal-2022-favorite,
    title = "Is {``}My Favorite New Movie{''} My Favorite Movie? Probing the Understanding of Recursive Noun Phrases",
    author = "Lyu, Qing  and
      Hua, Zheng  and
      Li, Daoxin  and
      Zhang, Li  and
      Apidianaki, Marianna  and
      Callison-Burch, Chris",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-main.388",
    pages = "5286--5302",
    abstract = "Recursive noun phrases (NPs) have interesting semantic properties. For example, {``}my favorite new movie{''} is not necessarily my favorite movie, whereas {``}my new favorite movie{''} is. This is common sense to humans, yet it is unknown whether language models have such knowledge. We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs. When evaluated on RNPC, state-of-the-art Transformer models only perform around chance. Still, we show that such knowledge is learnable with appropriate data. We further probe the models for relevant linguistic features that can be learned from our tasks, including modifier semantic category and modifier scope. Finally, models trained on RNPC achieve strong zero-shot performance on an extrinsic Harm Detection evaluation task, showing the usefulness of the understanding of recursive NPs in downstream applications.",
}
        

@inproceedings{zhang-etal-2020-small,
title = "Small but Mighty: New Benchmarks for Split and Rephrase",
author = "Zhang, Li  and
  Zhu, Huaiyu  and
  Brahma, Siddhartha  and
  Li, Yunyao",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.91",
pages = "1198--1205",
}      
        

@inproceedings{zhang-etal-2019-multi,
title = "Multi-Label Transfer Learning for Multi-Relational Semantic Similarity",
author = "Zhang, Li  and
  Wilson, Steven  and
  Mihalcea, Rada",
booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/S19-1005",
pages = "44--50",
abstract = "Multi-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e.g., similarity, relatedness, and so on. Yet, all the systems to date designed to capture such relations target one relation at a time. We propose a multi-label transfer learning approach based on LSTM to make predictions for several relations simultaneously and aggregate the losses to update the parameters. This multi-label regression approach jointly learns the information provided by the multiple relations, rather than treating them as separate tasks. Not only does this approach outperform the single-task approach and the traditional multi-task learning approach, but it also achieves state-of-the-art performance on all but one relation of the Human Activity Phrase dataset.",
}
        

@misc{zhang2018direct,
title={Direct Network Transfer: Transfer Learning of Sentence Embeddings for Semantic Similarity},
author={Li Zhang and Steven R. Wilson and Rada Mihalcea},
year={2018},
eprint={1804.07835},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
        

@InProceedings{acl18sql,
  author    = {Catherine Finegan-Dollak\*  and  Jonathan K. Kummerfeld\*  and  Li Zhang  and  Karthik Ramanathan  and  Sesh Sadasivam  and  Rui Zhang  and  Dragomir Radev},
  title     = {Improving Text-to-SQL Evaluation Methodology},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  shortvenue = {ACL},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Victoria, Australia},
  pages     = {351--360},
  abstract  = {To be informative, an evaluation must measure how well systems generalize to realistic unseen data. We identify limitations of and propose improvements to current evaluations of text-to-SQL systems. First, we compare human-generated and automatically generated questions, characterizing properties of queries necessary for real-world applications. To facilitate evaluation on multiple datasets, we release standardized and improved versions of seven existing datasets and one new text-to-SQL dataset. Second, we show that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries; therefore, we propose a complementary dataset split for evaluation of future work. Finally, we demonstrate how the common practice of anonymizing variables during evaluation removes an important challenge of the task. Our observations highlight key difficulties, and our methodology enables effective measurement of future development.},
  url       = {http://aclweb.org/anthology/P18-1033},
  software  = {https://github.com/jkkummerfeld/text2sql-data},
  data      = {https://github.com/jkkummerfeld/text2sql-data},
}
          

@article{Burdick2018EntityAE,
title={Entity and Event Extraction from Scratch Using Minimal Training Data},
author={Laura Burdick and Steven R. Wilson and Oana Ignat and Charles F Welch and Li Zhang and Mingzhe Wang and Jia Deng and Rada Mihalcea},
journal={Theory and Applications of Categories},
year={2018}
}