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Author: sh-dima

_pages/index.md

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 Welcome to the IT:U NLP Lab! Our overarching research goals are twofold: (a) to develop robust and trustworthy language processing techniques informed by linguistic principles for understanding and generating human language; and (b) to build human-centered NLP applications that advance education, social sciences, humanities, and scientific discovery. In pursuit of these research goals, our research agenda is center around the following topics:
 
-- Governance of Large Language Models (LLM): we will focus on evaluating and mitigating undesirable LLM behaviors, such as [building LLM evaluation benchmarks](https://arxiv.org/abs/2404.18923) and [understanding the behaviors of LLMs with real-world knowledge conflicts](https://arxiv.org/abs/2406.13805).
+- **Governance of Large Language Models (LLM):** we will focus on evaluating and mitigating undesirable LLM behaviors, such as [building LLM evaluation benchmarks](https://arxiv.org/abs/2404.18923) and [understanding the behaviors of LLMs with real-world knowledge conflicts](https://arxiv.org/abs/2406.13805).
 
-- Computational Argumentation and Fact-checking: we aim to combine argument mining and fact-checking technologies to [combat real-world misinformation](https://aclanthology.org/2022.emnlp-main.397/), such as [detecting health and science misinformation](https://aclanthology.org/2024.acl-long.240.pdf).
+- **Computational Argumentation and Fact-checking:** we aim to combine argument mining and fact-checking technologies to [combat real-world misinformation](https://aclanthology.org/2022.emnlp-main.397/), such as [detecting health and science misinformation](https://aclanthology.org/2024.acl-long.240.pdf).
 
-- NLP for Science: the goal is to develop reliable cross-document NLP techniques to support scientific research activities, including [generating slides from scientific papers](https://aclanthology.org/2021.naacl-main.111.pdf), [building scientific leaderboards](https://aclanthology.org/P19-1513.pdf), [generating citation text](https://aclanthology.org/2024.acl-long.265.pdf), [synthesizing biomedical studies](https://aclanthology.org/2024.acl-srw.42/), and [analyzing causal relations between scientific research concepts](https://aclanthology.org/2023.emnlp-main.142.pdf).
+- **NLP for Science:** the goal is to develop reliable cross-document NLP techniques to support scientific research activities, including [generating slides from scientific papers](https://aclanthology.org/2021.naacl-main.111.pdf), [building scientific leaderboards](https://aclanthology.org/P19-1513.pdf), [generating citation text](https://aclanthology.org/2024.acl-long.265.pdf), [synthesizing biomedical studies](https://aclanthology.org/2024.acl-srw.42/), and [analyzing causal relations between scientific research concepts](https://aclanthology.org/2023.emnlp-main.142.pdf).
 
-- Knowledge and Reasoning: we aim to develop techniques that can effectively represent both explicit and implicit knowledge to perform complex reasoning tasks in situated contexts, such as [designing a fallacious reasoning framework for misrepresented science](https://aclanthology.org/2024.acl-long.240.pdf) and [building an argumentation knowledge graph](https://ojs.aaai.org/index.php/AAAI/article/view/6231).
+- **Knowledge and Reasoning:** we aim to develop techniques that can effectively represent both explicit and implicit knowledge to perform complex reasoning tasks in situated contexts, such as [designing a fallacious reasoning framework for misrepresented science](https://aclanthology.org/2024.acl-long.240.pdf) and [building an argumentation knowledge graph](https://ojs.aaai.org/index.php/AAAI/article/view/6231).
 
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