Papers by Diyi Liu
Werewolf Among Us: Multimodal Resources for Modeling Persuasion Behaviors in Social Deduction Games (2023.findings-acl)
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Bolin Lai, Hongxin Zhang, Miao Liu, Aryan Pariani, Fiona Ryan, Wenqi Jia, Shirley Anugrah Hayati, James Rehg, Diyi Yang
| Challenge: | Existing studies on persuasive behavior modeling focus on textual dialogues . a multimodal dataset is available for persuasion modeling . |
| Approach: | They propose a multimodal dataset for modeling persuasive behaviors using visual signals. |
| Outcome: | The proposed dataset includes 199 dialogue transcriptions and videos captured in a multi-player social deduction game setting and 26,647 utterance level annotations of persuasion strategy and game level annotation of deduction game outcomes. |
HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter (2025.acl-long)
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Manuel Tonneau, Diyi Liu, Niyati Malhotra, Scott A. Hale, Samuel Fraiberger, Victor Orozco-Olvera, Paul Röttger
| Challenge: | Prior work on automated hate speech detection models has been limited due to systematic biases in evaluation datasets and poor performance across geographies. |
| Approach: | They propose to construct a global hate speech dataset representative of social media settings from tweets posted on September 21, 2022. |
| Outcome: | The proposed dataset covers eight languages and four English-speaking countries and covers eight countries where English is the main language on Twitter. |
Task-Agnostic Low-Rank Adapters for Unseen English Dialects (2023.emnlp-main)
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| Challenge: | a recent study found that LLMs are trained on corpora disproportionally weighted in favor of Standard American English . prior work on dialect struggle with generalizing to evolving and emerging dialects in a scalable manner. |
| Approach: | They propose a method that leverages linguistic knowledge to enable resource-efficient adaptation . their method disentangles dialect-specific and cross-dialectal information . |
| Outcome: | a new method improves generalization to unseen dialects in a task-agnostic fashion . it achieves the best or most competitive performance across 5 dialects . |
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach (2024.emnlp-main)
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| Challenge: | Existing studies on susceptibility to misinformation rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications. |
| Approach: | They propose a computational approach to efficiently model users’ latent susceptibility levels by using demographic factors and political ideology as inputs. |
| Outcome: | The proposed model shows that political leanings and other psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation. |
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue (2023.acl-long)
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| Challenge: | Existing studies show that multilingual models are less robust for semantic parsing compared to other tasks. |
| Approach: | They propose a constrained optimization technique to optimize multilingual parsing systems for multilingual use. |
| Outcome: | The proposed technique outperforms XLM-R and mT5-Large on three benchmarks and significantly outperformed other models. |
CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation (2023.emnlp-main)
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| Challenge: | Annotated data plays a critical role in training models and evaluating their performance. |
| Approach: | They propose a paradigm for Human-LLM co-annotation of unstructured texts at scale that utilizes uncertainty to estimate LLMs’ annotation capability. |
| Outcome: | The proposed model outperforms existing models on many text-annotation tasks with up to 21% performance improvement over random baseline. |
Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering (2025.naacl-long)
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| Challenge: | Generative AI has made rapid advances in multimodal understanding and code generation. |
| Approach: | They construct a first real-world benchmark for multimodal large language models that directly convert visual designs into code implementations by manually curating 484 diverse real-life webpages as test cases. |
| Outcome: | The proposed model can generate code implementations that directly render into the given reference webpages, given the screenshots as input. |
EquiBench: Benchmarking Large Language Models’ Reasoning about Program Semantics via Equivalence Checking (2025.emnlp-main)
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Anjiang Wei, Jiannan Cao, Ran Li, Hongyu Chen, Yuhui Zhang, Ziheng Wang, Yuan Liu, Thiago S. F. X. Teixeira, Diyi Yang, Ke Wang, Alex Aiken
| Challenge: | EquiBench is a new benchmark to evaluate large language models' ability to reason about program semantics . Unlike natural language, code is executable. |
| Approach: | They propose a benchmark to evaluate large language models through equivalence checking . EquiBench consists of 2400 program pairs across four languages and six categories . |
| Outcome: | The proposed benchmark consists of 2400 program pairs across four languages and six categories. |
DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules (2023.emnlp-main)
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| Challenge: | Existing large language models (LLMs) that focus on Standard American English (SAE) often suffer from performance degradation when applied to other dialects. |
| Approach: | They propose a modular approach to imbue SAE-trained models with multi-dialectal robustness . they propose adapters which handle specific linguistic features to imbibe SAe-taught models . |
| Outcome: | The proposed approach improves performance across multiple dialects and dialects. |