Papers by Laura Biester
Building Location Embeddings from Physical Trajectories and Textual Representations (2020.aacl-main)
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| Challenge: | Using a dataset consisting of the location trajectories of 729 students over a seven month period, we investigate whether embeddings can represent aspects such as location presence or location functionality. |
| Approach: | They propose to use location embeddings to generate embeddables of sequences of locations a student has visited to identify surface properties captured in the representations. |
| Outcome: | The proposed models can be used to predict depression levels and area of study, and can be applied to complex tasks such as predicting area of studies and depression levels. |
Sports and Women’s Sports: Gender Bias in Text Generation with Olympic Data (2025.naacl-short)
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| Challenge: | Large Language Models (LLMs) generate text that is stereotypical or not representative of the viewpoints and values of historically marginalized demographic groups. |
| Approach: | They propose to use data from the Olympic Games to investigate gender bias in large language models. |
| Outcome: | The proposed model consistently biased against women when the gender is ambiguous in the prompt, revealing pervasive gender bias in LLMs in the context of athletics. |
Eeyore: Realistic Depression Simulation via Expert-in-the-Loop Supervised and Preference Optimization (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have been explored for mental healthcare training and therapy client simulation, but they fail to authentically capture diverse client traits and psychological conditions. |
| Approach: | They propose an 8B model optimized for realistic depression simulation with expert input at every stage. |
| Outcome: | The model outperforms GPT-4o in linguistic authenticity and profile adherence. |
Representing and Clustering Errors in Offensive Language Detection (2025.naacl-srw)
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| Challenge: | Sentence-BERT embeddings of Large Language Model (LLM)-generated linguistic features give the most interpretable clustering for Arabic errors. |
| Approach: | They evaluate the K-Means clustering of four text representations for the task of offensive language detection in English and Levantine Arabic. |
| Outcome: | The proposed clustering of four text representations for offensive language detection in English and Levantine Arabic gives the most human-interpretable clustering for English errors and the grouping is mainly based on the targeted group in the text. |
Dark & Stormy: Modeling Humor in Sentences from the Bulwer-Lytton Fiction Contest (2026.acl-short)
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| Challenge: | a corpus of "bad" humor sentences from the Bulwer-Lytton Fiction Contest 1 is presented . standard humor detection models perform poorly on corpus, and these sentences combine features common in existing humor datasets with metaphor, metafiction and simile. |
| Approach: | They propose to analyze a corpus of "bad" humor sentences from the Bulwer-Lytton Fiction Contest . they use literary devices to synthesize contest-style sentences that imitate the form but exaggerate the effect . |
| Outcome: | The proposed corpus of sentences from the Bulwer-Lytton Fiction Contest 1 is analyzed . it shows that the sentences combine features common in existing humor datasets with metaphor, metafiction and simile. |
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models (2024.lrec-main)
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Oana Ignat, Zhijing Jin, Artem Abzaliev, Laura Biester, Santiago Castro, Naihao Deng, Xinyi Gao, Aylin Ece Gunal, Jacky He, Ashkan Kazemi, Muhammad Khalifa, Namho Koh, Andrew Lee, Siyang Liu, Do June Min, Shinka Mori, Joan C. Nwatu, Veronica Perez-Rosas, Siqi Shen, Zekun Wang, Winston Wu, Rada Mihalcea
| Challenge: | Recent advances in large language models have led to misleading public discourse that “it’s all been solved.” |
| Approach: | They identify 14 research areas encompassing 45 research directions that require new research and are not directly solvable by LLMs. |
| Outcome: | The research areas identified are 45 research directions that require new research and are not directly solvable by LLMs. |