Papers by Jessica Lin
GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction (2025.findings-acl)
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| Challenge: | Existing methods for graded entity salience are subjective but lack consistency. |
| Approach: | They propose a method for graded entity salience that combines subjective judgments and summarization-based methods that define saliency as mention-worthiness in a summary. |
| Outcome: | The proposed approach outperforms existing methods and shows stronger correlation with human summaries and alignments. |
Toward Inclusive Language Models: Sparsity-Driven Calibration for Systematic and Interpretable Mitigation of Social Biases in LLMs (2025.findings-emnlp)
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| Challenge: | a new method to mitigate stereotypical bias in large language models is needed . inherent biases from training on vast Internet datasets can amplify harmful stereotypes . |
| Approach: | They propose a method to identify stereotypical bias in decoder-only transformer models . they apply a localization mechanism that correlates internal activations with a new Context Influence score . |
| Outcome: | The proposed method reduces stereotypical biases on BBQ, StereoSet, and CrowS-Pairs while improving reasoning performance on MMLU by 10%. |
DART: Open-Domain Structured Data Record to Text Generation (2021.naacl-main)
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Linyong Nan, Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand Sivaprasad, Chiachun Hsieh, Xiangru Tang, Aadit Vyas, Neha Verma, Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto, Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Mutethia Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu, Yi Chern Tan, Xi Victoria Lin, Caiming Xiong, Richard Socher, Nazneen Fatema Rajani
| Challenge: | Data-to-text annotations can be costly when dealing with tables with nontrivial structures. |
| Approach: | They propose a procedure for extracting semantic triples from tables that encodes their structures by exploiting table headers and table title. |
| Outcome: | The proposed method exploits the semantic dependencies between table headers and title to extract semantic triples from tables. |
GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains (2024.emnlp-main)
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Yang Janet Liu, Tatsuya Aoyama, Wesley Scivetti, Yilun Zhu, Shabnam Behzad, Lauren Levine, Jessica Lin, Devika Tiwari, Amir Zeldes
| Challenge: | Existing shallow discourse parsing systems focus on the Wall Street Journal corpus, but the data is limited to the news domain and is 35 years old. |
| Approach: | They propose to use the Wall Street Journal corpus as a benchmark for PDTB-style shallow discourse parsing. |
| Outcome: | The proposed dataset is compatible with PDTB, but suffers from degradation out-of-domain. |
Expect the Unexpected? Testing the Surprisal of Salient Entities (2026.acl-long)
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| Challenge: | Existing work on the Uniform Information Density hypothesis has neglected the relative salience of discourse participants. |
| Approach: | They propose to use an annotated text to examine how overall salience of entities in discourse relates to surprisal. |
| Outcome: | The proposed method shows that global salience is a mechanism shaping information distribution in discourse. |
GUMsley: Evaluating Entity Salience in Summarization for 12 English Genres (2024.eacl-long)
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| Challenge: | Existing work on salient entity extraction relies on crowdsourcing or user statistics to derive labels for entities. |
| Approach: | They propose a dataset that defines salience using human summaries and shows high agreement between annotations based on whether a source entity is mentioned in the summary. |
| Outcome: | The proposed dataset shows that pre-trained models and zero-shot LLM prompting fail to capture salient entities in generated summaries. |
AfroBench: How Good are Large Language Models on African Languages? (2025.findings-acl)
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Jessica Ojo, Odunayo Ogundepo, Akintunde Oladipo, Kelechi Ogueji, Jimmy Lin, Pontus Stenetorp, David Ifeoluwa Adelani
| Challenge: | Large-scale multilingual evaluations often include only a handful of African languages due to the scarcity of high-quality data and the limited discoverability of existing datasets. |
| Approach: | They propose a multi-task benchmark to evaluate the performance of LLMs across 64 African languages, 15 tasks and 22 datasets. |
| Outcome: | The proposed benchmark compares LLMs across 64 African languages, 15 tasks and 22 datasets. |