Papers by Jane Yu
Efficient Tool Use with Chain-of-Abstraction Reasoning (2025.coling-main)
Copied to clipboard
Silin Gao, Jane Dwivedi-Yu, Ping Yu, Xiaoqing Ellen Tan, Ramakanth Pasunuru, Olga Golovneva, Koustuv Sinha, Asli Celikyilmaz, Antoine Bosselut, Tianlu Wang
| Challenge: | Recent large language models have made progress at interpreting and executing instructions. |
| Approach: | They propose a method to decouple general reasoning from specialized knowledge . they propose to use abstract reasoning chains and domain tools to reify each chain . |
| Outcome: | The proposed method outperforms baseline methods on QA and mathematical reasoning domains. |
Culture Cartography: Mapping the Landscape of Cultural Knowledge (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) can empower users to be more knowledgeable, productive, and creative, but their utility is often diminished for under-represented groups and cultures. |
| Approach: | They propose a methodology that operationalizes a mixed-initiative approach to finding culture-specific knowledge that is salient to in-group users but unknown to LLMs. |
| Outcome: | The proposed method improves the accuracy of LLMs on culturally-competent language models by 19.2%. |
The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems (2022.acl-long)
Copied to clipboard
| Challenge: | Moral integrity corpus captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs). |
| Approach: | They propose a resource that captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs). |
| Outcome: | The proposed resource captures the moral assumptions of 38k prompt-reply pairs, using 99k distinct Rules of Thumb (RoTs). |
Quantifying Adaptability in Pre-trained Language Models with 500 Tasks (2022.naacl-main)
Copied to clipboard
| Challenge: | a recent study examines the features and limits of LM adaptability to new tasks . many questions about the nature and limits remain unanswered . |
| Approach: | They evaluate adaptability to new tasks using a new benchmark, TaskBench500 . they find adaptation procedures differ dramatically in their ability to memorize small datasets . |
| Outcome: | The proposed benchmark compares 500 procedurally generated sequence modeling tasks to a new benchmark. |