Papers by Cathy Jiao
On the Feasibility of In-Context Probing for Data Attribution (2025.findings-naacl)
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| Challenge: | In-context probing (ICP) can be used to identify training data that contributes to model outputs, but many data attribution methods, such as influence functions, use model gradients and are computationally expensive. |
| Approach: | They propose to use in-context probing (ICP) to proxy for gradient-based data attribution for data selection under conditions contingent on data similarity. |
| Outcome: | The proposed method can be used to identify training data that contribute to model outputs and fine tune models on training data. |
Improving compositional generalization for multi-step quantitative reasoning in question answering (2022.emnlp-main)
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| Challenge: | Quantitative reasoning is an important aspect of question answering when numeric and verbal cues interact to indicate sophisticated, multi-step programs. |
| Approach: | They propose a method that encourages QA models to adjust attention patterns and capture input/output alignments that are meaningful to the reasoning task. |
| Outcome: | The proposed approach improves program accuracy and renders models more robust against overfitting as the number of reasoning steps grows. |
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning (2022.emnlp-main)
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| Challenge: | Instruction tuning is emerging in NLP, but has not been explored for dialogue-related tasks. |
| Approach: | They propose an instruction tuning framework for dialogue that leverages natural language instructions with language models to induce zero-shot generalization on unseen tasks. |
| Outcome: | The proposed framework enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection. |