Papers with LLM-in-the-loop
A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution (2024.naacl-long)
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| Challenge: | Existing state-of-the-art event coreference resolution systems rely on spurious and spurious associations in the input mention pair text. |
| Approach: | They propose a rationale-centric counterfactual data augmentation method that leverages the debiasing capability of counterfact data haussed by LLM-in-the-loop to mitigate spurious association while emphasizing causation. |
| Outcome: | The proposed method achieves state-of-the-art on three popular cross-document benchmarks and demonstrates robustness in out-of domain scenarios. |
LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis (2023.findings-emnlp)
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| Challenge: | Recent research shows that large language models can replicate human-like behavior in various tasks. |
| Approach: | They propose a framework for human-LLM collaboration to conduct TA with in-context learning (ICL) they propose to use survey data to frame discussions with an LLM to generate a final codebook for TA. |
| Outcome: | The proposed framework outperforms crowd workers on text-annotation tasks and yields similar coding quality to that of human coders but reduces TA’s labor and time demands. |