Papers with TA
Friendly Topic Assistant for Transformer Based Abstractive Summarization (2020.emnlp-main)
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| Challenge: | Abstractive document summarization is a comprehensive task in natural language processing. |
| Approach: | They propose a topic assistant that rearranges and learns document semantics . they propose TA that is compatible with Transformer-based models and user-friendly . |
| Outcome: | The proposed model is compatible with Transformer-based models and user-friendly. |
Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters (2026.acl-long)
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| Challenge: | Existing research lacks systematic analysis of the applicability and methodology of cross-modal skill injection. |
| Approach: | They investigate the applicability and methodology of cross-modal skill injection by integrating a domain-expert LLM into a VLM. |
| Outcome: | The proposed method enables transfer of domain-specific expertise from Large Language Models (LLMs) to VLMs without incurring additional training data requirements or significant computational overhead. |
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. |
TADI: Topic-aware Attention and Powerful Dual-encoder Interaction for Recall in News Recommendation (2023.findings-emnlp)
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| Challenge: | Recent researches of news recall adopt dual-encoder architecture as it provides a much faster recall scheme and they encode each word equally. |
| Approach: | They propose a model which weights words according to news topics and a module which enhances dual-encoder interaction. |
| Outcome: | The proposed model outperforms state-of-the-art models in a series of experiments. |