DGoT: Dynamic Graph of Thoughts for Scientific Abstract Generation (2024.lrec-main)
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| Challenge: | Existing methods for generating abstracts involve collecting domain data and training corresponding models to complete the task of text summarization. |
| Approach: | They propose a method to train language models based on domain datasets and a Dynamic Graph of Thought (DGoT) which inherits the advantages of existing GoT prompt approach while reducing model reasoning cost. |
| Outcome: | The proposed method saves the cost of model training and improves reliability due to the hallucination problem of LLMs. |
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