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# System prepended metadata

title: Thesis Revision

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# Thesis Revision

### 20250306

1. Page 2
    - Before: Moreover, recent work by ~~Zhao~~ et al. (2024) explores retrieval-augmented generation techniques that empower
LLMs with domain-specifc knowledge in educational contexts.
    - After: Moreover, recent work by **Lu, R** et al. (2024) explores retrieval-augmented generation techniques that empower LLMs with domain-specific knowledge in educational contexts.
    
2. Page 5
    - Before: methodology for developing TCNNet-9B involves a ~~multistep~~ process of data collection, ...
    - After: methodology for developing TCNNet-9B involves a **multi-step** process of data collection, ...

3. Page 9
    - Before: the networking domain. Using GPT-4o, we generated both single-turn and ~~multiturn~~ Q&A pairs. Single-turn Q&A pairs addressed straightforward questions, whereas ~~multiturn~~ Q&A pairs simulated more complex interactions requiring follow-up questions and contextual understanding
    - After: the networking domain. Using GPT-4o, we generated both single-turn and **multi-turn** Q&A pairs. Single-turn Q&A pairs addressed straightforward questions, whereas **multi-turn** Q&A pairs simulated more complex interactions requiring follow-up questions and contextual understanding

4. Page 16
    - Before: ~~Zhao, H.~~ et al. Empowering large Language models to leverage domain-specifc knowledge in E-Learning. Appl. Sci. 14(12), 5264 (2024).
    - After: **Lu, R**. et al. Empowering large Language models to leverage domain-specifc knowledge in E-Learning. Appl. Sci. 14(12), 5264 (2024).

5. Page 16
    - Before: Liang Xu, ~~C. L. U. E.~~ et al. A Chinese language understanding evaluation Benchmark. In: Proceedings of the 28th International Conference on Computational Linguistics (2020).
    - After: Liang Xu, et al. **CLUE:** A Chinese language understanding evaluation Benchmark. In: Proceedings of the 28th International Conference on Computational Linguistics (2020).




