以下是一些相关的文章，可供参考：
1. 教科书问答任务：
- Rajpurkar, P., et al. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. arXiv preprint arXiv:1606.05250.
- Chen, D., et al. (2017). Reading Wikipedia to Answer Open-Domain Questions. arXiv preprint arXiv:1704.00051.
- Joshi, M., et al. (2017). TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension. arXiv preprint arXiv:1705.03551.
2. 知识图谱的关系补全：
- Zeng, Y., et al. (2018). Knowledge graph embedding for link prediction: a comparative analysis. Journal of Computer Science and Technology, 33(2), 339-355.
- Lin, Y., et al. (2015). Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (pp. 2181-2187).
- Wang, Q., et al. (2014). Knowledge graph and text jointly embeddings. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 623-632).
3. 大语言模型：
- Radford, A., et al. (2019). Language Models are Unsupervised Multitask Learners. OpenAI Blog.
- Devlin, J., et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
- Dai, Z., et al. (2019). Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context. arXiv preprint arXiv:1901.02860.
请注意，这只是一小部分相关文献。如需更多文献或者特定领域的文献，请提供更详细的信息。 |
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