```json
{
  "title": "arXiv Multi-Citations",
  "author": "",
  "site": "",
  "published": ""
}
```

## Introduction

Recurrent models typically factor computation along the symbol positions of the input and output sequences, as explored in prior work [^6] [^1] [^2]. Some models use a single citation [^5] while others reference two works [^3] [^4].

## References

[^1]: Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. *CoRR*, 2014.

[^2]: Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014.

[^3]: Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. Convolutional sequence to sequence learning. 2017.

[^4]: Alex Graves. Generating sequences with recurrent neural networks. 2013.

[^5]: Sepp Hochreiter and Jurgen Schmidhuber. Long short-term memory. 1997.

[^6]: Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. Sequence to sequence learning with neural networks. 2014.