Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have demonstrated their significant potential to be applied for addressing various application tasks.
2018
Earlier work this paper cites.
Chen, X., Chen, H., Xu, H., Zhang, Y., Cao, Y., Qin, Z., Zha, H.: Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 765–774 (2019)
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Shi, S., Zhang, M., Yu, X., Zhang, Y., Hao, B., Liu, Y., Ma, S.: Adaptive feature sampling for recommendation with missing content feature values. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. pp. 1451–1460 (2019)
2019
Earlier work this paper cites.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Khashabi, D., Min, S., Khot, T., Sabharwal, A., Tafjord, O., Clark, P., Hajishirzi, H.: Unifiedqa: Crossing format boundaries with a single qa system (2020)
2020
Earlier work this paper cites.
Li, L., Zhang, Y., Chen, L.: Generate neural template explanations for recommendation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. pp. 755–764 (2020)
2020
Earlier work this paper cites.
Sun, C., Liu, H., Liu, M., Ren, Z., Gan, T., Nie, L.: Lara: Attribute-to-feature adversarial learning for new-item recommendation. In: Proceedings of the 13th international conference on web search and data mining. pp. 582–590 (2020)
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Xu, Y., Zhu, C., Xu, R., Liu, Y., Zeng, M., Huang, X.: Fusing context into knowledge graph for commonsense question answering (2021)
2021
Cited alongside, same era.
Yuan, F., Zhang, G., Karatzoglou, A., Jose, J., Kong, B., Li, Y.: One person, one model, one world: Learning continual user representation without forgetting. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 696–705 (2021)
2021
Cited alongside, same era.
LeCun, Y.: A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27. Open Review 62
2022
Later among the works it cites.
Parisi, A., Zhao, Y., Fiedel, N.: Talm: Tool augmented language models (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zhang, Y., Ding, H., Shui, Z., Ma, Y., Zou, J., Deoras, A., Wang, H.: Language models as recommender systems: Evaluations and limitations (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Cited alongside, same era.
Fu, Yao; Peng, H., Khot, T.: How does gpt obtain its ability? tracing emergent abilities of language models to their sources. Yao Fu’s Notion (Dec 2022), https://yaofu.notion.site/How-does-GPT-Obtain-its-Ability-Tracing-Emergent-Abilities-of-Language-Models-to-their-Sources-b9a57ac0fcf74f30a1ab9e3e36fa1dc1
2022
Cited alongside, same era.
Geng, S., Liu, S., Fu, Z., Ge, Y., Zhang, Y.: Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5). In: Proceedings of the 16th ACM Conference on Recommender Systems. pp. 299–315 (2022)
2022
Cited alongside, same era.
2023
Closest in time.
Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language models can teach themselves to use tools (2023)
2023
Closest in time.
2023
Closest in time.
Yu, W., Iter, D., Wang, S., Xu, Y., Ju, M., Sanyal, S., Zhu, C., Zeng, M., Jiang, M.: Generate rather than retrieve: Large language models are strong context generators (2023)
2023
Closest in time.