An Outlook for AI Innovation in Multimodal Communication Research

Author:

Henlein AlexanderORCID,Bauer AnastasiaORCID,Bhattacharjee ReetuORCID,Ćwiek AleksandraORCID,Gregori AlinaORCID,Kügler FrankORCID,Lemanski JensORCID,Lücking AndyORCID,Mehler AlexanderORCID,Prieto Pilar,Sánchez-Ramón Paula G.ORCID,Schepens JobORCID,Schulte-Rüther MartinORCID,Schweinberger Stefan R.ORCID,von Eiff Celina I.ORCID

Publisher

Springer Nature Switzerland

Reference223 articles.

1. Aguirre-Celis, N., Miikkulainen, R.: Understanding the semantic space: how word meanings dynamically adapt in the context of a sentence. In: Proceedings of the 2021 Workshop on Semantic Spaces at the Intersection of NLP, Physics, and Cognitive Science (SemSpace), pp. 1–11 (2021)

2. Aiyappa, R., An, J., Kwak, H., Ahn, Y.Y.: Can we trust the evaluation on chatgpt? arXiv preprint arXiv:2303.12767 (2023)

3. Akbari, H., et al.: VATT: transformers for multimodal self-supervised learning from raw video, audio and text. Adv. Neural. Inf. Process. Syst. 34, 24206–24221 (2021)

4. Alkaissi, H., McFarlane, S.I.: Artificial hallucinations in chatgpt: implications in scientific writing. Cureus 15(2) (2023)

5. Almazrouei, E., et al.: Falcon-40B: an open large language model with state-of-the-art performance. Find. Assoc. Comput. Linguist. ACL 2023, 10755–10773 (2023)

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