Emergent Communication Under Misinformation
Heeyoung Lee, Kyungwoo Song
Abstract
Social interactions are characterized by both adversarial and cooperative aspects. Communications between agents may also involve adversarially motivated actors. Messages can pass through intermediaries with malicious intent before reaching the intended receiver. These actors may modify the message to induce misunderstanding from the receiver while preserving the overall characteristics of the message. This form of misinformation is prevalent in real-world communications and may affect the dynamics under which communication protocols are developed. However, this aspect of social interaction is relatively underexplored in many emergent communication studies that aim to understand the environmental factors behind the emergence of languages' characteristics. This work explores how misinformation affects language emergence with a focus on compositionality. We design a communication game containing a malign intermediary between the sender and receiver. We find that risks of malign misrepresentation promote the emergence of compositional languages in simulations of communicative agents. Furthermore, we observe that adaptability of malign intermediaries is a crucial factor in forming a consistent pressure toward compositionality and that partial misinformation, in which the intermediary targets only a subset of attributes, can also induce compositionality.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Builds on23
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Compositional languages emerge in a neural iterated learning modelYi Ren, Shangmin Guo, Matthieu Labeau, Shay B. Cohen et al.ICLR 2020 · 111 citations
- Emergent Communication at ScaleRahma Chaabouni, Florian Strub, Florent Altché, Eugene Tarassov et al.ICLR 2022 · 65 citations
- Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent LanguageZhenlin Xu, Marc Niethammer, Colin RaffelNeurIPS 2022 · 59 citations
- Language Model Alignment with Elastic ResetMichael Noukhovitch, Samuel Lavoie, Florian Strub, Aaron C. CourvilleNeurIPS 2023 · 42 citations
Related papers
- One-to-Many Communication and Compositionality in Emergent CommunicationHeeyoung LeeEMNLP 2024 · 3 citations
- Emergent Communication: Generalization and Overfitting in Lewis GamesMathieu Rita, Corentin Tallec, Paul Michel, Jean-Bastien Grill et al.NeurIPS 2022 · 41 citations
- Emergent Communication with Conversational RepairMitja NikolausICLR 2024 · 4 citations
- Compositionality and Generalization In Emergent LanguagesRahma Chaabouni, Eugene Kharitonov, Diane Bouchacourt, Emmanuel Dupoux et al.ACL 2020 · 40 citations
- Entropy Minimization In Emergent LanguagesEugene Kharitonov, Rahma Chaabouni, Diane Bouchacourt, Marco BaroniICML 2020 · 39 citations
