# Continual adaptation for efficient machine communication

arXiv: https://arxiv.org/abs/1911.09896 · HTML: https://mlsanity.com/paper/1911.09896/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

## Built on by (most cited first, 28 of 28)

- [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022)
- [From partners to populations: A hierarchical Bayesian account of coordination and convention](https://mlsanity.com/paper/2104.05857/) (2021)
- [Continual Learning for Instruction Following from Realtime Feedback](https://mlsanity.com/paper/2212.09710/) (2022)
- [Continual Learning for Grounded Instruction Generation by Observing Human Following Behavior](https://mlsanity.com/paper/2108.04812/) (2021)
- [Talk Less, Interact Better: Evaluating In-context Conversational Adaptation in Multimodal LLMs](https://mlsanity.com/paper/2408.01417/) (2024)
- [Training Language Models for Social Deduction with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2502.06060/) (2025)
- [Learning to communicate about shared procedural abstractions](https://mlsanity.com/paper/2107.00077/) (2021)
- [Towards More Human-like AI Communication: A Review of Emergent Communication Research](https://mlsanity.com/paper/2308.02541/) (2023)
- [How Abstract Is Linguistic Generalization in Large Language Models? Experiments with Argument Structure](https://mlsanity.com/paper/2311.04900/) (2023)
- [Dealing with Semantic Underspecification in Multimodal NLP](https://mlsanity.com/paper/2306.05240/) (2023)
- [Natural Language Processing RELIES on Linguistics](https://mlsanity.com/paper/2405.05966/) (2024)
- [Measuring How (Not Just Whether) VLMs Build Common Ground](https://mlsanity.com/paper/2509.03805/) (2025)
- [LVLMs and Humans Ground Differently in Referential Communication](https://mlsanity.com/paper/2601.19792/) (2026)
- [Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind](https://mlsanity.com/paper/2305.19933/) (2023)
- [CoGen: Learning from Feedback with Coupled Comprehension and Generation](https://mlsanity.com/paper/2408.15992/) (2024)
- [Post-training for Efficient Communication via Convention Formation](https://mlsanity.com/paper/2508.06482/) (2025)
- [Success and Cost Elicit Convention Formation for Efficient Communication](https://mlsanity.com/paper/2510.24023/) (2025)
- [Retrospective Learning from Interactions](https://mlsanity.com/paper/2410.13852/) (2024)
- [Regularized Conventions: Equilibrium Computation as a Model of Pragmatic Reasoning](https://mlsanity.com/paper/2311.09712/) (2023)
- [Policy Learning with a Language Bottleneck](https://mlsanity.com/paper/2405.04118/) (2024)
- [Gesturing Toward Abstraction: Multimodal Convention Formation in Collaborative Physical Tasks](https://mlsanity.com/paper/2602.08914/) (2026)
- [Targeted Data Acquisition for Evolving Negotiation Agents](https://mlsanity.com/paper/2106.07728/) (2021)
- [Know your audience: specializing grounded language models with listener subtraction](https://mlsanity.com/paper/2206.08349/) (2022)
- [I see what you mean: Co-Speech Gestures for Reference Resolution in Multimodal Dialogue](https://mlsanity.com/paper/2503.00071/) (2025)
- [Direct Preference Optimization for Adaptive Concept-based Explanations](https://mlsanity.com/paper/2505.15626/) (2025)
- [Ad hoc conventions generalize to new referents](https://mlsanity.com/paper/2509.05566/) (2025)
- [Which one is banana man? Evaluating vision-language models in multi-turn pragmatic interpretation](https://mlsanity.com/paper/2608.29571/) (2026)
- [Efficient Test-Time Adaptation through Human-AI Interaction](https://mlsanity.com/paper/2609.04141/) (2026)

## Abstract (arXiv)

To communicate with new partners in new contexts, humans rapidly form new linguistic conventions. Recent neural language models are able to comprehend and produce the existing conventions present in their training data, but are not able to flexibly and interactively adapt those conventions on the fly as humans do. We introduce an interactive repeated reference task as a benchmark for models of adaptation in communication and propose a regularized continual learning framework that allows an artificial agent initialized with a generic language model to more accurately and efficiently communicate with a partner over time. We evaluate this framework through simulations on COCO and in real-time reference game experiments with human partners.