# Analysis of Language Change in Collaborative Instruction Following

arXiv: https://arxiv.org/abs/2109.04452 · HTML: https://mlsanity.com/paper/2109.04452/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 9 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Executing Instructions in Situated Collaborative Interactions](https://mlsanity.com/paper/1910.03655/) (2019)
- [Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings](https://mlsanity.com/paper/1704.07130/) (2017)
- [Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding](https://mlsanity.com/paper/1703.10186/) (2017)
- [The PhotoBook Dataset: Building Common Ground through Visually-Grounded Dialogue](https://mlsanity.com/paper/1906.01530/) (2019)
- [A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context](https://mlsanity.com/paper/1907.03399/) (2019)
- [Characterizing the dynamics of learning in repeated reference games](https://mlsanity.com/paper/1912.07199/) (2019)
- [Naturalizing a Programming Language via Interactive Learning](https://mlsanity.com/paper/1704.06956/) (2017)
- [Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks](https://mlsanity.com/paper/2002.01510/) (2020)
- [Precise but Natural Specification for Robot Tasks](https://mlsanity.com/paper/1803.02238/) (2018)

## Built on by (most cited first, 9 of 9)

- [Continual Learning for Instruction Following from Realtime Feedback](https://mlsanity.com/paper/2212.09710/) (2022)
- [Talk Less, Interact Better: Evaluating In-context Conversational Adaptation in Multimodal LLMs](https://mlsanity.com/paper/2408.01417/) (2024)
- [Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches](https://mlsanity.com/paper/2211.08371/) (2022)
- [MUG: Interactive Multimodal Grounding on User Interfaces](https://mlsanity.com/paper/2209.15099/) (2022)
- [CoGen: Learning from Feedback with Coupled Comprehension and Generation](https://mlsanity.com/paper/2408.15992/) (2024)
- [Retrospective Learning from Interactions](https://mlsanity.com/paper/2410.13852/) (2024)
- [Situated Instruction Following](https://mlsanity.com/paper/2407.12061/) (2024)
- [CB2: Collaborative Natural Language Interaction Research Platform](https://mlsanity.com/paper/2303.08127/) (2023)
- [Online library learning in human visual puzzle solving](https://mlsanity.com/paper/2603.23244/) (2026)

## Abstract (arXiv)

We analyze language change over time in a collaborative, goal-oriented instructional task, where utility-maximizing participants form conventions and increase their expertise. Prior work studied such scenarios mostly in the context of reference games, and consistently found that language complexity is reduced along multiple dimensions, such as utterance length, as conventions are formed. In contrast, we find that, given the ability to increase instruction utility, instructors increase language complexity along these previously studied dimensions to better collaborate with increasingly skilled instruction followers.