N e LLC om-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use (2025)

Abstract: Lexical semantic change has primarily been investigated with observational and experimental methods; however, observational methods (corpus analysis, distributional semantic modeling) cannot get at causal mechanisms, and experimental paradigms with humans are hard to apply to semantic change due to the extended diachronic processes involved. This work introduces NeLLCom-Lex, a neural-agent framework designed to simulate semantic change by first grounding agents in a real lexical system (e.g. English) and then systematically manipulating their communicative needs. Using a well-established color naming task, we simulate the evolution of a lexical system within a single generation, and study which factors lead agents to: (i) develop human-like naming behavior and lexicons, and (ii) change their behavior and lexicons according to their communicative needs. Our experiments with different supervised and reinforcement learning pipelines show that neural agents trained to ‘speak’ an existing language can reproduce human-like patterns in color naming to a remarkable extent, supporting the further use of NeLLCom-Lex to elucidate the mechanisms of semantic change.

## introduction a central goal in the study of language emergence and evolution is to understand how cognitive and communicative pressures shape linguistic systems @xcite @xcite . more broadly, different scholarly traditions and fields of study have stressed the dynamic relationship between language use, on the one hand, and the linguistic system, on the other, be it at the synchronic @xcite @xcite or diachronic level @xcite @xcite . in particular, the referential communicative context has been found to shape language use across timescales, ranging from pragmatically-driven naming choices in synchronous communication @xcite to the longer-term emergence of shared lexical conventions and to their semantic shift in response to changing communicative needs (see, e.g., artificial language learning studies in @xcite and @xcite ). while the relationship is widely acknowledged, studying the implicated mechanisms across all of these timescales is challenging. in recent decades, a large body of work has therefore focused on agent-based computational simulations @xcite @xcite , receiving a renewed increased interest with the deployment of modern neural-network agents @xcite @xcite @xcite . we follow this tradition, focusing on the lexicon, which is essential for enabling reference to entities in the world and therefore plays a key role in effective communication @xcite . computational modeling studies have used communication games to simulate the emergence of lexical systems in neural network-based agents @xcite @xcite . these methods enable control over contextual variables and make it possible to simulate language change across different timescales. despite these advances, most emergent communication studies begin with blank-slate agents and focus on how contextual variability shapes novel communication systems (e.g., @xcite . by contrast, little work has focused on modeling how an existing lexical system might adapt pragmatically to changing communicative needs, an approach that allows both more direct comparisons to human behavior and the possibility of simulating specific trajectories of language change. in this paper, we start exploring precisely this approach by introducing the nellcom-lex frame-work. we build on nellcom @xcite , a flexible agent-communication framework that combines generic supervised learning (sl) and reinforcement learning (rl) objectives, making it wellsuited for exploring how communication pressures shape language use and lexical adaptation across different communicative contexts. as a first exploration of the dynamics between use and structure, we focus on dyadic interactions and lexical evolution across a single generation; as well as on a specific contextual factor, namely the differences in the granularity of the distinctions speakers need to make in their communicative environments. what we are thus simulating is linguistic adaptation across a lifetime, brought upon by communicative needs regarding reference. through a number of experiments with a well-established color naming task @xcite and various evaluation methodologies inspired in the study of human behavior @xcite , we show that our nellcom-lex agents can reproduce human-like pragmatic naming behavior, while maintaining overall efficient lexical systems. we also find agent naming behavior changes in response to different communication needs in the environment. overall, our results support the use of nellcom-lex to investigate the interplay between language use and language structure in the lexicon. 1 2 background it is known that, synchronically, speakers choose words that best identify the referent given the communicative context. for example, if a target object, e.g., a dalmatian, appears in a context surrounded by other dogs, the word dog may be too broad to identify the referent. in such cases, speakers tend to choose more specific words (e.g., dalmatian), reflecting a pragmatic naming choice driven by communicative need @xcite . while it is easy to justify this kind of pragmatic adaptation to context, it is not so straightforward to explain why there is redundancy in the lexicon in the first place, in that it includes words at higher and lower levels of informativeness; why do languages not have only more informative words (dalmatian, golden retriever, chihuahua), or why do they not only have less informative words that they modify (dog with black spots)? @xcite hypothesized that this redundancy in the lexicon, coupled with pragmatic adaptation to context, is a good solution to the trade-off between communicative pressures to be understood and cognitive pressures to expend low effort. trade-offs between effort and informativeness in language use have been studied within the rational speech acts (rsa) framework @xcite @xcite @xcite . for instance, @xcite collected experimental data from color reference games and built pragmatic neural speaker and listener models within the rsa framework. experiments show that the embedded speaker model shows pragmatic behaviors, i.e., modulating utterance lengths based on contextual difficulty. subsequent work by @xcite extended this line and found that only models explicitly trained through a pragmatic reasoning objective (or approximations thereof) were able to adjust utterance length to communicative context. in both of these studies, messages could be multi-word utterances, and utterance length was used as a measure of effort while variations in informativeness of individual word choices were not considered. complementary research on emergent communication using rl highlights similar efficiency tradeoffs, though primarily at the level of the lexical system @xcite @xcite @xcite . for example, replicating earlier findings @xcite from largescale data analysis, @xcite examined pressures in emergent communication among neural agents, focusing on the trade-off between communicative accuracy and complexity in color partitioning in agents' naming systems. similarly, @xcite modeled combined pressures from communication and transmission of signals over generations to explain the emergence of efficient color naming systems. overall, these studies focus on how general pressures coming from language learning and use shape properties of the lexical system, rather than on how pragmatic language use in context shapes those properties. while @xcite studied emerging lexicons in context, their models developed languages from scratch using symbolic data, without grounding in real-world lexical meanings. the work by @xcite offers a promising bridge between these two traditions. they introduced a novel measure of informativeness for words and lexical systems that captures both context adaptation in language use and the structure of lexical systems. meanwhile, @xcite introduced the nellcom framework, which adopts a two-phase learning paradigm for simulating the emergence of syntactic universals @xcite @xcite without the need of complex, task-specific listener models typical of rsa. in nellcom, agents are first trained to use a language with human-like structural properties via supervised learning (sl); then, those same agents are jointly optimized through interaction via reinforcement learning (rl). building on these advances, we propose nellcom-lex to simulate lexical change and measure lexical properties at both the language use and system level, using the metrics from @xcite . this enables a unified analysis of how communicative needs shape both in-context communication and lexicon structure. ## nellcom-lex: a neural-agent framework for lexical systems extending nellcom to simulate the evolution of lexical meaning via a referential game requires a number of non-trivial modifications. in terms of meaning representations, nellcom-lex replaces symbolic triplets with continuous perceptual inputs (i.e., cielab color vectors), providing a grounded representation space suitable for modeling lexical meaning in the color domain. secondly, shifting from a meaning reconstruction task to a referential discrimination game requires architectural changes: instead of processing utterance sequences with recurrent models, nellcom-lex uses single-symbol utterances and incorporates a context encoder to model distractors. this section describes the resulting task and architecture. ## experimental setup our goal is to design agents that learn to use and adapt an existing lexical system in a way that mirrors human behavior. this involves adapting word use to the communicative context (using more informative color names in hard contexts and less informative ones in easier contexts) at multiple scales: that is, situationally in synchronous communication, but also in the longer term by updating shared lexical conventions according to changes in the communicative needs of the environment. we investigate the factors leading to more human-like behaving agents through two sets of experiments: different sl+rl training pipelines with varying access to context ( §5.1) and variations in the communicative environment of the agents during communication training ( §5.2), using the dataset and evaluation metrics described below. ## experiments and results we carry out two sets of experiments. the first set ( §5.1) aims at testing the conditions under which agents develop pragmatic naming behavior and lexical properties that are similar to those of humans. in particular, we check how and when access to context is necessary for human-like lexicons to emerge. the second set ( §5.2) tests the effect of different communicative needs on the resulting behavior and lexicons; in particular, it exposes agents to more and less granular color distinctions. ## discussion and conclusion we introduced nellcom-lex, a neural-agent framework to study the interplay between language use and the evolution of lexical systems. using a well-established color naming task for which human naming data is available, we examined which factors lead to more human-like pragmatic naming behavior in agents initially trained to 'speak' an existing lexical system (in our case: english). we found that agents learn to adapt their word use to context (i.e., using more informative terms in harder contexts and less informative ones in easier contexts) regardless of whether context is provided to them during supervised learning, communication learning, or both. furthermore, we found that optimizing communication accuracy through a referential game led the agents to develop a richer, more adaptive lexicon. in additional experiments, we simulated changing communicative needs in the agents' environment by manipulating the distribution of different context difficulties during rl training. results show that agents trained mostly in close contexts developed stronger pragmatic adaptations than those trained in far contexts, supporting the idea that pragmatic efficiency is driven by communicative needs. overall, our experiments show that neural agents can reproduce human-like patterns in color naming behavior, supporting their use as models for studying how language evolves under communicative pressures. in future work, we plan to use nellcom-lex to simulate specific instances of lexical semantic change, such as meaning narrowing and broadening, that have been traditionally assumed to be driven by changes in speakers' communicative needs, but whose mechanisms remain to be explored @xcite .