Agents generalize to novel levels of abstraction by using adaptive linguistic strategies (2025)

Abstract: We study abstraction in an emergent communication paradigm. In emergent communication, two artificial neural network agents develop a language while solving a communicative task. In this study, the agents play a concept-level reference game. This means that the speaker agent has to describe a concept to a listener agent, who has to pick the correct target objects that satisfy the concept. Concepts consist of multiple objects and can be either more specific, i.e. the target objects share many attributes, or more generic, i.e. the target objects share fewer attributes. We tested two directions of zero-shot generalization to novel levels of abstraction: When generalizing from more generic to very specific concepts, agents utilized a compositional strategy. When generalizing from more specific to very generic concepts, agents utilized a more flexible linguistic strategy that involves reusing many messages from training. Our results provide evidence that neural network agents can learn robust concepts based on which they can generalize using adaptive linguistic strategies. We discuss how this research provides new hypotheses on abstraction and informs linguistic theories on efficient communication.

## introduction one of the most fundamental goals of artificial intelligence (ai) and natural language processing (nlp) research is to build models which can generalize well to unseen data. this is, after all, one of the crucial abilities observed in human intelligence. abstraction has been argued to be a necessary first step towards achieving generalization @xcite . but there are also alternative views such as the exemplar-based model of categories where generalization is achieved without abstraction @xcite . we believe that understanding abstraction and how it interacts with generalization, is fundamental to building well-generalizing models. humans naturally use abstraction to solve complex tasks and to communicate about strategies and solutions. well-designed ai and nlp systems cannot only benefit from good abstraction abilities in, for example, reasoning and solving complex tasks (e.g. @xcite , but interactive systems should also be able to deal with human language inputs which involve abstractions (e.g. @xcite . many researchers studying human abstraction argue for a role of language therein (see e.g., @xcite @xcite . the main idea of these accounts is that the lexicalization of concepts, i.e. having a label for a concept, helps to acquire and structure information we obtain about an entity and to observe commonalities within members of a concept in the first place. the role of language in abstraction can also be tested in computational systems. the goals of the current research are tounderstand how abstraction and generalization interact and ultimately to inform the improvement of ai and nlp systems towards achieving human-like abstraction and generalization abilities. starting from the assumption that language is useful for abstraction, we study abstraction in a communicative setting and investigate how abstraction is achieved with the help of linguistic strategies such as compositionality and the reuse of previously established messages. to gain insights into the principled mechanisms of abstraction and the role of language for abstraction, we use a language emergence scenario. in language emergence research, the idea is to define a set of assumptions and then observe how these assumptions change a language-like system that emerges during interaction (see e.g. @xcite @xcite . this modeling framework is ideal for investigating whether human-like behavior and communication strategies can emerge even in a comparatively simple communicative setup between two artificial neural network agents. in our model, two artificial neural network agents solve a reference game, where a speaker agent has to communicate a target concept to a listener agent who needs to select the correct target concept in a context. we operationalize a concept as a set of target objects following previous work @xcite . our target concepts are designed in a hierarchical fashion, ranging from very specific concepts consisting of objects where all attributes (e.g., size, color and shape) are fixed to a certain value, e.g. 'small blue circle', to very generic concepts consisting of objects where only one attribute is fixed, e.g. 'circle'. we can study abstraction by making use of this abstraction hierarchy. here, we are interested in a specific kind of abstraction, namely the zero-shot generalization to concepts at novel levels of the concept hierarchy, or, to concepts at novel levels of abstraction (following the terminology of seminal research from cognitive psychology by @xcite . we will not only look at the generalization performance of the trained models, but also at the linguistic strategies the agents employ. specifically, we investigate the properties of the emergent protocol and the use of novel vs. established messages during abstraction. while previous work in emergent communication has highlighted the role of compositionality for generalization (see e.g. @xcite @xcite , in our experiments we disentangle two directions of generalization and propose that they require different linguistic strategies. we find that agents use a compositional strategy only when generalizing to specific concepts, but not when generalizing to generic concepts. these results highlight that compositionality is not the only way to achieve generalization, which is in line with recent findings from @xcite and @xcite . ## results we trained the models on six symbolic datasets with varying numbers of attributes and values. in a dataset d(n, k), objects have n attributes which each can take k different values. for all metrics, we report means and standard deviations over five individual runs per dataset. ## discussion when they generalize to very generic concepts, the agents' strategy is mostly characterized by reusing already established messages that might have been ambiguous during training and are sufficiently informative during testing. we observe lower performance when testing zero-shot generalization to very generic concepts than zero-shot generalization to very specific concepts. one explanation for this is that agents in the "to specific" condition make use of compositionality as their main strategy for generalization as shown by the compositionality scores and the qualitative analysis of the messages. intuitively, combining learned symbols for attributes like "circle" or "blue" into a message "blue circle" is easier to achieve with a finite lexicon than abstracting to novel generic concepts. if a language does not encode specific concepts, novel meanings can always be generated by combining established meanings. however, if a language does not encode generic concepts, a compositional strategy is not an option. novel meanings, however, cannot be established in a zero-shot generalization, so the only chance the agents have is to recur to already established meanings. but previously learned words that encode specific meanings are only useful to a certain extent, making the "to generic" direction of zeroshot generalization a harder task for our trained agent models which is reflected in the accuracies. this idea is supported by evidence we obtained from an in-depth analysis of the emerging protocols. first, we have shown that nmi scores are high but not maximal in both conditions, suggesting a large number of one-to-one mappings in the emergent mapping between concepts and messages. we have observed that more ambiguity emerges when training the agents in the "to generic" condition. this ambiguity likely is what enables the agents to perform fairly well on the generalization task, where we observe that they mostly reuse messages from the training phase to refer to novel concepts. ambiguity and the use of messages that rely on context to resolve ambiguities that remain after interpreting a message can lead to an efficient strategy that agents rely on mainly in coarse context conditions (see @xcite . second, we have shown that agents employ different strategies for generalizing to the most specific than to the most generic concepts. specifically, agents come up with more novel messages that are produced by compositionally combining symbols that have been associated with a fixed concept attribute during training, when being tested on the most specific concepts. agents trained in the "to generic" condition, on the other hand, mostly reuse entire messages the meanings of which have been established during training. these agents thus make use of the ambiguity of concept-message mappings that has emerged during training, accepting that these messages may be overinformative when describing the most generic concepts. our findings are in line with results from @xcite who found that compositionality is a sufficient but not a necessary condition for generalization. a fruitful direction for research building on our results would be to further investigate the non-compositional strategy in abstraction to more generic concepts. this might be done by relating our results in the "to generic" condition to the phenomenon of overgeneralization in children acquiring language. overgeneralization, or overextension, happens when a child uses a familiar label, for example "boot" to refer to an unfamiliar object, like sandal (see e.g. @xcite @xcite . this is similar to what our agents do when they use familiar messages to refer to novel concepts in the "to generic" condition. future research could benefit from integrating both lines of research to develop new hypotheses on children's acquisition of concepts and overgeneralization as a pragmatic strategy for successful and efficient communication even if the correct label is not known (see e.g. @xcite . another direction for future research is to investigate the role of communicative pressures during the emergent communication in our setting. recent research in the field is dedicated to understanding better how efficient emergent communication systems emerge as a function of informativeness and utility, and highlights the role of communicative pressures for the emergence of an efficient solution that generalizes well (e.g. @xcite . in our study, we do not use any communicative pressures except for keeping the maximum message length quite small (corresponding to the number of attributes in a dataset). as suggested by one of our reviewers, it would be interesting to test the effect of communicative pressures, such as a cost on the message length or an informativeness pressure as in @xcite , in our setup. for example, in the "to generic" condition, where we observe that agents mostly reuse messages from training that might be overinformative during testing on the most generic concepts, a communicative pressure might lead to shorter and less overinformative messages. related to both research directions outlined above, another fruitful avenue will be to investi-gate specifically the pragmatic processes involved in selecting an efficient message for an unfamiliar referent and investigate whether, for example, reasoning about the listener's likely interpretation of a message helps speakers to identify a well-suited message, improving the agents' performance in the zero-shot test (see zarrieß and schlangen, 2019, for a related approach). in summary, we have shown that the successful linguistic strategy for generalization depends on whether agents have to generalize from generic (low information) concepts to specific (high information) concepts or vice versa. our results add to existing evidence that language in the form of labels is important for abstraction in humans. our findings go beyond this research indicating a role of compositional messages and novel vs. established labels depending on the abstraction process. this work has important ramifications for linguistic theories on the role of compositionality and ambiguity in efficient communication and generalization: while previous work has highlighted the role of compositionality in generalization, we do not find evidence that abstraction to more generic concepts benefits from a compositional strategy in the same way as generalization to more specific concepts. instead, generalization to more generic concepts, i.e. abstraction, benefits from reusing ambiguous messages from the emerging protocol. these results in the "to generic" condition are in line with two linguistic phenomena: first, the widening of meanings in diachronic language change where previously specific meanings are used for more generic concepts (see e.g. @xcite , and second the overgeneralization of familiar labels to unfamiliar objects of the same category in language acquisition (see e.g. @xcite .