In this paper, we design a signalling game-based emergent communication environment to generate state-of-the-art emergent languages in terms of similarity to human language. This is done with hyperparameter optimization, using XferBench as the objective function. XferBench quantifies the statistical similarity of emergent language to human language by measuring its suitability for deep transfer learning to human language. Additionally, we demonstrate the predictive power of entropy on the transfer learning performance of emergent language as well as corroborate previous results on the entropy-minimization properties of emergent communication systems. Finally, we report generalizations regarding what hyperparameters produce more realistic emergent languages, that is, ones which transfer better to human language.
We show that it is possible to craft transformations that, applied to compositional grammars, result in grammars that neural networks can learn easily, but humans do not. This could explain the disconnect between current metrics of compositionality, that are arguably human-centric, and the ability of neural networks to generalize to unseen examples. We propose to use the transformations as a benchmark, ICY, which could be used to measure aspects of the compositional inductive bias of networks, and to search for networks with similar compositional inductive biases to humans. As an example of th…
Learning and communication pressures in neural networks: Lessons from emergent communication
Centre for Machine Learning（机器学习中心）；University of Southern Denmark (SDU)（南丹麦大学）；Max Planck Institute for Psycholinguistics（马克斯·普朗克心理语言学研究所）；University of Glasgow（格拉斯哥大学）
Shaping Shared Languages: Human and Large Language Models' Inductive Biases in Emergent Communication
Leiden Institute of Advanced Computer Science, Leiden University, Netherlands（莱顿高级计算机科学研究所，莱顿大学，荷兰）；School of Computing, University of Kent, United Kingdom（计算学院，肯特大学，英国）；Cognitive Psychology Unit, Institute of Psychology, Leiden, the Netherlands（认知心理学单元，心理学研究所，莱顿，荷兰）
Max Planck Institute for Psycholinguistics（马克斯·普朗克心理语言学研究所）；Tel Aviv University（特拉维夫大学）；University of Glasgow（格拉斯哥大学）
Emergent communication protocols among humans and artificial neural network agents do not yet share the same properties and show some critical mismatches in results. We describe three important phenomena with respect to the emergence and benefits of compositionality: ease-of-learning, generalization, and group size effects (i.e., larger groups create more systematic languages). The latter two are not fully replicated with neural agents, which hinders the use of neural emergent communication for language evolution research. We argue that one possible reason for these mismatches is that key cogn…
Recent research studies communication emergence in communities of deep network agents assigned a joint task, hoping to gain insights on human language evolution. We propose here a new task capturing crucial aspects of the human environment, such as natural object affordances, and of human conversation, such as full symmetry among the participants. By conducting a thorough pragmatic and semantic analysis of the emergent protocol, we show that the agents solve the shared task through genuine bilateral, referential communication. However, the agents develop multiple idiolects, which makes us conc…
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Cambridge（剑桥大学）；ETH Zürich（苏黎世联邦理工学院）
Computational models of human language often involve combinatorial problems. For instance, a probabilistic parser may marginalize over exponentially many trees to make predictions. Algorithms for such problems often employ dynamic programming and are not always unique. Finding one with optimal asymptotic runtime can be unintuitive, time-consuming, and error-prone. Our work aims to automate this laborious process. Given an initial correct declarative program, we search for a sequence of semantics-preserving transformations to improve its running time as much as possible. To this end, we describ…
Charles University（查理大学）；Faculty of Mathematics and Physics（数学与物理学院）
Compositionality has traditionally been understood as a major factor in productivity of language and, more broadly, human cognition. Yet, recently, some research started to question its status, showing that artificial neural networks are good at generalization even without noticeable compositional behavior. We argue that some of these conclusions are too strong and/or incomplete. In the context of a two-agent communication game, we show that compositionality indeed seems essential for successful generalization when the evaluation is done on a proper dataset.
University of Edinburgh（爱丁堡大学）；School of Informatics（信息学院）
With the rapid development of deep learning, most of current state-of-the-art techniques in natural langauge processing are based on deep learning models trained with argescaled static textual corpora. However, we human beings learn and understand in a different way. Thus, grounded language learning argues that models need to learn and understand language by the experience and perceptions obtained by interacting with enviroments, like how humans do. With the help of deep reinforcement learning techniques, there are already lots of works focusing on facilitating the emergence of communication p…
DeepMind；Facebook AI Research（Facebook人工智能研究院）；ICREA（加泰罗尼亚研究与创新高级研究院）；UPF（庞培法布拉大学）
The ability to cooperate through language is a defining feature of humans. As the perceptual, motory and planning capabilities of deep artificial networks increase, researchers are studying whether they also can develop a shared language to interact. From a scientific perspective, understanding the conditions under which language evolves in communities of deep agents and its emergent features can shed light on human language evolution. From an applied perspective, endowing deep networks with the ability to solve problems interactively by communicating with each other and with us should make th…
Emergent communication research often focuses on optimizing task-specific utility as a driver for communication. However, human languages appear to evolve under pressure to efficiently compress meanings into communication signals by optimizing the Information Bottleneck tradeoff between informativeness and complexity. In this work, we study how trading off these three factors -- utility, informativeness, and complexity -- shapes emergent communication, including compared to human communication. To this end, we propose Vector-Quantized Variational Information Bottleneck (VQ-VIB), a method for t…
With the starting point that implicit human biases are reflected in the statistical regularities of language, it is possible to measure biases in English static word embeddings. State-of-the-art neural language models generate dynamic word embeddings dependent on the context in which the word appears. Current methods measure pre-defined social and intersectional biases that appear in particular contexts defined by sentence templates. Dispensing with templates, we introduce the Contextualized Embedding Association Test (CEAT), that can summarize the magnitude of overall bias in neural language …
University of Southern California（南加州大学）；Microsoft（微软）
In MT evaluation, pairwise comparisons are conducted to identify the better system. In conducting the comparison, the experimenter must allocate a budget to collect Direct Assessment (DA) judgments. We provide a cost effective way to spend the budget, but show that typical budget sizes often do not allow for solid comparison. Taking the perspective that the basis of solid comparison is in achieving statistical significance, we study the power (rate of achieving significance) on a large collection of pairwise DA comparisons. Due to the nature of statistical estimation, power is low for differen…
LLMs Deceive Unintentionally: Emergent Misalignment in Dishonesty from Misaligned Samples to Biased Human-AI Interactions
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fudan University（复旦大学）；University of Science and Technology of China（中国科学技术大学）；Shanghai Jiao Tong University（上海交通大学）
New York University（纽约大学）；Facebook AI Research（脸书人工智能研究院）；CIFAR Azrieli Global Scholar（加拿大高级研究院阿兹列利全球学者）
While most machine translation systems to date are trained on large parallel corpora, humans learn language in a different way: by being grounded in an environment and interacting with other humans. In this work, we propose a communication game where two agents, native speakers of their own respective languages, jointly learn to solve a visual referential task. We find that the ability to understand and translate a foreign language emerges as a means to achieve shared goals. The emergent translation is interactive and multimodal, and crucially does not require parallel corpora, but only monoli…
Facebook AI Research（Facebook人工智能研究院）；ENS - EHESS - PSL Research University - CNRS - INRIA（巴黎高等师范学院-社会科学高等研究院-巴黎文理研究大学-法国国家科学研究中心-法国国家信息与自动化研究所）；ICREA（加泰罗尼亚研究与高等教育学院）
Despite renewed interest in emergent language simulations with neural networks, little is known about the basic properties of the induced code, and how they compare to human language. One fundamental characteristic of the latter, known as Zipf's Law of Abbreviation (ZLA), is that more frequent words are efficiently associated to shorter strings. We study whether the same pattern emerges when two neural networks, a "speaker" and a "listener", are trained to play a signaling game. Surprisingly, we find that networks develop an \emph{anti-efficient} encoding scheme, in which the most frequent inp…
Revealing emergent human-like conceptual representations from language prediction
College of Computer Science and Artificial Intelligence（计算机科学与人工智能学院）；Fudan University（复旦大学）；Institute of Modern Languages and Linguistics（现代语言与语言学研究所）；Shanghai Key Laboratory of Intelligent Information Processing（上海智能信息处理重点实验室）；Research Institute of Intelligent Complex Systems（智能复杂系统研究所）；Institute of Trustworthy Embodied Artificial Intelligence（可信具身人工智能研究所）；State Key Laboratory of Genetics and Development of Complex Phenotypes（复杂表型遗传与发育国家重点实验室）
UCLA（加州大学洛杉矶分校）；Beijing Institute for General Artificial Intelligence (BIGAI)（北京通用人工智能研究院）；Peking University（北京大学）
Humans communicate with graphical sketches apart from symbolic languages. Primarily focusing on the latter, recent studies of emergent communication overlook the sketches; they do not account for the evolution process through which symbolic sign systems emerge in the trade-off between iconicity and symbolicity. In this work, we take the very first step to model and simulate this process via two neural agents playing a visual communication game; the sender communicates with the receiver by sketching on a canvas. We devise a novel reinforcement learning method such that agents are evolved jointl…
Artificial Phantasia: Emergent Mental Imagery in Large Language Models
Khoury College of Computer Sciences, Northeastern University（东北大学克劳利计算机科学学院）；Department of Psychology, Northeastern University（东北大学心理学系）；Department of Philosophy, Northeastern University（东北大学哲学系）
Human language has been described as a system that makes \textit{use of finite means to express an unlimited array of thoughts}. Of particular interest is the aspect of compositionality, whereby, the meaning of a compound language expression can be deduced from the meaning of its constituent parts. If artificial agents can develop compositional communication protocols akin to human language, they can be made to seamlessly generalize to unseen combinations. Studies have recognized the role of curiosity in enabling linguistic development in children. In this paper, we seek to use this intrinsic …
AgentGroupChat: An Interactive Group Chat Simulacra For Better Eliciting Emergent Behavior
Fudan University（复旦大学）；Rhine AI（莱茵人工智能公司）；Shanghai University of Engineering and Science（上海工程技术大学）；Xiaohongshu Inc.（小红书公司）；Fudan-Aishu Cognitive Intelligence Joint Research Center（复旦-爱数认知智能联合研究中心）
Facebook AI（Facebook人工智能）；MILA（蒙特利尔学习算法研究所）；Facebook AI Research（Facebook人工智能研究院）
A promising approach for teaching artificial agents to use natural language involves using human-in-the-loop training. However, recent work suggests that current machine learning methods are too data inefficient to be trained in this way from scratch. In this paper, we investigate the relationship between two categories of learning signals with the ultimate goal of improving sample efficiency: imitating human language data via supervised learning, and maximizing reward in a simulated multi-agent environment via self-play (as done in emergent communication), and introduce the term supervised se…
Emergent communication in artificial agents has been studied to understand language evolution, as well as to develop artificial systems that learn to communicate with humans. We show that agents performing a cooperative navigation task in various gridworld environments learn an interpretable communication protocol that enables them to efficiently, and in many cases, optimally, solve the task. An analysis of the agents' policies reveals that emergent signals spatially cluster the state space, with signals referring to specific locations and spatial directions such as "left", "up", or "upper lef…
EmCom-Diffusion: Probing Visual Reflection in Emergent Languages via Image Generation
Graduate School of Informatics, Kyoto University, Kyoto, Japan（京都大学信息学研究科）；Research Organization of Science and Technology, Ritsumeikan University, Shiga, Japan（立命馆大学科学技术研究机构）
To build agents that can collaborate effectively with others, recent research has trained artificial agents to communicate with each other in Lewis-style referential games. However, this often leads to successful but uninterpretable communication. We argue that this is due to the game objective: communicating about a single object in a shared visual context is prone to overfitting and does not encourage language useful beyond concrete reference. In contrast, human language conveys a rich variety of abstract ideas. To promote such skills, we propose games that require communicating generalizati…
Massachusetts Institute of Technology（麻省理工学院）；University of Pittsburgh（匹兹堡大学）；Carnegie Mellon University（卡内基梅隆大学）
Neural agents trained in reinforcement learning settings can learn to communicate among themselves via discrete tokens, accomplishing as a team what agents would be unable to do alone. However, the current standard of using one-hot vectors as discrete communication tokens prevents agents from acquiring more desirable aspects of communication such as zero-shot understanding. Inspired by word embedding techniques from natural language processing, we propose neural agent architectures that enables them to communicate via discrete tokens derived from a learned, continuous space. We show in a decis…
University of Amsterdam（阿姆斯特丹大学）；Institute of Cognitive Science（认知科学研究所）；University of Osnabruek（奥斯纳布吕克大学）；Institute for Logic, Language and Computation（逻辑、语言与计算研究所）
In this paper, we consider the syntactic properties of languages emerged in referential games, using unsupervised grammar induction (UGI) techniques originally designed to analyse natural language. We show that the considered UGI techniques are appropriate to analyse emergent languages and we then study if the languages that emerge in a typical referential game setup exhibit syntactic structure, and to what extent this depends on the maximum message length and number of symbols that the agents are allowed to use. Our experiments demonstrate that a certain message length and vocabulary size are…
University of Amsterdam（阿姆斯特丹大学）；Facebook A.I. Research（脸书人工智能研究院）；Universitat Pompeu Fabra（庞培法布拉大学）
To cooperate with humans effectively, virtual agents need to be able to understand and execute language instructions. A typical setup to achieve this is with a scripted teacher which guides a virtual agent using language instructions. However, such setup has clear limitations in scalability and, more importantly, it is not interactive. Here, we introduce an autonomous agent that uses discrete communication to interactively guide other agents to navigate and act on a simulated environment. The developed communication protocol is trainable, emergent and requires no additional supervision. The em…
Facebook AI Research（脸书人工智能研究院）；Cognitive Machine Learning (ENS - EHESS - PSL - CNRS - INRIA)（认知机器学习实验室（巴黎高等师范学院-社会科学高等学院-巴黎文理研究大学-法国国家科学研究中心-法国国家信息与自动化研究所））；Catalan Institute for Research and Advanced Studies（加泰罗尼亚研究与高级研究所）
There is growing interest in studying the languages that emerge when neural agents are jointly trained to solve tasks requiring communication through a discrete channel. We investigate here the information-theoretic complexity of such languages, focusing on the basic two-agent, one-exchange setup. We find that, under common training procedures, the emergent languages are subject to an entropy minimization pressure that has also been detected in human language, whereby the mutual information between the communicating agent's inputs and the messages is minimized, within the range afforded by the…
Facebook A.I. Research（Facebook人工智能研究院）；ENS - EHESS - PSL Research University - CNRS - INRIA（巴黎高等师范学院-社会科学高等研究院-巴黎文理研究大学-法国国家科学研究中心-法国国家信息与自动化研究所）；ICREA（加泰罗尼亚研究与高等教育学院）
Sequence-processing neural networks led to remarkable progress on many NLP tasks. As a consequence, there has been increasing interest in understanding to what extent they process language as humans do. We aim here to uncover which biases such models display with respect to "natural" word-order constraints. We train models to communicate about paths in a simple gridworld, using miniature languages that reflect or violate various natural language trends, such as the tendency to avoid redundancy or to minimize long-distance dependencies. We study how the controlled characteristics of our miniatu…
University of Cambridge（剑桥大学）；MILA（蒙特利尔学习算法研究所）；McGill University（麦吉尔大学）；Polytechnique Montréal（蒙特利尔综合理工学院）；ServiceNow（ServiceNow公司）
In order to communicate, humans flatten a complex representation of ideas and their attributes into a single word or a sentence. We investigate the impact of representation learning in artificial agents by developing graph referential games. We empirically show that agents parametrized by graph neural networks develop a more compositional language compared to bag-of-words and sequence models, which allows them to systematically generalize to new combinations of familiar features.
Cognitive Machine Learning (ENS - EHESS - PSL Research University - CNRS - INRIA)（认知机器学习（巴黎高等师范学院 - 社会科学高等学院 - 巴黎文理研究大学 - 法国国家科学研究中心 - 法国国家信息与自动化研究所））；Facebook AI Research（Facebook人工智能研究院）；ICREA（ICREA（加泰罗尼亚研究与高等教育机构））
Natural language allows us to refer to novel composite concepts by combining expressions denoting their parts according to systematic rules, a property known as \emph{compositionality}. In this paper, we study whether the language emerging in deep multi-agent simulations possesses a similar ability to refer to novel primitive combinations, and whether it accomplishes this feat by strategies akin to human-language compositionality. Equipped with new ways to measure compositionality in emergent languages inspired by disentanglement in representation learning, we establish three main results. Fir…
Beyond Instrumental and Substitutive Paradigms: Introducing Machine Culture as an Emergent Phenomenon in Large Language Models
Institute of Neuroscience, Chinese Academy of Sciences（中国科学院神经科学研究所）；University of Cambridge（剑桥大学）；Chinese University of Hong Kong（香港中文大学）；Institute of Public Opinion Survey, Hong Kong SAR（香港特别行政区民意调查研究所）；Tsinghua University（清华大学）
