Adversarial examples pose a significant challenge to deep neural networks across both image and text domains, with the intent to degrade model performance through carefully altered inputs. Adversarial texts, however, are distinct from adversarial images due to their requirement for semantic similarity and the discrete nature of the textual contents. This study delves into the concept of human suspiciousness, a quality distinct from the traditional focus on imperceptibility found in image-based adversarial examples, where adversarial changes are often desired to be indistinguishable to the human eye even when placed side by side with originals. Although this is generally not possible with text, textual adversarial content must still often remain undetected or non-suspicious to human readers. Even when the text’s purpose is to deceive NLP systems or bypass filters, the text is often expected to be natural to read. In this research, we expand the study of human suspiciousness by analyzing how individuals perceive adversarial texts. We gather and publish a novel dataset of Likert-scale human evaluations on the suspiciousness of adversarial sentences, crafted by four widely used adversarial attack methods and assess their correlation with the human ability to detect machine-generated alterations. Additionally, we develop a regression-based model to predict levels of suspiciousness and establish a baseline for future research in reducing the suspiciousness in adversarial text generation. We also demonstrate how the regressor-generated suspicious scores can be incorporated into adversarial generation methods to produce texts that are less likely to be perceived as computer-generated.

Argumentation is a fundamental human activity that involves reasoning and persuasion, which also serves as the basis for the development of AI systems capable of complex reasoning. In NLP, to better understand human argumentation, argument structure analysis aims to identify argument components, such as claims and premises, and their relations from free text. It encompasses a variety of divergent tasks, such as end-to-end argument mining, argument pair extraction, and argument quadruplet extraction. Existing methods are usually tailored to only one specific argument structure analysis task, overlooking the inherent connections among different tasks. We observe that the fundamental goal of these tasks is similar: identifying argument components and their interrelations. Motivated by this, we present a unified generative framework for argument structure analysis (UniASA). It can uniformly address multiple argument structure analysis tasks in a sequence-to-sequence manner. Further, we enhance UniASA with a multi-view learning strategy based on subtask decomposition. We conduct experiments on seven datasets across three tasks. The results indicate that UniASA can address these tasks uniformly and achieve performance that is either superior to or comparable with the previous state-of-the-art methods. Also, we show that UniASA can be effectively integrated with large language models, such as Llama, through fine-tuning or in-context learning.

Large language models segment many words into multiple tokens, and there is mixed evidence as to whether tokenization affects how state-of-the-art models represent meanings. Chinese characters present an opportunity to investigate this issue: They contain semantic radicals, which often convey useful information; characters with the same semantic radical tend to begin with the same one or two bytes (when using UTF-8 encodings); and tokens are common strings of bytes, so characters with the same radical often begin with the same token. This study asked GPT-4, GPT-4o, and Llama 3 whether characters contain the same semantic radical, elicited semantic similarity ratings, and conducted odd-one-out tasks (i.e., which character is not like the others). In all cases, misalignment between tokens and radicals systematically corrupted representations of Chinese characters. In experiments comparing characters represented by single tokens to multi-token characters, the models were less accurate for single-token characters, which suggests that segmenting words into fewer, longer tokens obscures valuable information in word form and will not resolve the problems introduced by tokenization. In experiments with 12 European languages, misalignment between tokens and suffixes systematically corrupted categorization of words by all three models, which suggests that the tendency to treat malformed tokens like linguistic units is pervasive.

In Natural Language Processing (NLP), predicting linguistic structures, such as parsing and chunking, has mostly relied on manual annotations of syntactic structures. This article introduces an unsupervised approach to chunking, a syntactic task that involves grouping words in a non-hierarchical manner. We present a Hierarchical Recurrent Neural Network (HRNN) designed to model word-to-chunk and chunk-to-sentence compositions. Our approach involves a two-stage training process: pretraining with an unsupervised parser and finetuning on downstream NLP tasks. Experiments on multiple datasets reveal a notable improvement of unsupervised chunking performance in both pretraining and finetuning stages. Interestingly, we observe that the emergence of the chunking structure is transient during the neural model’s downstream-task training. This study contributes to the advancement of unsupervised syntactic structure discovery and opens avenues for further research in linguistic theory.1

We investigate two essential challenges in the context of hierarchical topic modeling (HTM)—(i) the impact of data representation and (ii) topic evaluation. The data representation directly influences the performance of the topic generation, and the impact of new representations such as contextual embeddings in this task has been under-investigated. Topic evaluation, responsible for driving the advances in the field, assesses the overall quality of the topic generation process. HTM studies exploit the exact topic modeling (TM) evaluation metrics as traditional TM to measure the quality of topics. One significant result of our work is demonstrating that the HTM’s hierarchical nature demands novel ways of evaluating the quality of topics. As our main contribution, we propose two new topic quality metrics to assess the topical quality of the hierarchical structures. Uniqueness considers topic topological consistency, while the Semantic Hierarchical Structure (SHS) captures the semantic relatedness of the hierarchies. We also present an additional advance to the state-of-the-art by proposing the c-CluHTM. To the best of our knowledge, c-CluHTM is the first method that exploits contextual embeddings into NMF in HTM tasks. c-CluHTM enhances the topics’ semantics while preserving the hierarchical structure. We perform an experimental evaluation, and our results demonstrate the superiority of our proposal with gains between 12% and 21%, regarding NPMI and Coherence over the best baselines. Regarding the newly proposed metrics, our results reveal that Uniqueness and SHS can capture relevant information about the structure of the hierarchical topics that traditional metrics cannot.

This position paper’s primary goal is to provoke thoughtful discussion about the relationship between bias and fundamental properties of large language models (LLMs). I do this by seeking to convince the reader that harmful biases are an inevitable consequence arising from the design of any large language model as LLMs are currently formulated. To the extent that this is true, it suggests that the problem of harmful bias cannot be properly addressed without a serious reconsideration of AI driven by LLMs, going back to the foundational assumptions underlying their design.

Large-scale deployment of large language models (LLMs) in various applications, such as chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ensure inclusivity. Culture has been widely studied in psychology and anthropology, and there has been a recent surge in research on making LLMs more culturally inclusive, going beyond multilinguality and building on findings from psychology and anthropology. In this article, we survey efforts towards incorporating cultural awareness into text-based and multimodal LLMs. We start by defining cultural awareness in LLMs, taking definitions of culture from the anthropology and psychology literature as a point of departure. We then examine methodologies adopted for creating cross-cultural datasets, strategies for cultural inclusion in downstream tasks, and methodologies that have been used for benchmarking cultural awareness in LLMs. Further, we discuss the ethical implications of cultural alignment, the role of human–computer interaction in driving cultural inclusion in LLMs, and the role of cultural alignment in driving social science research. We finally provide pointers to future research based on our findings about gaps in the literature.1

Large Language Models have become capable of generating highly fluent text in certain languages, without modules specially designed to capture grammar or semantic coherence. What does this mean for the future of linguistic expertise in NLP? We highlight several aspects in which NLP (still) relies on linguistics, or where linguistic thinking can illuminate new directions. We argue our case around the acronym RELIES, which encapsulates six major facets where linguistics contributes to NLP: Resources, Evaluation, Low-resource settings, Interpretability, Explanation, and the Study of language. This list is not exhaustive, nor is linguistics the main point of reference for every effort under these themes; but at a macro level, these facets highlight the enduring importance of studying machine systems vis-à-vis systems of human language.