In the fashion domain, there exists a variety of vision-and-language (V+L) tasks, including cross-modal retrieval, text-guided image retrieval, multi-modal classification, and image captioning. They differ drastically in each individual input/output format and dataset size. It has been common to design a task-specific model and fine-tune it independently from a pre-trained V+L model (e.g., CLIP). This results in parameter inefficiency and inability to exploit inter-task relatedness. To address such issues, we propose a novel FAshion-focused Multi-task Efficient learning method for Vision-and-Language tasks (FAME-ViL) in this work. Compared with existing approaches, FAME-ViL applies a single model for multiple heterogeneous fashion tasks, therefore being much more parameter-efficient. It is enabled by two novel components: (1) a task-versatile architecture with cross-attention adapters and task-specific adapters integrated into a unified V+L model, and (2) a stable and effective multi-task training strategy that supports learning from heterogeneous data and prevents negative transfer. Extensive experiments on four fashion tasks show that our FAME-ViL can save 61.5% of parameters over alternatives, while significantly outperforming the conventional independently trained single-task models. Code is available at https://github.com/BrandonHanx/FAME-ViL.
Feature attribution methods identify which features of an input most influence a model's output. Most widely-used feature attribution methods (such as SHAP, LIME, and Grad-CAM) are "class-dependent" methods in that they generate a feature attribution vector as a function of class. In this work, we demonstrate that class-dependent methods can "leak" information about the selected class, making that class appear more likely than it is. Thus, an end user runs the risk of drawing false conclusions when interpreting an explanation generated by a class-dependent method. In contrast, we introduce "distribution-aware" methods, which favor explanations that keep the label's distribution close to its distribution given all features of the input. We introduce SHAP-KL and FastSHAP-KL, two baseline distribution-aware methods that compute Shapley values. Finally, we perform a comprehensive evaluation of seven class-dependent and three distribution-aware methods on three clinical datasets of different high-dimensional data types: images, biosignals, and text.
Leveraging contextual knowledge has become standard practice in automated claim verification, yet the impact of temporal reasoning has been largely overlooked. Our study demonstrates that time positively influences the claim verification process of evidence-based fact-checking. The temporal aspects and relations between claims and evidence are first established through grounding on shared timelines, which are constructed using publication dates and time expressions extracted from their text. Temporal information is then provided to RNN-based and Transformer-based classifiers before or after claim and evidence encoding. Our time-aware fact-checking models surpass base models by up to 9% Micro F1 (64.17%) and 15% Macro F1 (47.43%) on the MultiFC dataset. They also outperform prior methods that explicitly model temporal relations between evidence. Our findings show that the presence of temporal information and the manner in which timelines are constructed greatly influence how fact-checking models determine the relevance and supporting or refuting character of evidence documents.
Recent years have seen impressive progress in AI-assisted writing, yet the developments in AI-assisted reading are lacking. We propose inline commentary as a natural vehicle for AI-based reading assistance, and present CARE: the first open integrated platform for the study of inline commentary and reading. CARE facilitates data collection for inline commentaries in a commonplace collaborative reading environment, and provides a framework for enhancing reading with NLP-based assistance, such as text classification, generation or question answering. The extensible behavioral logging allows unique insights into the reading and commenting behavior, and flexible configuration makes the platform easy to deploy in new scenarios. To evaluate CARE in action, we apply the platform in a user study dedicated to scholarly peer review. CARE facilitates the data collection and study of inline commentary in NLP, extrinsic evaluation of NLP assistance, and application prototyping. We invite the community to explore and build upon the open source implementation of CARE.
Text classification is a natural language processing (NLP) task relevant to many commercial applications, like e-commerce and customer service. Naturally, classifying such excerpts accurately often represents a challenge, due to intrinsic language aspects, like irony and nuance. To accomplish this task, one must provide a robust numerical representation for documents, a process known as embedding. Embedding represents a key NLP field nowadays, having faced a significant advance in the last decade, especially after the introduction of the word-to-vector concept and the popularization of Deep Learning models for solving NLP tasks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based Language Models (TLMs). Despite the impressive achievements in this field, the literature coverage regarding generating embeddings for Brazilian Portuguese texts is scarce, especially when considering commercial user reviews. Therefore, this work aims to provide a comprehensive experimental study of embedding approaches targeting a binary sentiment classification of user reviews in Brazilian Portuguese. This study includes from classical (Bag-of-Words) to state-of-the-art (Transformer-based) NLP models. The methods are evaluated with five open-source databases with pre-defined data partitions made available in an open digital repository to encourage reproducibility. The Fine-tuned TLMs achieved the best results for all cases, being followed by the Feature-based TLM, LSTM, and CNN, with alternate ranks, depending on the database under analysis.
Transportation equity is an interdisciplinary agenda that requires both transportation and social inputs. Traditionally, transportation equity information are sources from public libraries, conferences, televisions, social media, among other. Artificial intelligence (AI) tools including advanced language models such as ChatGPT are becoming favorite information sources. However, their credibility has not been well explored. This study explored the content and usefulness of ChatGPT-generated information related to transportation equity. It utilized 152 papers retrieved through the Web of Science (WoS) repository. The prompt was crafted for ChatGPT to provide an abstract given the title of the paper. The ChatGPT-based abstracts were then compared to human-written abstracts using statistical tools and unsupervised text mining. The results indicate that a weak similarity between ChatGPT and human-written abstracts. On average, the human-written abstracts and ChatGPT generated abstracts were about 58% similar, with a maximum and minimum of 97% and 1.4%, respectively. The keywords from the abstracts of papers with over the mean similarity score were more likely to be similar whereas those from below the average score were less likely to be similar. Themes with high similarity scores include access, public transit, and policy, among others. Further, clear differences in the key pattern of clusters for high and low similarity score abstracts was observed. Contrarily, the findings from collocated keywords were inconclusive. The study findings suggest that ChatGPT has the potential to be a source of transportation equity information. However, currently, a great amount of attention is needed before a user can utilize materials from ChatGPT
Human models play a crucial role in human-robot interaction (HRI), enabling robots to consider the impact of their actions on people and plan their behavior accordingly. However, crafting good human models is challenging; capturing context-dependent human behavior requires significant prior knowledge and/or large amounts of interaction data, both of which are difficult to obtain. In this work, we explore the potential of large-language models (LLMs) -- which have consumed vast amounts of human-generated text data -- to act as zero-shot human models for HRI. Our experiments on three social datasets yield promising results; the LLMs are able to achieve performance comparable to purpose-built models. That said, we also discuss current limitations, such as sensitivity to prompts and spatial/numerical reasoning mishaps. Based on our findings, we demonstrate how LLM-based human models can be integrated into a social robot's planning process and applied in HRI scenarios. Specifically, we present one case study on a simulated trust-based table-clearing task and replicate past results that relied on custom models. Next, we conduct a new robot utensil-passing experiment (n = 65) where preliminary results show that planning with a LLM-based human model can achieve gains over a basic myopic plan. In summary, our results show that LLMs offer a promising (but incomplete) approach to human modeling for HRI.
Deep neural networks are vulnerable to adversarial attacks. We consider adversarial defense in the case of zero-shot image classification setting, which has rarely been explored because both adversarial defense and zero-shot learning are challenging. We propose LAAT, a novel Language-driven, Anchor-based Adversarial Training strategy, to improve the adversarial robustness in a zero-shot setting. LAAT uses a text encoder to obtain fixed anchors (normalized feature embeddings) of each category, then uses these anchors to perform adversarial training. The text encoder has the property that semantically similar categories can be mapped to neighboring anchors in the feature space. By leveraging this property, LAAT can make the image model adversarially robust on novel categories without any extra examples. Experimental results show that our method achieves impressive zero-shot adversarial performance, even surpassing the previous state-of-the-art adversarially robust one-shot methods in most attacking settings. When models are trained with LAAT on large datasets like ImageNet-1K, they can have substantial zero-shot adversarial robustness across several downstream datasets.
Annotating large collections of textual data can be time consuming and expensive. That is why the ability to train models with limited annotation budgets is of great importance. In this context, it has been shown that under tight annotation budgets the choice of data representation is key. The goal of this paper is to better understand why this is so. With this goal in mind, we propose a metric that measures the extent to which a given representation is structurally aligned with a task. We conduct experiments on several text classification datasets testing a variety of models and representations. Using our proposed metric we show that an efficient representation for a task (i.e. one that enables learning from few samples) is a representation that induces a good alignment between latent input structure and class structure.
We present the call for papers for the BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. This shared task is intended for participants with an interest in small scale language modeling, human language acquisition, low-resource NLP, and cognitive modeling. In partnership with CoNLL and CMCL, we provide a platform for approaches to pretraining with a limited-size corpus sourced from data inspired by the input to children. The task has three tracks, two of which restrict the training data to pre-released datasets of 10M and 100M words and are dedicated to explorations of approaches such as architectural variations, self-supervised objectives, or curriculum learning. The final track only restricts the amount of text used, allowing innovation in the choice of the data, its domain, and even its modality (i.e., data from sources other than text is welcome). We will release a shared evaluation pipeline which scores models on a variety of benchmarks and tasks, including targeted syntactic evaluations and natural language understanding.