Shortcuts Arising from Contrast: Effective and Covert Clean-Label Attacks in Prompt-Based Learning

Prompt-based learning paradigm has demonstrated remarkable efficacy in enhancing the adaptability of pretrained language models (PLMs), particularly in few-shot scenarios. However, this learning paradigm has been shown to be vulnerable to backdoor attacks. The current clean-label attack, employing a specific prompt as a trigger, can achieve success without the need for external triggers and ensure correct labeling of poisoned samples, which is more stealthy compared to the poisoned-label attack, but on the other hand, it faces significant issues with false activations and poses greater challenges, necessitating a higher rate of poisoning. Using conventional negative data augmentation methods, we discovered that it is challenging to trade off between effectiveness and stealthiness in a clean-label setting. In addressing this issue, we are inspired by the notion that a backdoor acts as a shortcut and posit that this shortcut stems from the contrast between the trigger and the data utilized for poisoning. In this study, we propose a method named Contrastive Shortcut Injection (CSI), by leveraging activation values, integrates trigger design and data selection strategies to craft stronger shortcut features. With extensive experiments on full-shot and few-shot text classification tasks, we empirically validate CSI's high effectiveness and high stealthiness at low poisoning rates. Notably, we found that the two approaches play leading roles in full-shot and few-shot settings, respectively.

'One size doesn't fit all': Learning how many Examples to use for In-Context Learning for Improved Text Classification

Predictive models in natural language processing (NLP) have evolved from training models from scratch to fine-tuning pre-trained models with labelled data. An extreme form of this fine-tuning involves in-context learning (ICL), where the output of a pre-trained generative model (frozen decoder parameters) is controlled only with variations in the input strings (called instructions or prompts). An important component of ICL is the use of a small number of labelled data instances as examples in the prompt. While existing work uses a static number of examples during inference for each data instance, in this paper we propose a novel methodology of dynamically adapting the number of examples as per the data. This is analogous to the use of a variable-sized neighborhood in k-nearest neighbors (k-NN) classifier. In our proposed workflow of adaptive ICL (AICL), the number of demonstrations to employ during the inference on a particular data instance is predicted by the Softmax posteriors of a classifier. The parameters of this classifier are fitted on the optimal number of examples in ICL required to correctly infer the label of each instance in the training set with the hypothesis that a test instance that is similar to a training instance should use the same (or a closely matching) number of few-shot examples. Our experiments show that our AICL method results in improvement in text classification task on several standard datasets.

Text classification of column headers with a controlled vocabulary: leveraging LLMs for metadata enrichment

Traditional dataset retrieval systems index on metadata information rather than on the data values. Thus relying primarily on manual annotations and high-quality metadata, processes known to be labour-intensive and challenging to automate. We propose a method to support metadata enrichment with topic annotations of column headers using three Large Language Models (LLMs): ChatGPT-3.5, GoogleBard and GoogleGemini. We investigate the LLMs ability to classify column headers based on domain-specific topics from a controlled vocabulary. We evaluate our approach by assessing the internal consistency of the LLMs, the inter-machine alignment, and the human-machine agreement for the topic classification task. Additionally, we investigate the impact of contextual information (i.e. dataset description) on the classification outcomes. Our results suggest that ChatGPT and GoogleGemini outperform GoogleBard for internal consistency as well as LLM-human-alignment. Interestingly, we found that context had no impact on the LLMs performances. This work proposes a novel approach that leverages LLMs for text classification using a controlled topic vocabulary, which has the potential to facilitate automated metadata enrichment, thereby enhancing dataset retrieval and the Findability, Accessibility, Interoperability and Reusability (FAIR) of research data on the Web.

Comparing effectiveness of regularization methods on text classification: Simple and complex model in data shortage situation

Text classification is the task of assigning a document to a predefined class. However, it is expensive to acquire enough labeled documents or to label them. In this paper, we study the regularization methods' effects on various classification models when only a few labeled data are available. We compare a simple word embedding-based model, which is simple but effective, with complex models (CNN and BiLSTM). In supervised learning, adversarial training can further regularize the model. When an unlabeled dataset is available, we can regularize the model using semi-supervised learning methods such as the Pi model and virtual adversarial training. We evaluate the regularization effects on four text classification datasets (AG news, DBpedia, Yahoo! Answers, Yelp Polarity), using only 0.1% to 0.5% of the original labeled training documents. The simple model performs relatively well in fully supervised learning, but with the help of adversarial training and semi-supervised learning, both simple and complex models can be regularized, showing better results for complex models. Although the simple model is robust to overfitting, a complex model with well-designed prior beliefs can be also robust to overfitting.

Comprehensive Implementation of TextCNN for Enhanced Collaboration between Natural Language Processing and System Recommendation

Natural Language Processing (NLP) is an important branch of artificial intelligence that studies how to enable computers to understand, process, and generate human language. Text classification is a fundamental task in NLP, which aims to classify text into different predefined categories. Text classification is the most basic and classic task in natural language processing, and most of the tasks in natural language processing can be regarded as classification tasks. In recent years, deep learning has achieved great success in many research fields, and today, it has also become a standard technology in the field of NLP, which is widely integrated into text classification tasks. Unlike numbers and images, text processing emphasizes fine-grained processing ability. Traditional text classification methods generally require preprocessing the input model's text data. Additionally, they also need to obtain good sample features through manual annotation and then use classical machine learning algorithms for classification. Therefore, this paper analyzes the application status of deep learning in the three core tasks of NLP (including text representation, word order modeling, and knowledge representation). This content explores the improvement and synergy achieved through natural language processing in the context of text classification, while also taking into account the challenges posed by adversarial techniques in text generation, text classification, and semantic parsing. An empirical study on text classification tasks demonstrates the effectiveness of interactive integration training, particularly in conjunction with TextCNN, highlighting the significance of these advancements in text classification augmentation and enhancement.

Pushing The Limit of LLM Capacity for Text Classification

The value of text classification's future research has encountered challenges and uncertainties, due to the extraordinary efficacy demonstrated by large language models (LLMs) across numerous downstream NLP tasks. In this era of open-ended language modeling, where task boundaries are gradually fading, an urgent question emerges: have we made significant advances in text classification under the full benefit of LLMs? To answer this question, we propose RGPT, an adaptive boosting framework tailored to produce a specialized text classification LLM by recurrently ensembling a pool of strong base learners. The base learners are constructed by adaptively adjusting the distribution of training samples and iteratively fine-tuning LLMs with them. Such base learners are then ensembled to be a specialized text classification LLM, by recurrently incorporating the historical predictions from the previous learners. Through a comprehensive empirical comparison, we show that RGPT significantly outperforms 8 SOTA PLMs and 7 SOTA LLMs on four benchmarks by 1.36% on average. Further evaluation experiments show a clear surpassing of RGPT over human classification.

LARA: Linguistic-Adaptive Retrieval-Augmented LLMs for Multi-Turn Intent Classification

Following the significant achievements of large language models (LLMs), researchers have employed in-context learning for text classification tasks. However, these studies focused on monolingual, single-turn classification tasks. In this paper, we introduce LARA (Linguistic-Adaptive Retrieval-Augmented Language Models), designed to enhance accuracy in multi-turn classification tasks across six languages, accommodating numerous intents in chatbot interactions. Multi-turn intent classification is notably challenging due to the complexity and evolving nature of conversational contexts. LARA tackles these issues by combining a fine-tuned smaller model with a retrieval-augmented mechanism, integrated within the architecture of LLMs. This integration allows LARA to dynamically utilize past dialogues and relevant intents, thereby improving the understanding of the context. Furthermore, our adaptive retrieval techniques bolster the cross-lingual capabilities of LLMs without extensive retraining and fine-tune. Comprehensive experiments demonstrate that LARA achieves state-of-the-art performance on multi-turn intent classification tasks, enhancing the average accuracy by 3.67% compared to existing methods.

CrossTune: Black-Box Few-Shot Classification with Label Enhancement

Training or finetuning large-scale language models (LLMs) requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks. One approach is to treat these models as black boxes and use forward passes (Inference APIs) to interact with them. Current research focuses on adapting these black-box models to downstream tasks using gradient-free prompt optimization, but this often involves an expensive process of searching task-specific prompts. Therefore, we are motivated to study black-box language model adaptation without prompt search. Specifically, we introduce a label-enhanced cross-attention network called CrossTune, which models the semantic relatedness between the input text sequence and task-specific label descriptions. Its effectiveness is examined in the context of few-shot text classification. To improve the generalization of CrossTune, we utilize ChatGPT to generate additional training data through in-context learning. A switch mechanism is implemented to exclude low-quality ChatGPT-generated data. Through extensive experiments on seven benchmark text classification datasets, we demonstrate that our proposed approach outperforms the previous state-of-the-art gradient-free black-box tuning method by 5.7% on average. Even without using ChatGPT-augmented data, CrossTune performs better or comparably than previous black-box tuning methods, suggesting the effectiveness of our approach.

Label Propagation for Zero-shot Classification with Vision-Language Models

Vision-Language Models (VLMs) have demonstrated impressive performance on zero-shot classification, i.e. classification when provided merely with a list of class names. In this paper, we tackle the case of zero-shot classification in the presence of unlabeled data. We leverage the graph structure of the unlabeled data and introduce ZLaP, a method based on label propagation (LP) that utilizes geodesic distances for classification. We tailor LP to graphs containing both text and image features and further propose an efficient method for performing inductive inference based on a dual solution and a sparsification step. We perform extensive experiments to evaluate the effectiveness of our method on 14 common datasets and show that ZLaP outperforms the latest related works. Code: https://github.com/vladan-stojnic/ZLaP

On the Fragility of Active Learners

Active learning (AL) techniques aim to maximally utilize a labeling budget by iteratively selecting instances that are most likely to improve prediction accuracy. However, their benefit compared to random sampling has not been consistent across various setups, e.g., different datasets, classifiers. In this empirical study, we examine how a combination of different factors might obscure any gains from an AL technique. Focusing on text classification, we rigorously evaluate AL techniques over around 1000 experiments that vary wrt the dataset, batch size, text representation and the classifier. We show that AL is only effective in a narrow set of circumstances. We also address the problem of using metrics that are better aligned with real world expectations. The impact of this study is in its insights for a practitioner: (a) the choice of text representation and classifier is as important as that of an AL technique, (b) choice of the right metric is critical in assessment of the latter, and, finally, (c) reported AL results must be holistically interpreted, accounting for variables other than just the query strategy.