Mexico

Adversarial example attacks against textual data have been drawing increasing attention in both the natural language processing (NLP) and security domains. However, most of the existing attacks overlook the importance of semantic similarity and yield easily recognizable adversarial samples. As a result, the defense methods developed in response to these attacks remain vulnerable and could be evaded by advanced adversarial examples that maintain high semantic similarity with the original, non-adversarial text. Hence, this paper aims to investigate the extent of textual adversarial examples in maintaining such high semantic similarity. We propose \emph{Reinforce} attack, a reinforcement learning-based framework to generate adversarial text that preserves high semantic similarity with the original text. In particular, the attack process is controlled by a reward function rather than heuristics, as in previous methods, to encourage higher semantic similarity and lower query costs. Through automatic and human evaluations, we show that our generated adversarial texts preserve significantly higher semantic similarity than state-of-the-art attacks while achieving similar attack success rates (outperforming at times), thus uncovering novel challenges for effective defenses.

NAACL 2024

Semantic-Preserving Adversarial Example Attack against BERT

large language models

adversarial attacks

reinforcement learning

### Welcome!
The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. Our Virtual Poster Sessions will take place online Thursday, August 22, 2024.

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

workshop paper

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
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Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
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2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

In the expanding field of language model applications, medical knowledge representation remains a significant challenge due to the specialized nature of the domain. Large language models, such as GPT-4, obtain reasonable scores on medical question-answering tasks, but smaller models are far behind.
In this work, we introduce a method to improve the proficiency of a small language model in the medical domain by employing a two-fold approach. We first fine-tune the model on a corpus of medical textbooks. Then, we use GPT-4 to generate questions similar to the downstream task, prompted with textbook knowledge, and use them to fine-tune the model. Additionally, we introduce ECN-QA, a novel Medical QA dataset containing ``progressive questions'' composed of related sequential questions. We show the benefits of our training strategy on this dataset.
The study's findings highlight the potential of small language models in the medical domain when appropriately fine-tuned.

Efficient Medical Question Answering with Knowledge-Augmented Question Generation

Electronic health records (EHR) and claims data are rich sources of real-world data that reflect patient health status and healthcare utilization. 
Querying these databases to answer epidemiological questions is challenging due to the intricacy of medical terminology and the need for complex SQL queries. Here, we introduce an end-to-end methodology that combines text-to-SQL generation with retrieval augmented generation (RAG) to answer epidemiological questions using EHR and claims data. We show that our approach, which integrates a medical coding step into the text-to-SQL process, significantly improves the performance over simple prompting. Our findings indicate that although current language models are not yet sufficiently accurate for unsupervised use, RAG offers a promising direction for improving their capabilities, as shown in a realistic industry setting.

Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records

Language Models (LMs) have been shown to inherit undesired biases that might hurt minorities and underrepresented groups if such systems were integrated into real-world applications without careful fairness auditing.
This paper proposes FairBelief, an analytical approach to capture and assess beliefs, i.e., propositions that an LM may embed with different degrees of confidence and that covertly influence its predictions. With FairBelief, we leverage prompting to study the behavior of several state-of-the-art LMs across different previously neglected axes, such as model scale and likelihood, assessing predictions on a fairness dataset specifically designed to quantify LMs' outputs' hurtfulness.
Finally, we conclude with an in-depth qualitative assessment of the beliefs emitted by the models.
We apply FairBelief to English LMs, revealing that, although these architectures enable high performances on diverse natural language processing tasks, they show hurtful beliefs about specific genders. Interestingly, training procedure and dataset, model scale, and architecture induce beliefs of different degrees of hurtfulness.

FairBelief - Assessing Harmful Beliefs in Language Models

In this paper, we introduce a new black-box attack vector, termed the Sandwich attack. This is a multi-language mixture attack that manipulates state-of-the-art Language Learning Models (LLMs) into generating harmful and misaligned responses, with an overall success rate exceeding 50%. It only allows the models to produce safe responses 38% of the time. Our experiments with five different models, namely Google's Bard, Gemini Pro, LLaMA-2-70-B-Chat, GPT-3.5-Turbo, GPT-4, and Claude-3-OPUS, demonstrate that this attack vector can be exploited by adversaries to generate harmful and misaligned responses. Empirically, we show that state-of-the-art LLMs fail to perform self-evaluation in multi-language mixture settings. We also highlight a number of noteworthy behaviors and patterns observed in LLMs under the Sandwich attack. Lastly, we present an empirical investigation showing that safety mechanisms in LLMs rely more on English text than on other non-English text.

Sandwich attack: Multi-language Mixture Adaptive Attack on LLMs

Large language models incorporate world knowledge and present breakthrough performances on zero-shot learning. However, these models capture societal bias (e.g., gender or racial bias) due to bias during the training process which raises ethical concerns or can even be potentially harmful. The issue is more pronounced in multi-modal settings, such as image captioning, as images can also add onto biases (e.g., due to historical non-equal representation of genders in different occupations). In this study, we investigate the removal of potentially problematic knowledge from multi-modal models used for image captioning. We relax the gender bias issue in captioning models by degenderizing generated captions through the use of a simple linear mask, trained via adversarial training. Our proposal makes no assumption on the architecture of the model and freezes the model weights during the procedure, which also enables the mask to be turned off. We conduct experiments on COCO caption datasets using our masking solution. The results suggest that the proposed mechanism can effectively mask the targeted biased knowledge, by replacing more than 99\% gender words with neutral ones, and maintain a comparable captioning quality performance with minimal (e.g., -1.4 on BLEU4 and ROUGE) impact to accuracy metrics.

Erasing Latent Gender Knowledge for Debiasing Image Captioning

Modern language models are trained on large scrapes of the Web, containing hundreds of millions of instances of personal information (PI), many of which language models (LMs) can memorize, increasing privacy risks. In this work, we develop the regexes and rules (R\&R) detector suite, which outperforms the WIMBD detector suite for email addresses and phone numbers by reducing false positives and detecting phone numbers with the +1 country code. We then develop the first risk taxonomy for model memorization and parroting of personal information for these character-based PI types. Using our taxonomy, we evaluate a filtered, deduplicated subset of detected PI containing nearly a million instances of PI and find that approximately 8\% of them are memorized and verbatim parroted by the 6.9B Pythia model, i.e. when the model is prompted with the tokens that precede PI in the original document, greedy decoding generates that PI span exactly. When extrapolating this to the full Pile, we expect that approximately 1.3 million instances of PI are extreme risk with another 5 million instances being at least medium risk for the 6.9B model. We expand this analysis to study models of varying sizes (160M-6.9B) and timesteps of pretraining (70k-143k iterations) on the Pythia model suite and find that risk levels, model size, and amount of pretraining are all positively correlated. Taken together, our study suggests that PI is difficult to detect accurately and verbatim parroting is rampant, regardless of scale. We recommend that pretraining datasets be aggressively filtered and anonymized such that unwanted parroting of PI is minimized.

Evaluating Personal Information Parroting in Language Models

Language models, pre-trained on large amounts of unmoderated content, have been shown to contain societal biases. Mitigating such biases typically requires access to model parameters and training schemas. In this work, we address bias mitigation at inference time, such that it can be applied to any black-box model. To this end, we propose a belief generation and augmentation framework, BELIEVE, that demonstrates effective bias mitigation for natural language generation by augmenting input prompts with automatically generated instruction-based beliefs. Our framework eases the bottleneck required for manually crafting these instruction-based beliefs, by extending a recently proposed iterative in-context learning framework to automatically generate beliefs via a language model. We assess the impact of this system on fairness, and demonstrate effective bias mitigation on pretrained and instruction-tuned models for both sentiment and regard with respect to multiple protected classes including race, gender, and political ideology.

BELIEVE: Belief-Enhanced Instruction Generation and Augmentation for Zero-Shot Bias Mitigation

Mitigating bias in language models (LMs) has become a critical problem due to the widespread deployment of LMs. Numerous approaches revolve around data pre-processing and fine-tuning of language models, tasks that can be both time-consuming and computationally demanding. Consequently, there is a growing interest in machine unlearning techniques given their capacity to induce the forgetting of undesired behaviors of the existing pre-trained or fine-tuned models with lower computational cost. In this work, we explore two unlearning methods, (1) Partitioned Contrastive Gradient Unlearning (PCGU) applied on decoder models and (2) Negation via Task Vector, to reduce social biases in state-of-the-art and open-source LMs such as LLaMA-2 and OPT. We also implement distributed PCGU for large models. It is empirically shown, through quantitative and qualitative analyses, that negation via Task Vector method outperforms PCGU in debiasing with minimum deterioration in performance and perplexity of the models. On LLaMA-2 7B, negation via Task Vector reduces the bias score by 11.8%.

Mitigating Social Biases in Language Models through Unlearning

Multilingual pre-trained Large Language Models (LLMs) are incredibly effective at Question Answering (QA), a core task in Natural Language Understanding, achieving high accuracies on several multilingual benchmarks. However, little is known about how well their confidences are calibrated. In this paper, we comprehensively benchmark the calibration of several multilingual LLMs (MLLMs) on a variety of QA tasks. We perform extensive experiments, spanning encoder-only, encoder-decoder, and decoder-only QA models (size varying from 110M to 7B parameters) and diverse languages, including both high- and low-resource ones. We study different dimensions of calibration in in-distribution, out-of-distribution, and cross-lingual transfer settings, and investigate strategies to improve it, including post-hoc methods and regularized fine-tuning. For decoder-only LLMs such as LlaMa2, we additionally find that in-context learning improves confidence calibration on multilingual data.
We also conduct several ablation experiments to study the effect of language distances, language corpus size, and model size on calibration, and how multilingual models compare with their monolingual counterparts for diverse tasks and languages. Our experiments suggest that the multilingual QA models are poorly calibrated for languages other than English and incorporating a small set of cheaply translated multilingual samples during fine-tuning/calibration effectively enhances the calibration performance.

On the Calibration of Multilingual Question Answering LLMs

This paper presents a comprehensive analysis of explainable fact-checking through a series of experiments, focusing on the ability of large language models to verify public health claims and provide explanations or justifications for their veracity assessments.
We examine the effectiveness of zero/few-shot prompting and parameter-efficient fine-tuning across various open and closed-source models, examining their performance in both isolated and joint tasks of veracity prediction and explanation generation. 
Importantly, we employ a dual evaluation approach comprising previously established automatic metrics and a novel set of criteria through human evaluation. 
Our automatic evaluation indicates that, within the zero-shot scenario, GPT-4 emerges as the standout performer, but in few-shot and parameter-efficient fine-tuning contexts, open-source models demonstrate their capacity to not only bridge the performance gap but, in some instances, surpass GPT-4.
Human evaluation reveals yet more nuance as well as indicating potential problems with the gold explanations.

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