Mexico

Large language models (LLMs), trained on vast datasets, can carry biases that manifest in various forms, from overt discrimination to implicit stereotypes. One facet of bias is performance disparities in LLMs, often harming underprivileged groups, such as racial minorities. A common approach to quantifying bias is to use template-based bias probes, which explicitly state group membership (e.g. White) and evaluate if the outcome of a task, sentiment analysis for instance, is invariant to the change of group membership (e.g. change White race to Black). This approach is widely used in bias quantification. However, in this work, we find evidence of an unexpectedly overlooked consequence of using template-based probes for LLM bias quantification. We find that in doing so, text examples associated with White ethnicities appear to be classified as exhibiting negative sentiment at elevated rates. We hypothesize that the scenario arises artificially through a mismatch between the pre-training text of LLMs and the templates used to measure bias through reporting bias, unstated norms that imply group membership without explicit statement. Our finding highlights the potential misleading impact of varying group membership through explicit mention in bias quantification.

NAACL 2024

Reevaluating Bias Detection in Language Models: The Role of Implicit Norms

bias in large language models

template-based bias quantification

reporting bias

large language models

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
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This General Chair’s message is where I express my gratitude to the many individuals who have made
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Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

workshop paper

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
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Welcome and hope you all enjoy the conference! 
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

Modern large language models (LLMs) have a significant amount of world knowledge, which enables strong performance in commonsense reasoning and knowledge-intensive tasks when harnessed properly. The language model can also learn social biases, which has a significant potential for societal harm. There have been many mitigation strategies proposed for LLM safety, but it is unclear how effective they are for eliminating social biases. In this work, we propose a new methodology for attacking language models with knowledge graph-augmented generation. We refactor natural language stereotypes into a knowledge graph, and use adversarial attacking strategies to induce biased responses from several open- and closed-source language models. We find our method increases bias in all models, even those trained with safety guardrails. This demonstrates the need for further research in AI safety, and we encourage further work in this new adversarial space.

BiasKG: Adversarial Knowledge Graphs to Induce Bias in Large Language Models

Amidst growing popularity of language models (LMs), the appropriate communication of uncertainty is crucial to reliable human-AI interactions. Existing work has examined models' abilities to communicate their internal uncertainty but does not consider how LMs interpret communicated uncertainty. In this paper, we investigate the ability of LMs to understand the degree of confidence expressed in language. To this end, we systematically analyse how models' behaviors change with the use of different probability phrases (e.g., likely, doubtful, unlikely) and compare LMs' perceptions to previously reported human perceptions. We find that, while popular LMs are able to rank probability phrases, models struggle to assign accurate estimates for each probability phrase, failing to distinguish phrases, such as doubtful, uncertain, probable. Furthermore, ChatGPT tends to mirror human perceptions, whilst perceptions of Llama-2 7B and Gemma 7B reveal disparate behaviors across different experiments.

Can Language Models Interpret Verbalized Uncertainty?

As the utilization of large language models (LLMs) has proliferated worldwide, it is crucial for them to have adequate knowledge and fair representation for diverse global cultures. In this work, we uncover culture perceptions of three SOTA models on 110 countries and regions on 8 culture-related topics through culture-conditioned generations, and extract symbols from these generations that are associated to each culture by the LLM. We discover that culture-conditioned generation consist of linguistic "markers" that distinguish marginalized cultures apart from default cultures. We also discover that LLMs have an uneven degree of diversity in the culture symbols, and that cultures from different geographic regions have different presence in LLMs' culture-agnostic generation. Our findings promote further research in studying the knowledge and fairness of global culture perception in LLMs. Code and Data can be found in: https://github.com/huihanlhh/Culture-Gen/

CULTURE-GEN: Natural Language Prompts Reveal Uneven Culture Presence in Language Models

In the dynamic realm of call center communications, the potential of abstractive summarization to transform information condensation is evident. However, evaluating the performance of abstractive summarization systems within contact center domain poses a significant challenge. Traditional evaluation metrics prove inadequate in capturing the multifaceted nature of call center conversations, characterized by diverse topics, emotional nuances, and dynamic contexts. This paper uses domain-specific perturbed summaries to scrutinize the robustness of summarization metrics in the call center domain. Through extensive experiments on call center data, we illustrate how perturbed summaries uncover limitations in existing metrics. We additionally utilize perturbation as data augmentation strategy to train domain-specific metrics. Our findings underscore the potential of perturbed summaries to complement current evaluation techniques, advancing reliable and adaptable summarization solutions in the call center domain.

Tweak to Trust: Assessing the Reliability of Summarization Metrics in Contact Centers via Perturbed Summaries

Multimodal Large Language Models (MLLMs) are commonly evaluated using costly annotated multimodal benchmarks. However, these benchmarks often struggle to keep pace with the rapidly advancing requirements of MLLM evaluation. We propose GenCeption, a novel and annotation-free MLLM evaluation framework that merely requires unimodal data to assess inter-modality semantic coherence and inversely reflects the models' inclination to hallucinate. Analogous to the popular DrawCeption game, GenCeption initiates with a non-textual sample and undergoes a series of iterative description and generation steps. Semantic drift across iterations is quantified using the GC@T metric. Our empirical findings validate GenCeption's efficacy, showing strong correlations with popular MLLM benchmarking results. GenCeption may be extended to mitigate training data contamination by utilizing ubiquitous, previously unseen unimodal data.

Introducing GenCeption for Multimodal LLM Benchmarking: You May Bypass Annotations

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.

Semantic-Preserving Adversarial Example Attack against BERT

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.

Next from NAACL 2024

BiasKG: Adversarial Knowledge Graphs to Induce Bias in Large Language Models

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This prompt is measuring <mask>: evaluating bias evaluation in language models