GenAI4ELab/papercli-papers huggingface dataset:
Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models
Authors: Tobias Groot; Matias Valdenegro - Toro
Venue: NAACL 2024 (Proceedings of the 4th Workshop on Trustworthy Natural Language Processing (TrustNLP 2024))

Abstract: Language and Vision-Language Models (LLMs/VLMs) have revolutionized the field of AI by their ability to generate human-like text and understand images, but ensuring their reliability is crucial. This paper aims to evaluate the ability of LLMs (GPT4, GPT-3.5, LLaMA2, and PaLM 2) and VLMs (GPT4V and Gemini Pro Vision) to estimate their verbalized uncertainty via prompting. We propose the new Japanese Uncertain Scenes (JUS) dataset, aimed at testing VLM capabilities via difficult queries and object counting, and the Net Calibration Error (NCE) to measure direction of miscalibration.Results show that both LLMs and VLMs have a high calibration error and are overconfident most of the time, indicating a poor capability for uncertainty estimation. Additionally we develop prompts for regression tasks, and we show that VLMs have poor calibration when producing mean/standard deviation and 95% confidence intervals.
Tweak to Trust: Assessing the Reliability of Summarization Metrics in Contact Centers via Perturbed Summaries
Authors: Kevin Patel; Suraj Agrawal; Ayush Kumar
Venue: NAACL 2024 (Proceedings of the 4th Workshop on Trustworthy Natural Language Processing (TrustNLP 2024))

Abstract: 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.
Flatness-Aware Gradient Descent for Safe Conversational AI
Authors: Leila Khalatbari; Saeid Hosseini; Hossein Sameti; Pascale Fung
Venue: NAACL 2024 (Proceedings of the 4th Workshop on Trustworthy Natural Language Processing (TrustNLP 2024))

Abstract: As generative dialog models become ubiquitous in real-world applications, it is paramount to ensure a harmless generation. There are two major challenges when enforcing safety to open-domain chatbots. Firstly, it is impractical to provide training data reflecting the desired response to all emerging forms of toxicity (generalisation challenge). Secondly, implementing safety features may compromise the quality of the conversation (trade-off challenge). To tackle the challenges, this paper introduces a regularized fine-tuning approach called FlatGD. By employing a safety-tailored loss, we translate better optimization to more safety. To ensure better optimization, FlatGD penalizes sharp trajectories of loss curve, encouraging flatness of the converged local minima. Experimental results on datasets of “BAD” and “prosocial dialog” demonstrate that our model outperforms the current baselines in reducing toxicity while preserving the conversation quality. Moreover, compared to other baselines, FlatGD can better generalize to unseen toxic data.
Introducing GenCeption for Multimodal LLM Benchmarking: You May Bypass Annotations
Authors: Lele Cao; Valentin Buchner; Zineb Senane; Fangkai Yang
Venue: NAACL 2024 (Proceedings of the 4th Workshop on Trustworthy Natural Language Processing (TrustNLP 2024))

Abstract: 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.
Semantic-Preserving Adversarial Example Attack against BERT
Authors: Chongyang Gao; Kang Gu; Soroush Vosoughi; Shagufta Mehnaz
Venue: NAACL 2024 (Proceedings of the 4th Workshop on Trustworthy Natural Language Processing (TrustNLP 2024))

Abstract: 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 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.
Sandwich attack: Multi-language Mixture Adaptive Attack on LLMs
Authors: Bibek Upadhayay; Vahid Behzadan
Venue: NAACL 2024 (Proceedings of the 4th Workshop on Trustworthy Natural Language Processing (TrustNLP 2024))

Abstract: A significant challenge in reliable deployment of Large Language Models (LLMs) is malicious manipulation via adversarial prompting techniques such as jailbreaks. Employing mechanisms such as safety training have proven useful in addressing this challenge. However, in multilingual LLMs, adversaries can exploit the imbalanced representation of low-resource languages in datasets used for pretraining and safety training. In this paper, we introduce a new black-box attack vector called the Sandwich Attack: a multi-language mixture attack, which manipulates state-of-the-art LLMs into generating harmful and misaligned responses. Our experiments with five different models, namely Bard, Gemini Pro, LLaMA-2-70-B-Chat, GPT-3.5-Turbo, GPT-4, and Claude-3-OPUS, show that this attack vector can be used by adversaries to elicit harmful responses from these models. By detailing both the mechanism and impact of the Sandwich attack, this paper aims to guide future research and development towards more secure and resilient LLMs, ensuring they serve the public good while minimizing potential for misuse. Content Warning: This paper contains examples of harmful language.
Masking Latent Gender Knowledge for Debiasing Image Captioning
Authors: Fan Yang; Shalini Ghosh; Emre Barut; Kechen Qin; Prashan Wanigasekara; Chengwei Su; Weitong Ruan; Rahul Gupta
Venue: NAACL 2024 (Proceedings of the 4th Workshop on Trustworthy Natural Language Processing (TrustNLP 2024))

Abstract: 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.
BELIEVE: Belief-Enhanced Instruction Generation and Augmentation for Zero-Shot Bias Mitigation
Authors: Lisa Bauer; Ninareh Mehrabi; Palash Goyal; Kai-Wei Chang; Aram Galstyan; Rahul Gupta
Venue: NAACL 2024 (Proceedings of the 4th Workshop on Trustworthy Natural Language Processing (TrustNLP 2024))

Abstract: 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.
Tell Me Why: Explainable Public Health Fact-Checking with Large Language Models
Authors: Majid Zarharan; Pascal Wullschleger; Babak Behkam Kia; Mohammad Taher Pilehvar; Jennifer Foster
Venue: NAACL 2024 (Proceedings of the 4th Workshop on Trustworthy Natural Language Processing (TrustNLP 2024))

Abstract: 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.
VarDial Evaluation Campaign 2024: Commonsense Reasoning in Dialects and Multi-Label Similar Language Identification
Authors: Adrian-Gabriel Chifu; Goran Glavaš; Radu Tudor Ionescu; Nikola Ljubešić; Aleksandra Miletić; Filip Miletić; Yves Scherrer; Ivan Vulić
Venue: NAACL 2024 (Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024))

Abstract: This report presents the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2024. The campaign is part of the eleventh workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with NAACL 2024. Two shared tasks were included this year: dialectal causal commonsense reasoning (DIALECT-COPA), and Multi-label classification of similar languages (DSL-ML). Both tasks were organized for the first time this year, but DSL-ML partially overlaps with the DSL-TL task organized in 2023.
What Drives Performance in Multilingual Language Models?
Authors: Sina Bagheri Nezhad; Ameeta Agrawal
Venue: NAACL 2024 (Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024))

Abstract: This study investigates the factors influencing the performance of multilingual large language models (MLLMs) across diverse languages. We study 6 MLLMs, including masked language models, autoregressive models, and instruction-tuned LLMs, on the SIB-200 dataset, a topic classification dataset encompassing 204 languages. Our analysis considers three scenarios: ALL languages, SEEN languages (present in the model’s pretraining data), and UNSEEN languages (not present or documented in the model’s pretraining data in any meaningful way). We examine the impact of factors such as pretraining data size, general resource availability, language family, and script type on model performance. Decision tree analysis reveals that pretraining data size is the most influential factor for SEEN languages. However, interestingly, script type and language family become more crucial for UNSEEN languages, highlighting the importance of cross-lingual transfer learning. Notably, model size and architecture do not significantly alter the most important features identified. Our findings provide valuable insights into the strengths and limitations of current MLLMs and hope to guide the development of more effective and equitable multilingual NLP systems.
Does Whisper Understand Swiss German? An Automatic, Qualitative, and Human Evaluation
Authors: Eyal Dolev; Clemens Lutz; Noëmi Aepli
Venue: NAACL 2024 (Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024))

Abstract: Whisper is a state-of-the-art automatic speech recognition (ASR) model (Radford et al., 2022). Although Swiss German dialects are allegedly not part of Whisper’s training data, preliminary experiments showed Whisper can transcribe Swiss German quite well, with the output being a speech translation into Standard German. To gain a better understanding of Whisper’s performance on Swiss German, we systematically evaluate it using automatic, qualitative, and human evaluation. We test its performance on three existing test sets: SwissDial (Dogan-Schönberger et al., 2021), STT4SG-350 (Plüss et al., 2023), and Swiss Parliaments Corpus (Plüss et al., 2021). In addition, we create a new test set for this study based on short mock clinical interviews. To automatically evaluate performance, we used word error rate (WER) and BLEU. We also conducted a qualitative analysis of Whisper’s performance, discussing its strengths and weaknesses. Finally, 28 people participated in a survey evaluating Whisper’s performance. All of our evaluations showed that Whisper is a viable ASR system for Swiss German, so long as the Standard German output is desired.
How Well Do Tweets Represent Sub-Dialects of Egyptian Arabic?
Authors: Mai Mohamed Eida; Mayar Nassar; Jonathan Dunn
Venue: NAACL 2024 (Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024))

Abstract: How well does naturally-occurring digital text, such as Tweets, represent sub-dialects of Egyptian Arabic (EA)? This paper focuses on two EA sub-dialects: Cairene Egyptian Arabic (CEA) and Sa’idi Egyptian Arabic (SEA). We use morphological markers from ground-truth dialect surveys as a distance measure across four geo-referenced datasets. Results show that CEA markers are prevalent as expected in CEA geo-referenced tweets, while SEA markers are limited across SEA geo-referenced tweets. SEA tweets instead show a prevalence of CEA markers and higher usage of Modern Standard Arabic. We conclude that corpora intended to represent sub-dialects of EA do not accurately represent sub-dialects outside of the Cairene variety. This finding calls into question the validity of relying on tweets alone to represent dialectal differences.
When Elote, Choclo and Mazorca are not the Same. Isomorphism-Based Perspective to the Spanish Varieties Divergences
Authors: Cristina España-Bonet; Ankur Bhatt; Koel Dutta Chowdhury; Alberto Barrón-Cedeño
Venue: NAACL 2024 (Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024))

Abstract: Spanish is an official language in 20 countries; in 19 of them, it arrived by means of overseas colonisation. Its close contact with several coexistent languages and the rich regional and cultural diversity has produced varieties which divert from each other. We study these divergences in a data-based approach and according to their qualitative and quantitative effects in word embeddings. We generate embeddings for Spanish in 24 countries and examine the topology of the spaces. Due to the similarities between varieties —in contrast to what happens to different languages in bilingual topological studies— we first scrutinise the behaviour of three isomorphism measures in (quasi-)isomorphic settings: relational similarity, Eigenvalue similarity and Gromov-Hausdorff distance. We then use the most trustworthy measure to quantify the divergences among varieties. Finally, we use the departures from isomorphism to build relational trees for the Spanish varieties by hierarchical clustering.
Modeling Orthographic Variation in Occitan’s Dialects
Authors: Zachary William Hopton; Noëmi Aepli
Venue: NAACL 2024 (Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024))

Abstract: Effectively normalizing spellings in textual data poses a considerable challenge, especially for low-resource languages lacking standardized writing systems. In this study, we fine-tuned a multilingual model with data from several Occitan dialects and conducted a series of experiments to assess the model’s representations of these dialects. For evaluation purposes, we compiled a parallel lexicon encompassing four Occitan dialects.Intrinsic evaluations of the model’s embeddings revealed that surface similarity between the dialects strengthened representations. When the model was further fine-tuned for part-of-speech tagging, its performance was robust to dialectical variation, even when trained solely on part-of-speech data from a single dialect. Our findings suggest that large multilingual models minimize the need for spelling normalization during pre-processing.
DIALECT-COPA: Extending the Standard Translations of the COPA Causal Commonsense Reasoning Dataset to South Slavic Dialects
Authors: Nikola Ljubešić; Nada Galant; Sonja Benčina; Jaka Čibej; Stefan Milosavljević; Peter Rupnik; Taja Kuzman
Venue: NAACL 2024 (Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024))

Abstract: The paper presents new causal commonsense reasoning datasets for South Slavic dialects, based on the Choice of Plausible Alternatives (COPA) dataset. The dialectal datasets are built by translating by native dialect speakers from the English original and the corresponding standard translation. Three dialects are covered – the Cerkno dialect of Slovenian, the Chakavian dialect of Croatian and the Torlak dialect of Serbian. The datasets are the first resource for evaluation of large language models on South Slavic dialects, as well as among the first commonsense reasoning datasets on dialects overall. The paper describes specific challenges met during the translation process. A comparison of the dialectal datasets with their standard language counterparts shows a varying level of character-level, word-level and lexicon-level deviation of dialectal text from the standard datasets. The observed differences are well reproduced in initial zero-shot and 10-shot experiments, where the Slovenian Cerkno dialect and the Croatian Chakavian dialect show significantly lower results than the Torlak dialect. These results show also for the dialectal datasets to be significantly more challenging than the standard datasets. Finally, in-context learning on just 10 examples shows to improve the results dramatically, especially for the dialects with the lowest results.
The Role of Adverbs in Language Variety Identification: The Case of Portuguese Multi-Word Adverbs
Authors: Izabela Müller; Nuno Mamede; Jorge Baptista
Venue: NAACL 2024 (Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024))

Abstract: This paper aims to assess the role of multiword compound adverbs in distinguishing Brazilian Portuguese (PT-BR) from European Portuguese (PT-PT). Two key factors underpin this focus: Firstly, multiword expressions often provide less ambiguity compared to single words, even when their meaning is idiomatic (non-compositional). Secondly, despite constituting a significant portion of lexicons in many languages, they are frequently overlooked in Natural Language Processing, possibly due to their heterogeneous nature and lexical range.For this study, a large lexicon of Portuguese multiword adverbs (3,665) annotated with diatopic information regarding language variety was utilized. The paper investigates the distribution of this category in a corpus consisting in excerpts from journalistic texts sourced from the DSL (Dialect and Similar Language) corpus, representing Brazilian (PT-BR) and European Portuguese (PT-PT), respectively, each partition containing 18,000 sentences.Results indicate a substantial similarity between the two varieties, with a considerable overlap in the lexicon of multiword adverbs. Additionally, specific adverbs unique to each language variety were identified. Lexical entries recognized in the corpus represent 18.2% (PT-BR) to 19.5% (PT-PT) of the lexicon, and approximately 5,700 matches in each partition. While many of the matches are spurious due to ambiguity with otherwise non-idiomatic, free strings, occurrences of adverbs marked as exclusive to one variety in texts from the other variety are rare.
NoMusic - The Norwegian Multi-Dialectal Slot and Intent Detection Corpus
Authors: Petter Mæhlum; Yves Scherrer
Venue: NAACL 2024 (Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024))

Abstract: This paper presents a new textual resource for Norwegian and its dialects. The NoMusic corpus contains Norwegian translations of the xSID dataset, an evaluation dataset for spoken language understanding (slot and intent detection). The translations cover Norwegian Bokmål, as well as eight dialects from three of the four major Norwegian dialect areas. To our knowledge, this is the first multi-parallel resource for written Norwegian dialects, and the first evaluation dataset for slot and intent detection focusing on non-standard Norwegian varieties. In this paper, we describe the annotation process and provide some analyses on the types of linguistic variation that can be found in the dataset.