GenAI4ELab/papercli-papers huggingface dataset:
Is there Gender Bias in Dependency Parsing? Revisiting “Women’s Syntactic Resilience”
Authors: Paul Go; Agnieszka Falenska
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: In this paper, we revisit the seminal work of Garimella et al. 2019, who reported that dependency parsers learn demographically-related signals from their training data and perform differently on sentences authored by people of different genders. We re-run all the parsing experiments from Garimella et al. 2019 and find that their results are not reproducible. Additionally, the original patterns suggesting the presence of gender biases fail to generalize to other treebank and parsing architecture. Instead, our data analysis uncovers methodological shortcomings in the initial study that artificially introduced differences into female and male datasets during preprocessing. These disparities potentially compromised the validity of the original conclusions.
From ‘Showgirls’ to ‘Performers’: Fine-tuning with Gender-inclusive Language for Bias Reduction in LLMs
Authors: Marion Bartl; Susan Leavy
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Gender bias is not only prevalent in Large Language Models (LLMs) and their training data, but also firmly ingrained into the structural aspects of language itself. Therefore, adapting linguistic structures within LLM training data to promote gender-inclusivity can make gender representations within the model more inclusive.The focus of our work are gender-exclusive affixes in English, such as in ‘show-girl’ or ‘man-cave’, which can perpetuate gender stereotypes and binary conceptions of gender.We use an LLM training dataset to compile a catalogue of 692 gender-exclusive terms along with gender-neutral variants and from this, develop a gender-inclusive fine-tuning dataset, the ‘Tiny Heap’. Fine-tuning three different LLMs with this dataset, we observe an overall reduction in gender-stereotyping tendencies across the models. Our approach provides a practical method for enhancing gender inclusivity in LLM training data and contributes to incorporating queer-feminist linguistic activism in bias mitigation research in NLP.
Sociodemographic Bias in Language Models: A Survey and Forward Path
Authors: Vipul Gupta; Pranav Narayanan Venkit; Shomir Wilson; Rebecca Passonneau
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Sociodemographic bias in language models (LMs) has the potential for harm when deployed in real-world settings. This paper presents a comprehensive survey of the past decade of research on sociodemographic bias in LMs, organized into a typology that facilitates examining the different aims: types of bias, quantifying bias, and debiasing techniques. We track the evolution of the latter two questions, then identify current trends and their limitations, as well as emerging techniques. To guide future research towards more effective and reliable solutions, and to help authors situate their work within this broad landscape, we conclude with a checklist of open questions.
Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP
Authors: Vagrant Gautam; Arjun Subramonian; Anne Lauscher; Os Keyes
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Personal names simultaneously differentiate individuals and categorize them in ways that are important in a given society. While the natural language processing community has thus associated personal names with sociodemographic characteristics in a variety of tasks, researchers have engaged to varying degrees with the established methodological problems in doing so. To guide future work that uses names and sociodemographic characteristics, we provide an overview of relevant research: first, we present an interdisciplinary background on names and naming. We then survey the issues inherent to associating names with sociodemographic attributes, covering problems of validity (e.g., systematic error, construct validity), as well as ethical concerns (e.g., harms, differential impact, cultural insensitivity). Finally, we provide guiding questions along with normative recommendations to avoid validity and ethical pitfalls when dealing with names and sociodemographic characteristics in natural language processing.
Evaluating Gender Bias in Multilingual Multimodal AI Models: Insights from an Indian Context
Authors: Kshitish Ghate; Arjun Choudhry; Vanya Bannihatti Kumar
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: We evaluate gender biases in multilingual multimodal image and text models in two settings: text-to-image retrieval and text-to-image generation, to show that even seemingly gender-neutral traits generate biased results. We evaluate our framework in the context of people from India, working with two languages: English and Hindi. We work with frameworks built around mCLIP-based models to ensure a thorough evaluation of recent state-of-the-art models in the multilingual setting due to their potential for widespread applications. We analyze the results across 50 traits for retrieval and 8 traits for generation, showing that current multilingual multimodal models are biased towards men for most traits, and this problem is further exacerbated for lower-resource languages like Hindi. We further discuss potential reasons behind this observation, particularly stemming from the bias introduced by the pretraining datasets.
Detecting and Mitigating LGBTQIA+ Bias in Large Norwegian Language Models
Authors: Selma Bergstrand; Björn Gambäck
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: The paper aims to detect and mitigate LGBTQIA+ bias in large language models (LLMs). As the usage of LLMs quickly increases, so does the significance of the harms they may cause due to bias. The research field of bias in LLMs has seen massive growth, but few attempts have been made to detect or mitigate other biases than gender bias, and most focus has been on English LLMs. This work shows experimentally that LLMs may cause representational harms towards LGBTQIA+ individuals when evaluated on sentence completion tasks and on a benchmark dataset constructed from stereotypes reported by the queer community of Norway, collected through a survey in order to directly involve the affected community. Furthermore, Norwegian training corpora are probed for queer bias, revealing strong associations between queer terms and anti-queer slurs, as well as words related to pedophilia. Finally, a fine-tuning-based debiasing method is applied to two Norwegian LLMs. This method does not consistently reduce bias, but shows that queer bias can be altered, laying the foundation for future debiasing approaches. By shedding light on the severe discrimination that can occur through the usage of LLMs, this paper contributes to the ongoing fight for equal rights for the LGBTQIA+ community.
Whose wife is it anyway? Assessing bias against same-gender relationships in machine translation
Authors: Ian Stewart; Rada Mihalcea
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Machine translation often suffers from biased data and algorithms that can lead to unacceptable errors in system output. While bias in gender norms has been investigated, less is known about whether MT systems encode bias about social relationships, e.g., “the lawyer kissed her wife.” We investigate the degree of bias against same-gender relationships in MT systems, using generated template sentences drawn from several noun-gender languages (e.g., Spanish) and comprised of popular occupation nouns. We find that three popular MT services consistently fail to accurately translate sentences concerning relationships between entities of the same gender. The error rate varies considerably based on the context, and same-gender sentences referencing high female-representation occupations are translated with lower accuracy. We provide this work as a case study in the evaluation of intrinsic bias in NLP systems with respect to social relationships.
Analysis of Annotator Demographics in Sexism Detection
Authors: Narjes Tahaei; Sabine Bergler
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: This study explores the effect of annotators’ demographic features on labeling sexist content in social media datasets, specifically focusing on the EXIST dataset, which includes direct sexist messages, reports and descriptions of sexist experiences and stereotypes. We investigate how various demographic backgrounds influence annotation outcomes and examine methods to incorporate these features into BERT-based model training. Our experiments demonstrate that adding demographic information improves performance in detecting sexism and assessing intention of the author.
An Empirical Study of Gendered Stereotypes in Emotional Attributes for Bangla in Multilingual Large Language Models
Authors: Jayanta Sadhu; Maneesha Saha; Rifat Shahriyar
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: The influence of Large Language Models (LLMs) is rapidly growing, automating more jobs over time. Assessing the fairness of LLMs is crucial due to their expanding impact. Studies reveal the reflection of societal norms and biases in LLMs, which creates a risk of propagating societal stereotypes in downstream tasks. Many studies on bias in LLMs focus on gender bias in various NLP applications. However, there’s a gap in research on bias in emotional attributes, despite the close societal link between emotion and gender. This gap is even larger for low-resource languages like Bangla. Historically, women are associated with emotions like empathy, fear, and guilt, while men are linked to anger, bravado, and authority. This pattern reflects societal norms in Bangla-speaking regions. We offer the first thorough investigation of gendered emotion attribution in Bangla for both closed and open source LLMs in this work. Our aim is to elucidate the intricate societal relationship between gender and emotion specifically within the context of Bangla. We have been successful in showing the existence of gender bias in the context of emotions in Bangla through analytical methods and also show how emotion attribution changes on the basis of gendered role selection in LLMs. All of our resources including code and data are made publicly available to support future research on Bangla NLP. Warning: This paper contains explicit stereotypical statements that many may find offensive.
Overview of the Shared Task on Machine Translation Gender Bias Evaluation with Multilingual Holistic Bias
Authors: Marta R. Costa-jussà; Pierre Andrews; Christine Basta; Juan Ciro; Agnieszka Falenska; Seraphina Goldfarb-Tarrant; Rafael Mosquera; Debora Nozza; Eduardo Sánchez
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: We describe the details of the Shared Task of the 5th ACL Workshop on Gender Bias in Natural Language Processing (GeBNLP 2024). The task uses dataset to investigate the quality of Machine Translation systems on a particular case of gender robustness. We report baseline results as well as the results of the first participants. The shared task will be permanently available in the Dynabench platform.
Human Speech Perception in Noise: Can Large Language Models Paraphrase to Improve It?
Authors: Anupama Chingacham; Miaoran Zhang; Vera Demberg; Dietrich Klakow
Venue: ACL 2024 (Proceedings of the 1st Human-Centered Large Language Modeling Workshop)

Abstract: Large Language Models (LLMs) can generate text by transferring style attributes like formality resulting in formal or informal text.However, instructing LLMs to generate text that when spoken, is more intelligible in an acoustically difficult environment, is an under-explored topic.We conduct the first study to evaluate LLMs on a novel task of generating acoustically intelligible paraphrases for better human speech perception in noise.Our experiments in English demonstrated that with standard prompting, LLMs struggle to control the non-textual attribute, i.e., acoustic intelligibility, while efficiently capturing the desired textual attributes like semantic equivalence. To remedy this issue, we propose a simple prompting approach, prompt-and-select, which generates paraphrases by decoupling the desired textual and non-textual attributes in the text generation pipeline.Our approach resulted in a 40% relative improvement in human speech perception, by paraphrasing utterances that are highly distorted in a listening condition with babble noise at signal-to-noise ratio (SNR) -5 dB. This study reveals the limitation of LLMs in capturing non-textual attributes, and our proposed method showcases the potential of using LLMs for better human speech perception in noise.
Human-Centered Design Recommendations for LLM-as-a-judge
Authors: Qian Pan; Zahra Ashktorab; Michael Desmond; Martín Santillán Cooper; James Johnson; Rahul Nair; Elizabeth Daly; Werner Geyer
Venue: ACL 2024 (Proceedings of the 1st Human-Centered Large Language Modeling Workshop)

Abstract: Traditional reference-based metrics, such as BLEU and ROUGE, are less effective for assessing outputs from Large Language Models (LLMs) that produce highly creative or superior-quality text, or in situations where reference outputs are unavailable. While human evaluation remains an option, it is costly and difficult to scale. Recent work using LLMs as evaluators (LLM-as-a-judge) is promising, but trust and reliability remain a significant concern. Integrating human input is crucial to ensure criteria used to evaluate are aligned with the human’s intent, and evaluations are robust and consistent. This paper presents a user study of a design exploration called EvaluLLM, that enables users to leverage LLMs as customizable judges, promoting human involvement to balance trust and cost-saving potential with caution. Through interviews with eight domain experts, we identified the need for assistance in developing effective evaluation criteria aligning the LLM-as-a-judge with practitioners’ preferences and expectations. We offer findings and design recommendations to optimize human-assisted LLM-as-judge systems.
Parameter-Efficient Detoxification with Contrastive Decoding
Authors: Tong Niu; Caiming Xiong; Yingbo Zhou; Semih Yavuz
Venue: ACL 2024 (Proceedings of the 1st Human-Centered Large Language Modeling Workshop)

Abstract: The field of natural language generation has witnessed significant advancements in recent years, including the development of controllable text generation techniques. However, controlling the attributes of the generated text remains a challenge, especially when aiming to avoid undesirable behavior such as toxicity. In this work, we introduce Detoxification Generator (DETOXIGEN), an inference-time algorithm that steers the generation away from unwanted styles. DETOXIGEN is an ensemble of a pre-trained language model (generator) and a detoxifier. The detoxifier is trained intentionally on the toxic data representative of the undesirable attribute, encouraging it to generate text in that style exclusively. During the actual generation, we use the trained detoxifier to produce undesirable tokens for the generator to contrast against at each decoding step. This approach directly informs the generator to avoid generating tokens that the detoxifier considers highly likely. We evaluate DETOXIGEN on the commonly used REALTOXICITYPROMPTS benchmark (Gehman et al., 2020) with various language models as generators. We find that it significantly outperforms previous approaches in detoxification metrics while not compromising on the generation quality. Moreover, the detoxifier is obtained by soft prompt-tuning using the same backbone language model as the generator. Hence, DETOXIGEN requires only a tiny amount of extra weights from the virtual tokens of the detoxifier to be loaded into GPU memory while decoding, making it a promising lightweight, practical, and parameter-efficient detoxification strategy.
To What Extent Are Large Language Models Capable of Generating Substantial Reflections for Motivational Interviewing Counseling Chatbots? A Human Evaluation
Authors: Erkan Basar; Iris Hendrickx; Emiel Krahmer; Gert-Jan Bruijn; Tibor Bosse
Venue: ACL 2024 (Proceedings of the 1st Human-Centered Large Language Modeling Workshop)

Abstract: Motivational Interviewing is a counselling style that requires skillful usage of reflective listening and engaging in conversations about sensitive and personal subjects. In this paper, we investigate to what extent we can use generative large language models in motivational interviewing chatbots to generate precise and variable reflections on user responses. We conduct a two-step human evaluation where we first independently assess the generated reflections based on four criteria essential to health counseling; appropriateness, specificity, naturalness, and engagement. In the second step, we compare the overall quality of generated and human-authored reflections via a ranking evaluation. We use GPT-4, BLOOM, and FLAN-T5 models to generate motivational interviewing reflections, based on real conversational data collected via chatbots designed to provide support for smoking cessation and sexual health. We discover that GPT-4 can produce reflections of a quality comparable to human-authored reflections. Finally, we conclude that large language models have the potential to enhance and expand reflections in predetermined health counseling chatbots, but a comprehensive manual review is advised.
Vision-Language Models under Cultural and Inclusive Considerations
Authors: Antonia Karamolegkou; Phillip Rust; Ruixiang Cui; Yong Cao; Anders Søgaard; Daniel Hershcovich
Venue: ACL 2024 (Proceedings of the 1st Human-Centered Large Language Modeling Workshop)

Abstract: Large Vision Language Models can be used to assist visually impaired individuals by describing images they capture in their daily lives. Current evaluation datasets may not reflect the diverse cultural user backgrounds nor the situational context of this use case. To address this problem, we create a survey to determine caption preferences and propose a culture-centric evaluation benchmark by filtering VizWiz, an existing dataset with images taken by people who are blind. We then evaluate different models and prompts, investigating their reliability as visual assistants. While the evaluation results for state-of-the-art models seem promising, we identified some weak spots such as hallucinations and problems with conventional evaluation metrics. Our survey, data, code, and model outputs will be publicly available.
Evaluating Large Language Models on Social Signal Sensitivity: An Appraisal Theory Approach
Authors: Zhen Wu; Ritam Dutt; Carolyn Rose
Venue: ACL 2024 (Proceedings of the 1st Human-Centered Large Language Modeling Workshop)

Abstract: We present a framework to assess the sensitivity of Large Language Models (LLMs) to textually embedded social signals using an Appraisal Theory perspective. We report on an experiment that uses prompts encoding three dimensions of social signals: Affect, Judgment, and Appreciation. In response to the prompt, an LLM generates both an analysis (Insight) and a conversational Response, which are analyzed in terms of sensitivity to the signals. We quantitatively evaluate the output text through topical analysis of the Insight and predicted social intelligence scores of the Response in terms of empathy and emotional polarity. Key findings show that LLMs are more sensitive to positive signals. The personas impact Responses but not the Insight. We discuss how our framework can be extended to a broader set of social signals, personas, and scenarios to evaluate LLM behaviors under various conditions.
Aligning to Adults Is Easy, Aligning to Children Is Hard: A Study of Linguistic Alignment in Dialogue Systems
Authors: Dorothea French; Sidney D’Mello; Katharina von der Wense
Venue: ACL 2024 (Proceedings of the 1st Human-Centered Large Language Modeling Workshop)

Abstract: During conversations, people align to one another over time, by using similar words, concepts, and syntax. This helps form a shared understanding of the conversational content and is associated with increased engagement and satisfaction. It also affects conversation outcomes: e.g., when talking to language learners, an above normal level of linguistic alignment of parents or language teachers is correlated with faster language acquisition. These benefits make human-like alignment an important property of dialogue systems, which has often been overlooked by the NLP community. In order to fill this gap, we ask: (RQ1) Due to the importance for engagement and satisfaction, to what degree do state-of-the-art dialogue systems align to adult users? (RQ2) With a potential application to child language acquisition in mind, do systems, similar to parents, show high levels of alignment during conversations with children? Our experiments show that ChatGPT aligns to adults at roughly human levels, while Llama2 shows elevated alignment. However, when responding to a child, both systems’ alignment is below human levels.
FINDINGS OF THE IWSLT 2024 EVALUATION CAMPAIGN
Authors: Ibrahim Said Ahmad; Antonios Anastasopoulos; Ondřej Bojar; Claudia Borg; Marine Carpuat; Roldano Cattoni; Mauro Cettolo; William Chen; Qianqian Dong; Marcello Federico; Barry Haddow; Dávid Javorský; Mateusz Krubiński; Tsz Kin Lam; Xutai Ma; Prashant Mathur; Evgeny Matusov; Chandresh Maurya; John P. McCrae; Kenton Murray; Satoshi Nakamura; Matteo Negri; Jan Niehues; Xing Niu; Atul Kr. Ojha; John Ortega; Sara Papi; Peter Polák; Adam Pospíšil; Pavel Pecina; Elizabeth Salesky; Nivedita Sethiya; Balaram Sarkar; Jiatong Shi; Claytone Sikasote; Matthias Sperber; Sebastian Stüker; Katsuhito Sudoh; Brian Thompson; Alex Waibel; Shinji Watanabe; Patrick Wilken; Petr Zemánek; Rodolfo Zevallos
Venue: ACL 2024 (Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024))

Abstract: This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, dialect and low-resource speech translation, and Indic languages. The shared tasks attracted 17 teams whose submissions are documented in 27 system papers. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia.