GenAI4ELab/papercli-papers huggingface dataset:
Multilingual Fact-Checking using LLMs
Authors: Aryan Singhal; Thomas Law; Coby Kassner; Ayushman Gupta; Evan Duan; Aviral Damle; Ryan Luo Li
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: Due to the recent rise in digital misinformation, there has been great interest shown in using LLMs for fact-checking and claim verification. In this paper, we answer the question: Do LLMs know multilingual facts and can they use this knowledge for effective fact-checking? To this end, we create a benchmark by filtering multilingual claims from the X-fact dataset and evaluating the multilingual fact-checking capabilities of five LLMs across five diverse languages: Spanish, Italian, Portuguese, Turkish, and Tamil on our benchmark. We employ three different prompting techniques: Zero-Shot, English Chain-of-Thought, and Cross-Lingual Prompting, using both greedy and self-consistency decoding. We extensively analyze our results and find that GPT-4o achieves the highest accuracy, but zero-shot prompting with self-consistency was the most effective overall. We also show that techniques like Chain-of-Thought and Cross-Lingual Prompting, which are designed to improve reasoning abilities, do not necessarily improve the fact-checking abilities of LLMs. Interestingly, we find a strong negative correlation between model accuracy and the amount of internet content for a given language. This suggests that LLMs are better at fact-checking from knowledge in low-resource languages. We hope that this study will encourage more work on multilingual fact-checking using LLMs.
Transferring Fairness using Multi-Task Learning with Limited Demographic Information
Authors: Carlos Alejandro Aguirre; Mark Dredze
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: Training supervised machine learning systems with a fairness loss can improve prediction fairness across different demographic groups. However, doing so requires demographic annotations for training data, without which we cannot produce debiased classifiers for most tasks. Drawing inspiration from transfer learning methods, we investigate whether we can utilize demographic data from a related task to improve the fairness of a target task. We adapt a single-task fairness loss to a multi-task setting to exploit demographic labels from a related task in debiasing a target task, and demonstrate that demographic fairness objectives transfer fairness within a multi-task framework. Additionally, we show that this approach enables intersectional fairness by transferring between two datasets with different single-axis demographics. We explore different data domains to show how our loss can improve fairness domains and tasks.
Selecting Shots for Demographic Fairness in Few-Shot Learning with Large Language Models
Authors: Carlos Aguirre; Kuleen Sasse; Isabel Cachola; Mark Dredze
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: Recently, work in NLP has shifted to few-shot (in-context) learning, with large language models (LLMs) performing well across a range of tasks. However, while fairness evaluations have become a standard for supervised methods, little is known about the fairness of LLMs as prediction systems. Further, common standard methods for fairness involve access to model weights or are applied during finetuning, which are not applicable in few-shot learning. Do LLMs exhibit prediction biases when used for standard NLP tasks?In this work, we analyze the effect of shots, which directly affect the performance of models, on the fairness of LLMs as NLP classification systems. We consider how different shot selection strategies, both existing and new demographically sensitive methods, affect model fairness across three standard fairness datasets. We find that overall the performance of LLMs is not indicative of their fairness, and there is not a single method that fits all scenarios. In light of these facts, we discuss how future work can include LLM fairness in evaluations.
Covert Bias: The Severity of Social Views’ Unalignment in Language Models Towards Implicit and Explicit Opinion
Authors: Abeer Aldayel; Areej Alokaili; Rehab Alahmadi
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: While various approaches have recently been studied for bias identification, little is known about how implicit language that does not explicitly convey a viewpoint affects bias amplification in large language models. To examine the severity of bias toward a view, we evaluated the performance of two downstream tasks where the implicit and explicit knowledge of social groups were used. First, we present a stress test evaluation by using a biased model in edge cases of excessive bias scenarios. Then, we evaluate how LLMs calibrate linguistically in response to both implicit and explicit opinions when they are aligned with conflicting viewpoints. Our findings reveal a discrepancy in LLM performance in identifying implicit and explicit opinions, with a general tendency of bias toward explicit opinions of opposing stances. Moreover, the bias-aligned models generate more cautious responses using uncertainty phrases compared to the unaligned (zero-shot) base models. The direct, incautious responses of the unaligned models suggest a need for further refinement of decisiveness by incorporating uncertainty markers to enhance their reliability, especially on socially nuanced topics with high subjectivity.
PG-Story: Taxonomy, Dataset, and Evaluation for Ensuring Child-Safe Content for Story Generation
Authors: Alicia Y. Tsai; Shereen Oraby; Anjali Narayan-Chen; Alessandra Cervone; Spandana Gella; Apurv Verma; Tagyoung Chung; Jing Huang; Nanyun Peng
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: Creating children’s stories through text generation is a creative task that requires stories to be both entertaining and suitable for young audiences. However, since current story generation systems often rely on pre-trained language models fine-tuned with limited story data, they may not always prioritize child-friendliness. This can lead to the unintended generation of stories containing problematic elements such as violence, profanity, and biases. Regrettably, despite the significance of these concerns, there is a lack of clear guidelines and benchmark datasets for ensuring content safety for children. In this paper, we introduce a taxonomy specifically tailored to assess content safety in text, with a strong emphasis on children’s well-being. We present PG-Story, a dataset that includes detailed annotations for both sentence-level and discourse-level safety. We demonstrate the potential of identifying unsafe content through self-diagnosis and employing controllable generation techniques during the decoding phase to minimize unsafe elements in generated stories.
Towards Explainable Multi-Label Text Classification: A Multi-Task Rationalisation Framework for Identifying Indicators of Forced Labour
Authors: Erick Mendez Guzman; Viktor Schlegel; Riza Batista-Navarro
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: The importance of rationales, or natural language explanations, lies in their capacity to bridge the gap between machine predictions and human understanding, by providing human-readable insights into why a text classifier makes specific decisions. This paper presents a novel multi-task rationalisation approach tailored to enhancing the explainability of multi-label text classifiers to identify indicators of forced labour. Our framework integrates a rationale extraction task with the classification objective and allows the inclusion of human explanations during training. We conduct extensive experiments using transformer-based models on a dataset consisting of 2,800 news articles, each annotated with labels and human-generated explanations. Our findings reveal a statistically significant difference between the best-performing architecture leveraging human rationales during training and variants using only labels. Specifically, the supervised model demonstrates a 10% improvement in predictive performance measured by the weighted F1 score, a 15% increase in the agreement between human and machine-generated rationales, and a 4% improvement in the generated rationales’ comprehensiveness. These results hold promising implications for addressing complex human rights issues with greater transparency and accountability using advanced NLP techniques.
All Models are Wrong, But Some are Deadly: Inconsistencies in Emotion Detection in Suicide-related Tweets
Authors: Annika Marie Schoene; Resmi Ramachandranpillai; Tomo Lazovich; Ricardo A. Baeza-Yates
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: Recent work in psychology has shown that people who experience mental health challenges are more likely to express their thoughts, emotions, and feelings on social media than share it with a clinical professional. Distinguishing suicide-related content, such as suicide mentioned in a humorous context, from genuine expressions of suicidal ideation is essential to better understanding context and risk. In this paper, we give a first insight and analysis into the differences between emotion labels annotated by humans and labels predicted by three fine-tuned language models (LMs) for suicide-related content. We find that (i) there is little agreement between LMs and humans for emotion labels of suicide-related Tweets and (ii) individual LMs predict similar emotion labels for all suicide-related categories. Our findings lead us to question the credibility and usefulness of such methods in high-risk scenarios such as suicide ideation detection.
Efficient Aspect-Based Summarization of Climate Change Reports with Small Language Models
Authors: Iacopo Ghinassi; Leonardo Catalano; Tommaso Colella
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: The use of Natural Language Processing (NLP) for helping decision-makers with Climate Change action has recently been highlighted as a use case aligning with a broader drive towards NLP technologies for social good. In this context, Aspect-Based Summarization (ABS) systems that extract and summarize relevant information are particularly useful as they provide stakeholders with a convenient way of finding relevant information in expert-curated reports. In this work, we release a new dataset for ABS of Climate Change reports and we employ different Large Language Models (LLMs) and so-called Small Language Models (SLMs) to tackle this problem in an unsupervised way. Considering the problem at hand, we also show how SLMs are not significantly worse for the problem while leading to reduced carbon footprint; we do so by applying for the first time an existing framework considering both energy efficiency and task performance to the evaluation of zero-shot generative models for ABS. Overall, our results show that modern language models, both big and small, can effectively tackle ABS for Climate Change reports but more research is needed when we frame the problem as a Retrieval Augmented Generation (RAG) problem and our work and dataset will help foster efforts in this direction.
An NLP Case Study on Predicting the Before and After of the Ukraine–Russia and Hamas–Israel Conflicts
Authors: Jordan Miner; John E. Ortega
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: We propose a method to predict toxicity and other textual attributes through the use of natural language processing (NLP) techniques for two recent events: the Ukraine-Russia and Hamas-Israel conflicts. This article provides a basis for exploration in future conflicts with hopes to mitigate risk through the analysis of social media before and after a conflict begins. Our work compiles several datasets from Twitter and Reddit for both conflicts in a before and after separation with an aim of predicting a future state of social media for avoidance. More specifically, we show that: (1) there is a noticeable difference in social media discussion leading up to and following a conflict and (2) social media discourse on platforms like Twitter and Reddit is useful in identifying future conflicts before they arise. Our results show that through the use of advanced NLP techniques (both supervised and unsupervised) toxicity and other attributes about language before and after a conflict is predictable with a low error of nearly 1.2 percent for both conflicts.
Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis
Authors: David F. Jenny; Yann Billeter; Bernhard Schölkopf; Zhijing Jin
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: The rapid advancement of Large Language Models (LLMs) has sparked intense debate regarding the prevalence of bias in these models and its mitigation. Yet, as exemplified by both results on debiasing methods in the literature and reports of alignment-related defects from the wider community, bias remains a poorly understood topic despite its practical relevance. To enhance the understanding of the internal causes of bias, we analyse LLM bias through the lens of causal fairness analysis, which enables us to both comprehend the origins of bias and reason about its downstream consequences and mitigation. To operationalize this framework, we propose a prompt-based method for the extraction of confounding and mediating attributes which contribute to the LLM decision process. By applying Activity Dependency Networks (ADNs), we then analyse how these attributes influence an LLM’s decision process. We apply our method to LLM ratings of argument quality in political debates. We find that the observed disparate treatment can at least in part be attributed to confounding and mitigating attributes and model misalignment, and discuss the consequences of our findings for human-AI alignment and bias mitigation.
AgriLLM:Harnessing Transformers for Framer Queries
Authors: Krish Didwania; Pratinav Seth; Aditya Kasliwal; Amit Agarwal
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information.The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps.Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture.This work explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types.
SciTechBaitRO: ClickBait Detection for Romanian Science and Technology News
Authors: Raluca-Andreea Gînga; Ana Sabina Uban
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: In this paper, we introduce a new annotated corpus of clickbait news in a low-resource language - Romanian, and a rarely covered domain - science and technology news: SciTechBaitRO. It is one of the first and the largest corpus (almost 11,000 examples) of annotated clickbait texts for the Romanian language and the first one to focus on the sci-tech domain, to our knowledge. We evaluate the possibility of automatically detecting clickbait through a series of data analysis and machine learning experiments with varied features and models, including a range of linguistic features, classical machine learning models, deep learning and pre-trained models. We compare the performance of models using different kinds of features, and show that the best results are given by the BERT models, with results of up to 89% F1 score. We additionally evaluate the models in a cross-domain setting for news belonging to other categories (i.e. politics, sports, entertainment) and demonstrate their capacity to generalize by detecting clickbait news outside of domain with high F1-scores.
Investigating Ableism in LLMs through Multi-turn Conversation
Authors: Guojun Wu; Sarah Ebling
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: To reveal ableism (i.e., bias against persons with disabilities) in large language models (LLMs), we introduce a novel approach involving multi-turn conversations, enabling a comparative assessment. Initially, we prompt the LLM to elaborate short biographies, followed by a request to incorporate information about a disability. Finally, we employ several methods to identify the top words that distinguish the disability-integrated biographies from those without. This comparative setting helps us uncover how LLMs handle disability-related information and reveal underlying biases. We observe that LLMs tend to highlight disabilities in a manner that can be perceived as patronizing or as implying that overcoming challenges is unexpected due to the disability.
Eliciting Uncertainty in Chain-of-Thought to Mitigate Bias against Forecasting Harmful User Behaviors
Authors: Anthony Sicilia; Malihe Alikhani
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: Conversation forecasting tasks a model with predicting the outcome of an unfolding conversation. For instance, it can be applied in social media moderation to predict harmful user behaviors before they occur, allowing for preventative interventions. While large language models (LLMs) have recently been proposed as an effective tool for conversation forecasting, it’s unclear what biases they may have, especially against forecasting the (potentially harmful) outcomes we request them to predict during moderation. This paper explores to what extent model uncertainty can be used as a tool to mitigate potential biases. Specifically, we ask three primary research questions: 1) how does LLM forecasting accuracy change when we ask models to represent their uncertainty; 2) how does LLM bias change when we ask models to represent their uncertainty; 3) how can we use uncertainty representations to reduce or completely mitigate biases without many training data points. We address these questions for 5 open-source language models tested on 2 datasets designed to evaluate conversation forecasting for social media moderation.
Inferring Mental Burnout Discourse Across Reddit Communities
Authors: Nazanin Sabri; Anh C. Pham; Ishita Kakkar; Mai ElSherief
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: Mental burnout refers to a psychological syndrome induced by chronic stress that negatively impacts the emotional and physical well-being of individuals. From the occupational context to personal hobbies, burnout is pervasive across domains and therefore affects the morale and productivity of society as a whole. Currently, no linguistic resources are available for the analysis or detection of burnout language. We address this gap by introducing a dataset annotated for burnout language. Given that social media is a platform for sharing life experiences and mental health struggles, our work examines the manifestation of burnout language in Reddit posts. We introduce a contextual word sense disambiguation approach to identify the specific meaning or context in which the word “burnout” is used, distinguishing between its application in mental health (e.g., job-related stress leading to burnout) and non-mental health contexts (e.g., engine burnout in a mechanical context). We create a dataset of 2,330 manually labeled Reddit posts for this task, as well as annotating the reason the poster associates with their burnout (e.g., professional, personal, non-traditional). We train machine learning models on this dataset achieving a minimum F1 score of 0.84 on the different tasks. We make our dataset of annotated Reddit post IDs publicly available to help advance future research in this field.
Decoding Ableism in Large Language Models: An Intersectional Approach
Authors: Rong Li; Ashwini Kamaraj; Jing Ma; Sarah Ebling
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: With the pervasive use of large language models (LLMs) across various domains, addressing the inherent ableist biases within these models requires more attention and resolution. This paper examines ableism in three LLMs (GPT-3.5, GPT-4, and Llama 3) by analyzing the intersection of disability with two additional social categories: gender and social class. Utilizing two task-specific prompts, we generated and analyzed text outputs with two metrics, VADER and regard, to evaluate sentiment and social perception biases within the responses. Our results indicate a marked improvement in bias mitigation from GPT-3.5 to GPT-4, with the latter demonstrating more positive sentiments overall, while Llama 3 showed comparatively weaker performance. Additionally, our findings underscore the complexity of intersectional biases: These biases are shaped by the combined effects of disability, gender, and class, which alter the expression and perception of ableism in LLM outputs. This research highlights the necessity for more nuanced and inclusive bias mitigation strategies in AI development, contributing to the ongoing dialogue on ethical AI practices.
Explainable Identification of Hate Speech towards Islam using Graph Neural Networks
Authors: Azmine Toushik Wasi
Venue: EMNLP 2024 (Proceedings of the Third Workshop on NLP for Positive Impact)

Abstract: Islamophobic language on online platforms fosters intolerance, making detection and elimination crucial for promoting harmony. Traditional hate speech detection models rely on NLP techniques like tokenization, part-of-speech tagging, and encoder-decoder models. However, Graph Neural Networks (GNNs), with their ability to utilize relationships between data points, offer more effective detection and greater explainability. In this work, we represent speeches as nodes and connect them with edges based on their context and similarity to develop the graph. This study introduces a novel paradigm using GNNs to identify and explain hate speech towards Islam. Our model leverages GNNs to understand the context and patterns of hate speech by connecting texts via pretrained NLP-generated word embeddings, achieving state-of-the-art performance and enhancing detection accuracy while providing valuable explanations. This highlights the potential of GNNs in combating online hate speech and fostering a safer, more inclusive online environment.