GenAI4ELab/papercli-papers huggingface dataset:
SemEval-2023 Task 11: Learning with Disagreements (LeWiDi)
Authors: Elisa Leonardelli; Gavin Abercrombie; Dina Almanea; Valerio Basile; Tommaso Fornaciari; Barbara Plank; Verena Rieser; Alexandra Uma; Massimo Poesio
Venue: ACL 2023 (Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023))

Abstract: NLP datasets annotated with human judgments are rife with disagreements between the judges. This is especially true for tasks depending on subjective judgments such as sentiment analysis or offensive language detection. Particularly in these latter cases, the NLP community has come to realize that the common approach of reconciling’ these different subjective interpretations risks misrepresenting the evidence. Many NLP researchers have therefore concluded that rather than eliminating disagreements from annotated corpora, we should preserve themindeed, some argue that corpora should aim to preserve all interpretations produced by annotators. But this approach to corpus creation for NLP has not yet been widely accepted. The objective of the Le-Wi-Di series of shared tasks is to promote this approach to developing NLP models by providing a unified framework for training and evaluating with such datasets. We report on the second such shared task, which differs from the first edition in three crucial respects: (i) it focuses entirely on NLP, instead of both NLP and computer vision tasks in its first edition; (ii) it focuses on subjective tasks, instead of covering different types of disagreements as training with aggregated labels for subjective NLP tasks is in effect a misrepresentation of the data; and (iii) for the evaluation, we concentrated on soft approaches to evaluation. This second edition of Le-Wi-Di attracted a wide array of partici- pants resulting in 13 shared task submission papers.
SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)
Authors: Shamsuddeen Hassan Muhammad; Idris Abdulmumin; Seid Muhie Yimam; David Ifeoluwa Adelani; Ibrahim Said Ahmad; Nedjma Ousidhoum; Abinew Ali Ayele; Saif Mohammad; Meriem Beloucif; Sebastian Ruder
Venue: ACL 2023 (Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023))

Abstract: We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEval is a sentiment classification challenge in 14 African languages: Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yorb (Muhammad et al., 2023), using data labeled with 3 sentiment classes. We present three subtasks: (1) Task A: monolingual classification, which received 44 submissions; (2) Task B: multilingual classification, which received 32 submissions; and (3) Task C: zero-shot classification, which received 34 submissions. The best performance for tasks A and B was achieved by NLNDE team with 71.31 and 75.06 weighted F1, respectively. UCAS-IIE-NLP achieved the best average score for task C with 58.15 weighted F1. We describe the various approaches adopted by the top 10 systems and their approaches.
ITTC at SemEval 2023-Task 7: Document Retrieval and Sentence Similarity for Evidence Retrieval in Clinical Trial Data
Authors: Rahmad Mahendra; Damiano Spina; Karin Verspoor
Venue: ACL 2023 (Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023))

Abstract: This paper describes the submissions of the Natural Language Processing (NLP) team from the Australian Research Council Industrial Transformation Training Centre (ITTC) for Cognitive Computing in Medical Technologies to the SemEval 2023 Task 7, i.e., multi-evidence natural language inference for clinical trial data (NLI4CT). More specifically, we were working on subtask 2 whose objective is to identify the relevant parts of the premise from clinical trial report that justify the truth of information in the statement. We approach the evidence retrieval problem as a document retrieval and sentence similarity task. Our results show that the task poses some challenges which involve dealing with complex sentences and implicit evidences.
SemEval-2023 Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup
Authors: Jakub Piskorski; Nicolas Stefanovitch; Giovanni Da San Martino; Preslav Nakov
Venue: ACL 2023 (Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023))

Abstract: We describe SemEval-2023 task 3 on Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multilingual Setup: the dataset, the task organization process, the evaluation setup, the results, and the participating systems. The task focused on news articles in nine languages (six known to the participants upfront: English, French, German, Italian, Polish, and Russian), and three additional ones revealed to the participants at the testing phase: Spanish, Greek, and Georgian). The task featured three subtasks: (1) determining the genre of the article (opinion, reporting, or satire), (2) identifying one or more frames used in an article from a pool of 14 generic frames, and (3) identify the persuasion techniques used in each paragraph of the article, using a taxonomy of 23 persuasion techniques. This was a very popular task: a total of 181 teams registered to participate, and 41 eventually made an official submission on the test set.
SemEval-2023 Task 6: LegalEval - Understanding Legal Texts
Authors: Ashutosh Modi; Prathamesh Kalamkar; Saurabh Karn; Aman Tiwari; Abhinav Joshi; Sai Kiran Tanikella; Shouvik Kumar Guha; Sachin Malhan; Vivek Raghavan
Venue: ACL 2023 (Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023))

Abstract: In populous countries, pending legal cases have been growing exponentially. There is a need for developing NLP-based techniques for processing and automatically understanding legal documents. To promote research in the area of Legal NLP we organized the shared task LegalEval - Understanding Legal Texts at SemEval 2023. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document and Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case along with providing an explanation for the prediction. In total 26 teams (approx. 100 participants spread across the world) submitted systems paper. In each of the sub-tasks, the proposed systems outperformed the baselines; however, there is a lot of scope for improvement. This paper describes the tasks, and analyzes techniques proposed by various teams.
Towards Faithful Explanations for Text Classification with Robustness Improvement and Explanation Guided Training
Authors: Dongfang Li; Baotian Hu; Qingcai Chen; Shan He
Venue: ACL 2023 (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023))

Abstract: Feature attribution methods highlight the important input tokens as explanations to model predictions, which have been widely applied to deep neural networks towards trustworthy AI. However, recent works show that explanations provided by these methods face challenges of being faithful and robust. In this paper, we propose a method with Robustness improvement and Explanation Guided training towards more faithful EXplanations (REGEX) for text classification. First, we improve model robustness by input gradient regularization technique and virtual adversarial training. Secondly, we use salient ranking to mask noisy tokens and maximize the similarity between model attention and feature attribution, which can be seen as a self-training procedure without importing other external information. We conduct extensive experiments on six datasets with five attribution methods, and also evaluate the faithfulness in the out-of-domain setting. The results show that REGEX improves fidelity metrics of explanations in all settings and further achieves consistent gains based on two randomization tests. Moreover, we show that using highlight explanations produced by REGEX to train select-then-predict models results in comparable task performance to the end-to-end method.
Driving Context into Text-to-Text Privatization
Authors: Stefan Arnold; Dilara Yesilbas; Sven Weinzierl
Venue: ACL 2023 (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023))

Abstract: Metric Differential Privacy enables text-to-text privatization by adding calibrated noise to the vector of a word derived from an embedding space and projecting this noisy vector back to a discrete vocabulary using a nearest neighbor search. Since words are substituted without context, this mechanism is expected to fall short at finding substitutes for words with ambiguous meanings, such as ‘bank’. To account for these ambiguous words, we leverage a sense embedding and incorporate a sense disambiguation step prior to noise injection. We encompass our modification to the privatization mechanism with an estimation of privacy and utility. For word sense disambiguation on the Words in Context dataset, we demonstrate a substantial increase in classification accuracy by 6.05%.
Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models
Authors: Pranav Narayanan Venkit; Mukund Srinath; Shomir Wilson
Venue: ACL 2023 (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023))

Abstract: We analyze sentiment analysis and toxicity detection models to detect the presence of explicit bias against people with disability (PWD). We employ the bias identification framework of Perturbation Sensitivity Analysis to examine conversations related to PWD on social media platforms, specifically Twitter and Reddit, in order to gain insight into how disability bias is disseminated in real-world social settings. We then create the Bias Identification Test in Sentiment (BITS) corpus to quantify explicit disability bias in any sentiment analysis and toxicity detection models. Our study utilizes BITS to uncover significant biases in four open AIaaS (AI as a Service) sentiment analysis tools, namely TextBlob, VADER, Google Cloud Natural Language API, DistilBERT and two toxicity detection models, namely two versions of Toxic-BERT. Our findings indicate that all of these models exhibit statistically significant explicit bias against PWD.
Pay Attention to the Robustness of Chinese Minority Language Models! Syllable-level Textual Adversarial Attack on Tibetan Script
Authors: Xi Cao; Dolma Dawa; Nuo Qun; Trashi Nyima
Venue: ACL 2023 (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023))

Abstract: The textual adversarial attack refers to an attack method in which the attacker adds imperceptible perturbations to the original texts by elaborate design so that the NLP (natural language processing) model produces false judgments. This method is also used to evaluate the robustness of NLP models. Currently, most of the research in this field focuses on English, and there is also a certain amount of research on Chinese. However, to the best of our knowledge, there is little research targeting Chinese minority languages. Textual adversarial attacks are a new challenge for the information processing of Chinese minority languages. In response to this situation, we propose a Tibetan syllable-level black-box textual adversarial attack called TSAttacker based on syllable cosine distance and scoring mechanism. And then, we conduct TSAttacker on six models generated by fine-tuning two PLMs (pre-trained language models) for three downstream tasks. The experiment results show that TSAttacker is effective and generates high-quality adversarial samples. In addition, the robustness of the involved models still has much room for improvement.
Can we trust the evaluation on ChatGPT?
Authors: Rachith Aiyappa; Jisun An; Haewoon Kwak; Yong-yeol Ahn
Venue: ACL 2023 (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023))

Abstract: ChatGPT, the first large language model with mass adoption, has demonstrated remarkableperformance in numerous natural language tasks. Despite its evident usefulness, evaluatingChatGPT’s performance in diverse problem domains remains challenging due to the closednature of the model and its continuous updates via Reinforcement Learning from HumanFeedback (RLHF). We highlight the issue of data contamination in ChatGPT evaluations, with a case study in stance detection. We discuss the challenge of preventing data contamination and ensuring fair model evaluation in the age of closed and continuously trained models.
Improving Factuality of Abstractive Summarization via Contrastive Reward Learning
Authors: I-chun Chern; Zhiruo Wang; Sanjan Das; Bhavuk Sharma; Pengfei Liu; Graham Neubig
Venue: ACL 2023 (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023))

Abstract: Modern abstractive summarization models often generate summaries that contain hallucinated or contradictory information. In this paper, we propose a simple but effective contrastive learning framework that incorporates recent developments in reward learning and factuality metrics. Empirical studies demonstrate that the proposed framework enables summarization models to learn from feedback of factuality metrics using contrastive reward learning, leading to more factual summaries by human evaluations. This suggests that further advances in learning and evaluation algorithms can feed directly into providing more factual summaries. Code and human evaluation results will be publicly available at \url{https://github.com/EthanC111/factuality_summarization}.
Examining the Causal Impact of First Names on Language Models: The Case of Social Commonsense Reasoning
Authors: Sullam Jeoung; Jana Diesner; Halil Kilicoglu
Venue: ACL 2023 (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023))

Abstract: As language models continue to be integrated into applications of personal and societal relevance, ensuring these models’ trustworthiness is crucial, particularly with respect to producing consistent outputs regardless of sensitive attributes. Given that first names may serve as proxies for (intersectional) socio-demographic representations, it is imperative to examine the impact of first names on commonsense reasoning capabilities. In this paper, we study whether a model’s reasoning given a specific input differs based on the first names provided. Our underlying assumption is that the reasoning about Alice should not differ from the reasoning about James. We propose and implement a controlled experimental framework to measure the causal effect of first names on commonsense reasoning, enabling us to distinguish between model predictions due to chance and caused by actual factors of interest. Our results indicate that the frequency of first names has a direct effect on model prediction, with less frequent names yielding divergent predictions compared to more frequent names. To gain insights into the internal mechanisms of models that are contributing to these behaviors, we also conduct an in-depth explainable analysis. Overall, our findings suggest that to ensure model robustness, it is essential to augment datasets with more diverse first names during the configuration stage.
Reliability Check: An Analysis of GPT-3’s Response to Sensitive Topics and Prompt Wording
Authors: Aisha Khatun; Daniel Brown
Venue: ACL 2023 (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023))

Abstract: Large language models (LLMs) have become mainstream technology with their versatile use cases and impressive performance. Despite the countless out-of-the-box applications, LLMs are still not reliable. A lot of work is being done to improve the factual accuracy, consistency, and ethical standards of these models through fine-tuning, prompting, and Reinforcement Learning with Human Feedback (RLHF), but no systematic analysis of the responses of these models to different categories of statements, or on their potential vulnerabilities to simple prompting changes is available. In this work, we analyze what confuses GPT-3: how the model responds to certain sensitive topics and what effects the prompt wording has on the model response. We find that GPT-3 correctly disagrees with obvious Conspiracies and Stereotypes but makes mistakes with common Misconceptions and Controversies. The model responses are inconsistent across prompts and settings, highlighting GPT-3’s unreliability.
Sample Attackability in Natural Language Adversarial Attacks
Authors: Vyas Raina; Mark Gales
Venue: ACL 2023 (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023))

Abstract: Adversarial attack research in natural language processing (NLP) has made significant progress in designing powerful attack methods and defence approaches. However, few efforts have sought to identify which source samples are the most attackable or robust, i.e. can we determine for an unseen target model, which samples are the most vulnerable to an adversarial attack. This work formally extends the definition of sample attackability/robustness for NLP attacks. Experiments on two popular NLP datasets, four state of the art models and four different NLP adversarial attack methods, demonstrate that sample uncertainty is insufficient for describing characteristics of attackable/robust samples and hence a deep learning based detector can perform much better at identifying the most attackable and robust samples for an unseen target model. Nevertheless, further analysis finds that there is little agreement in which samples are considered the most attackable/robust across different NLP attack methods, explaining a lack of portability of attackability detection methods across attack methods.
A Keyword Based Approach to Understanding the Overpenalization of Marginalized Groups by English Marginal Abuse Models on Twitter
Authors: Kyra Yee; Alice Schoenauer Sebag; Olivia Redfield; Matthias Eck; Emily Sheng; Luca Belli
Venue: ACL 2023 (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023))

Abstract: Harmful content detection models tend to have higher false positive rates for content from marginalized groups. In the context of marginal abuse modeling on Twitter, such disproportionate penalization poses the risk of reduced visibility, where marginalized communities lose the opportunity to voice their opinion on the platform. Current approaches to algorithmic harm mitigation, and bias detection for NLP models are often very ad hoc and subject to human bias. We make two main contributions in this paper. First, we design a novel methodology, which provides a principled approach to detecting and measuring the severity of potential harms associated with a text-based model. Second, we apply our methodology to audit Twitter’s English marginal abuse model, which is used for removing amplification eligibility of marginally abusive content. Without utilizing demographic labels or dialect classifiers, we are still able to detect and measure the severity of issues related to the over-penalization of the speech of marginalized communities, such as the use of reclaimed speech, counterspeech, and identity related terms. In order to mitigate the associated harms, we experiment with adding additional true negative examples and find that doing so provides improvements to our fairness metrics without large degradations in model performance.
An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language Models
Authors: Saghar Hosseini; Hamid Palangi; Ahmed Hassan Awadallah
Venue: ACL 2023 (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023))

Abstract: Large-scale Pre-Trained Language Models (PTLMs) capture knowledge from massive human-written data which contains latent societal biases and toxic contents. In this paper, we leverage the primary task of PTLMs, i.e., language modeling, and propose a new metric to quantify manifested implicit representational harms in PTLMs towards 13 marginalized demographics. Using this metric, we conducted an empirical analysis of 24 widely used PTLMs. Our analysis provides insights into the correlation between the proposed metric in this work and other related metrics for representational harm. We observe that our metric correlates with most of the gender-specific metrics in the literature. Through extensive experiments, we explore the connections between PTLMs architectures and representational harms across two dimensions: depth and width of the networks. We found that prioritizing depth over width, mitigates representational harms in some PTLMs. Our code and data can be found at [place holder].
Linguistic Properties of Truthful Response
Authors: Bruce W. Lee; Benedict Florance Arockiaraj; Helen Jin
Venue: ACL 2023 (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023))

Abstract: We investigate the phenomenon of an LLM’s untruthful response using a large set of 220 handcrafted linguistic features. We focus on GPT-3 models and find that the linguistic profiles of responses are similar across model sizes. That is, how varying-sized LLMs respond to given prompts stays similar on the linguistic properties level. We expand upon this finding by training support vector machines that rely only upon the stylistic components of model responses to classify the truthfulness of statements. Though the dataset size limits our current findings, we present promising evidence that truthfulness detection is possible without evaluating the content itself. We release our code and raw data.
Debunking Biases in Attention
Authors: Shijing Chen; Usman Naseem; Imran Razzak
Venue: ACL 2023 (Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023))

Abstract: Despite the remarkable performances in various applications, machine learning (ML) models could potentially discriminate. They may result in biasness in decision-making, leading to an impact negatively on individuals and society. Recently, various methods have been developed to mitigate biasness and achieve significant performance. Attention mechanisms are a fundamental component of many state-of-the-art ML models and may potentially impact the fairness of ML models. However, how they explicitly influence fairness has yet to be thoroughly explored. In this paper, we investigate how different attention mechanisms affect the fairness of ML models, focusing on models used in Natural Language Processing (NLP) models. We evaluate the performance of fairness of several models with and without different attention mechanisms on widely used benchmark datasets. Our results indicate that the majority of attention mechanisms that have been assessed can improve the fairness performance of Bidirectional Gated Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) in all three datasets regarding religious and gender-sensitive groups, however, with varying degrees of trade-offs in accuracy measures. Our findings highlight the possibility of fairness being affected by adopting specific attention mechanisms in machine learning models for certain datasets