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Argument retrieval is the task of finding relevant arguments for a given query. While existing approaches rely solely on the semantic alignment of queries and arguments, this first shared task on perspective argument retrieval incorporates perspectives during retrieval, accounting for latent influences in argumentation. We present a novel multilingual dataset covering demographic and socio-cultural (socio) variables, such as age, gender, and political attitude, representing minority and majority groups in society. We distinguish between three scenarios to explore how retrieval systems consider explicitly (in both query and corpus) and implicitly (only in query) formulated perspectives. This paper provides an overview of this shared task and summarizes the results of the six submitted systems. We find substantial challenges in incorporating perspectivism, especially when aiming for personalization based solely on the text of arguments without explicitly providing socio profiles. Moreover, retrieval systems tend to be biased towards the majority group but partially mitigate bias for the female gender. While we bootstrap perspective argument retrieval, further research is essential to optimize retrieval systems to facilitate personalization and reduce polarization.

ACL 2024

Overview of PerpectiveArg2024 The First Shared Task on Perspective Argument Retrieval

argument retrieval

perspectivism

argument mining

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

This paper describes the contribution of team GESIS-DSM to the Perspective Argument Retrieval Task, a task on retrieving socio-culturally relevant and diverse arguments for different user queries. Our experiments and analyses aim to explore the nature of the socio-cultural specialization in argument retrieval: (how) do the arguments written by different socio-cultural groups differ? We investigate the impact of content and style for the task of identifying arguments relevant to a query and a certain demographic attribute. In its different configurations, our system employs sentence embedding representations, arguments generated with Large Language Model, as well as stylistic features. Our final method places third overall in the shared task, and, in comparison, does particularly well in the most difficult evaluation scenario, where the socio-cultural background of the argument author is implicit (i.e. has to be inferred from the text). This result indicates that socio-cultural differences in argument production may indeed be a matter of style.

GESIS-DSM at PerpectiveArg2024: A Matter of Style? Socio-Cultural Differences in Argumentation

This paper reports on the argument mining system submitted to the ArgMining workshop 2024 for The Perspective Argument Retrieval Shared Task (Falk et al., 2024). We combine the strengths of a smaller Sentence BERT model and a Large Language Model: the former is fine-tuned for a contrastive embedding objective and a classification objective whereas the latter is invoked to augment the query and populate the latent space with diverse relevant arguments. We conduct an ablation study on these components to find that each contributes substantially to the diversity and relevance criteria for the top-k retrieval of arguments from the given corpus.

XFACT Team0331 at PerspectiveArg2024: Sampling from Bounded Clusters for Diverse Relevant Argument Retrieval

This paper presents the POLygraph dataset, a unique resource for fake news detection in Polish. The dataset, created by an interdisciplinary team, is composed of two parts: the “fake-or-not” dataset with 11,360 pairs of news articles (identified by their URLs) and corresponding labels, and the “fake-they-say” dataset with 5,082 news articles (identified by their URLs) and tweets commenting on them. Unlike existing datasets, POLygraph encompasses a variety of approaches from source literature, providing a comprehensive resource for fake news detection. The data was collected through manual annotation by expert and non-expert annotators. The project also developed a software tool that uses advanced machine learning techniques to analyze the data and determine content authenticity. The tool and dataset are expected to benefit various entities, from public sector institutions to publishers and fact-checking organizations. Further dataset exploration will foster fake news detection and potentially stimulate the implementation of similar models in other languages. The paper focuses on the creation and composition of the dataset, so it does not include a detailed evaluation of the software tool for content authenticity analysis, which is planned at a later stage of the project.

POLygraph: Polish Fake News Datase

We present XARELLO: a generator of adversarial examples for testing the robustness of text classifiers based on reinforcement learning. Our solution is adaptive, it learns from previous successes and failures in order to better adjust to the vulnerabilities of the attacked model. This reflects the behaviour of a persistent and experienced attacker, which are common in the misinformation-spreading environment. We evaluate our approach using several victim classifiers and credibility-assessment tasks, showing it generates better-quality examples with less queries, and is especially effective against the modern LLMs. We also perform a qualitative analysis to understand the language patterns in the misinformation text that play a role in the attacks.

Know Thine Enemy: Adaptive Attacks on Misinformation Detection Using Reinforcement Learning

We consider how to credibly and reliably assess the opinions of individuals using their social media posts. To this end, this paper makes three contributions. First, we assemble a workflow and approach to applying modern natural language processing (NLP) methods to multi-target user stance detection in the wild. Second, we establish why the multi-target modeling of user stance is qualitatively more complicated than uni-target user-stance detection. Finally, we validate our method by showing how multi-dimensional measurement of user opinions not only reproduces known opinion polling results, but also enables the study of opinion dynamics at high levels of temporal and semantic resolution.

Multi-Target User Stance Discovery on Reddit

Online social networks often create echo chambers where people only hear opinions reinforcing their beliefs.
An echo chamber often generates polarization, leading to conflicts between people with radical opinions.
The echo chamber has been viewed as a human-specific problem, but this implicit assumption is becoming less reasonable as large language models, such as ChatGPT, acquire social abilities. 
In response to this situation, we investigated the potential for polarization to occur among a group of autonomous AI agents based on generative language models in an echo chamber environment. 
We had AI agents discuss specific topics and analyzed how the group's opinions changed as the discussion progressed. 
As a result, we found that the group of agents based on ChatGPT tended to become polarized in echo chamber environments. 
The analysis of opinion transitions shows that this result is caused by ChatGPT's high prompt understanding ability to update its opinion by considering its own and surrounding agents' opinions. 
We conducted additional experiments to investigate under what specific conditions AI agents tended to polarize. As a result, we identified factors that influence polarization, such as the agent's persona.

Polarization of Autonomous Generative AI Agents Under Echo Chambers

Corpora that are the fundament for toxicity detection contain such expressions typically directed against a target individual or group, e.g., people of a specific gender or ethnicity. Prior work has shown that the target identity mention can constitute a confounding variable. As an example, a model might learn that Christians are always mentioned in the context of hate speech. This misguided focus can lead to a limited generalization to newly emerging targets that are not found in the training data. In this paper, we hypothesize and subsequently show that this issue can be mitigated by considering targets on different levels of specificity. We distinguish levels of (1) the existence of a target, (2) a class (e.g., that the target is a religious group), or (3) a specific target group (e.g., Christians or Muslims). We define a target label hierarchy based on these three levels and then exploit this hierarchy in an adversarial correction for the lowest level (i.e. (3)) while maintaining some basic target features. This approach does not lower the toxicity detection performance but increases the generalization to targets not being available at training time.

Hierarchical Adversarial Correction to Mitigate Identity Term Bias in Toxicity Detection

This study examines a novel methodology for enhanced financial sentiment analysis and trading strategy development using large language models (LLMs) such as OPT, BERT, FinBERT, LLAMA 3, and RoBERTa. Utilizing a dataset of 965,375 U.S. financial news articles from 2010 to 2023, our research demonstrates that the GPT-3-based OPT significantly outperforms other models, achieving a prediction accuracy of 74.4% for stock market returns. Our findings reveal that the advanced capabilities of LLMs, particularly OPT, surpass traditional sentiment analysis methods such as the Loughran-McDonald dictionary model in predicting and explaining stock returns. For instance, a self-financing strategy based on OPT scores achieves a Sharpe ratio of 3.05 over our sample period, compared to a Sharpe ratio of 1.23 for the strategy based on the dictionary model. This study highlights the superior performance of LLMs in financial sentiment analysis, encouraging further research into integrating artificial intelligence and LLMs in financial markets.

Enhanced Financial Sentiment Analysis and Trading Strategy Development Using Large Language Models

In this paper, we address the issue of explainability in a transformer-based subjectivity regressor trained on native English speakers’ judgements. The main goal of this work is to test how the regressor's predictions, and therefore native speakers’ intuitions, relate to theoretical accounts of subjectivity. We approach this goal using two methods: a top-down manual selection of theoretically defined subjectivity features and a bottom-up extraction of top subjective and objective features using the LIME explanation method. The explainability of the subjectivity regressor is evaluated on a British news dataset containing sentences taken from social media news posts and from articles on the websites of the same news outlets. Both methods provide converging evidence that theoretically defined subjectivity features, such as emoji, evaluative adjectives, exclamations, questions, intensifiers, and first person pronouns, are prominent predictors of subjectivity scores. Thus, our findings show that the predictions of the regressor, and therefore native speakers' perceptions of subjectivity, align with subjectivity theory. However, an additional comparison of the effects of different subjectivity features in author text and the text of cited sources reveals that the distinction between author and source subjectivity might not be as salient for naïve speakers as it is in the theory.

Subjectivity Theory vs. Speaker Intuitions: Explaining the Results of a Subjectivity Regressor Trained on Native Speaker Judgements

While Sentiment Analysis has become increasingly central in computational approaches to literary texts, the literary domain still poses important challenges for the detection of textual sentiment due to its highly complex use of language and devices - from subtle humor to poetic imagery. Furthermore these challenges are only further amplified in low-resource language and domain settings. 
In this paper we investigate the application and efficacy of different Sentiment Analysis tools on Danish literary texts, using historical fairy tales and religious hymns as our datasets. The scarcity of linguistic resources for Danish and the historical context of the data further compounds the challenges for the tools. 
We compare human annotations to the continuous valence scores of both transformer- and dictionary-based Sentiment Analysis methods to assess their performance, seeking to understand how distinct methods handle the language of Danish prose and poetry.

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