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Humans attempt to understand the real world by asking the fundamental question &#39;&#39;Why?&#39;&#39; when faced with incomprehensible situations in everyday life. Such why-questions provide essential knowledge that can help in understanding these situations. In this study, we conducted an end-to-end process to verify the utility of consecutive why-questions, from constructing a large language model (LLM)-based dataset to performing quantitative evaluation and analysis. Firstly, we created a WHY-Chain dataset, consisting of answers generated by an LLM in response to chain-of-why-questions, including a validity check. We also incorporated objectives that effectively capture the &#39;&#39;consecutive&#39;&#39; characteristic of the data. Using the WHY-Chain dataset and two types of self-supervised objectives, we trained the pre-trained model. As a result, the refined model demonstrated improved performance on downstream tasks that require commonsense reasoning. Additionally, we conducted various ablation studies to assess the impact of different factors, confirming the scalability of the proposed approach. Lastly, we confirmed the consistency of the logical information by reasoning chain analysis of the answers generated from consecutive why-questions.

NAACL 2025

From Curiosity to Clarity : Exploring the Impact of Consecutive Why-Questions

sentence-level objective

span-level objective

why-chain

consecutive why

commonsense reasoning

**Welcome to ACL-IJCNLP 2021!**

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

Humans attempt to understand the real world by asking the fundamental question ''Why?'' when faced with incomprehensible situations in everyday life. Such why-questions provide essential knowledge that can help in understanding these situations. In this study, we conducted an end-to-end process to verify the utility of consecutive why-questions, from constructing a large language model (LLM)-based dataset to performing quantitative evaluation and analysis. Firstly, we created a WHY-Chain dataset, consisting of answers generated by an LLM in response to chain-of-why-questions, including a validity check. We also incorporated objectives that effectively capture the ''consecutive'' characteristic of the data. Using the WHY-Chain dataset and two types of self-supervised objectives, we trained the pre-trained model. As a result, the refined model demonstrated improved performance on downstream tasks that require commonsense reasoning. Additionally, we conducted various ablation studies to assess the impact of different factors, confirming the scalability of the proposed approach. Lastly, we confirmed the consistency of the logical information by reasoning chain analysis of the answers generated from consecutive why-questions.

poster

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

In this paper, we introduce a learning analytics framework to analyze the in-context learning (ICL) behavior of large language models (LLMs) through the lens of the Zone of Proximal Development (ZPD), an established theory in educational psychology. ZPD delineates the range of tasks a learner can accomplish with appropriate guidance but not yet independently. We adapt this concept to ICL, measuring the ZPD of LLMs based on model performance on individual examples in different settings. Furthermore, we propose an item response theory (IRT) model to predict the distribution of zones for LLMs. Our findings reveal a series of intricate and multifaceted behaviors of ICL, providing new insights into understanding and leveraging this technique. Finally, we demonstrate how our framework can enhance LLM in both inference and fine-tuning scenarios: (1) By predicting a model’s zone distribution, we selectively apply ICL to queries that are most likely to benefit from demonstrations, achieving a better balance between inference cost and performance; (2) We propose a human-like curriculum for fine-tuning, which prioritizes examples within the model’s ZPD. The curriculum results in improved performance, and we explain its effectiveness through an analysis of the training dynamics of LLMs.

Investigating the Zone of Proximal Development of Language Models for In-Context Learning

Large language models (LLMs) have shown promise in representing individuals and communities, offering new ways to study complex social dynamics. However, effectively aligning LLMs with specific human groups and systematically assessing the fidelity of the alignment remains a challenge. This paper presents a robust framework for aligning LLMs with online communities via instruction-tuning and comprehensively evaluating alignment across various aspects of language, including authenticity, emotional tone, toxicity, and harm. We demonstrate the utility of our approach by applying it to online communities centered on dieting and body image. We administer an eating disorder psychometric test to the aligned LLMs to reveal unhealthy beliefs and successfully differentiate communities with varying levels of eating disorder risk. Our results highlight the potential of LLMs in automated moderation and broader applications in public health and social science research.

Improving and Assessing the Fidelity of Large Language Models Alignment to Online Communities

Images taken out of their context are the most prevalent form of multimodal misinformation. Debunking them requires (1) providing the true context of the image and (2) checking the veracity of the image's caption. However, existing automated fact-checking methods fail to tackle both objectives explicitly. In this work, we introduce COVE, a new method that predicts first the true COntext of the image and then uses it to predict the VEracity of the caption. COVE beats the SOTA context prediction model on all context items, often by more than five percentage points. It is competitive with the best veracity prediction models on synthetic data and outperforms them on real-world data, showing that it is beneficial to combine the two tasks sequentially. Finally, we conduct a human study that reveals that the predicted context is a reusable and interpretable artifact to verify new out-of-context captions for the same image. Our code and data are made available.

COVE: COntext and VEracity prediction for out-of-context images

Chiasmus, a debated literary device in Biblical texts, has captivated mystics while sparking ongoing scholarly discussion. In this paper, we introduce the first computational approach to systematically detect chiasmus within Biblical passages. Our method leverages neural embeddings to capture lexical and semantic patterns associated with chiasmus, applied at multiple levels of textual granularity (half-verses, verses). We also involve expert annotators to review a subset of the detected patterns. Despite its computational efficiency, our method achieves robust results, with high inter-annotator agreement and system accuracy of 0.80 at the verse level and 0.60 at the half-verse level. We further provide a qualitative analysis of the distribution of detected chiasmi, along with selected examples that highlight the effectiveness of our approach.

Computational Discovery of Chiasmus in Ancient Religious Text

State-run Chinese newspapers are believed to strategically select and frame news articles to align with the shifting political tides of the country. This paper describes methods to quantify these changes in content and framing over time. Looking at more than 50 years of articles from the People's Daily and Reference News, we analyze differences in name mentions and sentiment in news articles for politicians before and after their deaths, as well as during and not during certain political events. We find significant estimates of difference, reflecting the changes in various aspects of the political environment in China during different time periods. We also apply change point detection methods to identify turning points in time series data of name mentions and sentiment. The identified turning points show a high co-occurrence with crucial political events and deaths of politicians. Furthermore, we utilize topic modeling to analyze the framing choices for articles written in different decades. The changes in frequent topic words are more significant in People's Daily than in Reference News, which is consistent with the focus shifts of the Chinese central government in history. Finally, by using pre-trained language models to predict masked names in news articles, we analyze the distinctiveness of the language used to report individuals.

Through the Lens of History: Methods for Analyzing Temporal Variation in Content and Framing of State-run Chinese Newspapers

The spread of harmful content online is a dynamic issue evolving over time. Existing detection models, reliant on static data, are becoming less effective and generalizable. Developing new models requires sufficient up-to-date data, which is challenging. A potential solution is to combine existing datasets with minimal new data. However, detection tasks vary—some focus on hate speech, offensive, or abusive con- tent, which differ in the intent to harm, while others focus on identifying targets of harmful speech such as racism, sexism, etc.—raising the challenge of handling nuanced class differences. To address these issues, we introduce a novel transfer learning method that leverages class-specific knowledge to enhance harmful content detection. In our approach, we first present label-specific soft prompt tuning, which captures and represents class-level information. Secondly, we propose two approaches to transfer this fine-grained knowledge from source (existing tasks) to target (unseen and new tasks): initializing the target task prompts from source prompts and using an attention mechanism that learns and adjusts attention scores to utilize the most relevant information from source prompts. Experiments demonstrate significant improvements in harmful content detection across English and German datasets, highlighting the effectiveness of label-specific representations and knowledge transfer.

Fine-Grained Transfer Learning for Harmful Content Detection through Label-Specific Soft Prompt Tuning

Email is a vital conduit for human communication across businesses, organizations, and broader societal contexts. In this study, we aim to model the intents, expectations, and responsiveness in email exchanges. To this end, we release SIZZLER, a new dataset containing 1800 emails annotated with nuanced types of intents and expectations. We benchmark models ranging from feature-based logistic regression to zero-shot prompting of large language models. Leveraging the predictive model for intent, expectations, and 14 other features, we analyze 11.3M emails from GMANE to study how linguistic and social factors influence the conversational dynamics in email exchanges. Through our causal analysis, we find that the email response rates are influenced by social status, argumentation, and in certain limited contexts, the strength of social connection.

Causally Modeling the Linguistic and Social Factors that Predict Email Response

Large language models (LLMs) struggle in social science domains, where critical thinking and human-level inference are crucial. In this work, we propose a multi-agent social reasoning framework that leverages the generative and reasoning capabilities of LLMs to generate and evaluate reasons from multiple perspectives grounded in social science theories, and construct a factor graph for inference. Experimental results on understanding power dynamics in conversations show that our method outperforms standard prompting baselines, demonstrating its potential for tackling hard Computational Social Science (CSS) tasks.

Identifying Power Relations in Conversations using Multi-Agent Social Reasoning

This study empirically tests the Narrative Economics hypothesis, which posits that narratives (ideas that are spread virally and affect public beliefs) can influence economic fluctuations. We introduce two curated datasets containing posts from X (formerly Twitter) which capture economy-related narratives. Employing Natural Language Processing (NLP) methods, we extract and summarize narratives from
the tweets. We test their predictive power for macroeconomic forecasting by incorporating the tweets’ or the extracted narratives’ representations in downstream financial prediction tasks. Our work highlights the challenges in improving macroeconomic models with narrative data, paving the way for the research
community to realistically address this important challenge. From a scientific perspective, our investigation offers valuable insights and NLP tools for narrative extraction and summarization using Large Language Models (LLMs), contributing to future research on the role of narratives in economics.1

Can LLMs Learn Macroeconomic Narratives from Social Media?

Large Language Models (LLMs) have been recently adopted in interactive systems requiring communication. As the false belief in a model can harm the usability of such systems, LLMs should not have cognitive biases that humans have. Psychologists especially focus on the False Consensus Effect (FCE), a cognitive bias where individuals overestimate the extent to which others share their beliefs or behaviors, because FCE can distract smooth communication by posing false beliefs. However, previous studies have less examined FCE in LLMs thoroughly, which needs more consideration of confounding biases, general situations, and prompt changes. Therefore, in this paper, we conduct two studies to examine the FCE phenomenon in LLMs. In Study 1, we investigate whether LLMs have FCE. In Study 2, we explore how various prompting styles affect the demonstration of FCE. As a result of these studies, we identified that popular LLMs have FCE. Also, the result specifies the conditions when FCE becomes more or less prevalent compared to normal usage.

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Investigating the Zone of Proximal Development of Language Models for In-Context Learning

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TellMeWhy: A Dataset for Answering Why-Questions in Narratives

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