India

Model collapse—a phenomenon where models degrade in performance due to indiscriminate use of synthetic data—is well studied. However, its role in bias amplification—the progressive reinforcement of pre-existing social biases in Large Language Models (LLMs)—remains underexplored. In this paper, we formally define the conditions for bias amplification and demonstrate through statistical simulations that bias can intensify even in the absence of sampling errors, the primary driver of model collapse. Empirically, we investigate political bias amplification in GPT-2 using a custom-built benchmark for sentence continuation tasks. Our findings reveal a progressively increasing right-leaning bias. Furthermore, we evaluate three mitigation strategies—Overfitting, Preservation, and Accumulation—and show that bias amplification persists even when model collapse is mitigated. Finally, a mechanistic interpretation identifies distinct sets of neurons responsible for model collapse and bias amplification, suggesting they arise from different underlying mechanisms.

IJCNLP-AACL 2025

Bias Amplification: Large Language Models as Increasingly Biased Media

keywords: bias amplification

model collapse

fairness

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
The opportunity to present at NAACL should not depend on a researcher’s travel budget, or their family status. This is why it is so important to make virtual participation at NAACL as good an experience as possible – but we want to also provide a good experience for in-person participants. As a community, we are still working out the best way to do that. This year at NAACL, we are trying out a big virtual poster session ahead of the conference, with the hope that this will make make for a lively and interactive experience. At the same time, we are reducing virtual oral presentations, which seem to be particularly tricky to make to work well. A big thanks to the NAACL program chairs and to Luciana Benotti for all their ideas and work to improve the virtual experience. And participants, virtual as well as in-person: Please let us know what worked for you and what didn’t, so we can continue to improve hybrid conferences. 
 
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Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

technical paper

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
General Chair, IJCNLP-AACL 2025* 
Read full message and download the Conference Handbook [**here**](https://drive.google.com/file/d/1UTwxkAqSqI-GAoJC3wE1zZt5VP1Y8GX0/view?usp=sharing).

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

Large Language Models (LLMs) demonstrate strong reasoning and self-correction abilities in high-resource languages like English, but their performance remains limited in low-resource languages such as Korean. In this study, we investigate whether reinforcement learning (RL) can enhance Korean reasoning abilities to a degree comparable to English. Our findings reveal that RL alone yields limited improvements when applied to models lacking inherent Korean reasoning capabilities. To address this, we explore several fine-tuning strategies and show that aligning the model’s internal reasoning processes with Korean inputs—particularly by tuning Korean-specific neurons in early layers—is key to unlocking RL's effectiveness. We introduce a self-correction code-switching dataset to facilitate this alignment and observe significant performance gains in both mathematical reasoning and self-correction tasks. Ultimately, we conclude that the crucial factor in multilingual reasoning enhancement is not injecting new linguistic knowledge, but effectively eliciting and aligning existing reasoning capabilities. Our study provides a new perspective on how internal translation and neuron-level tuning contribute to multilingual reasoning alignment in LLMs.

Do LLMs Need Inherent Reasoning Before Reinforcement Learning? A Study in Korean Self-Correction

Emotions are a fundamental facet of human experience, varying across individuals, cultural contexts, and nationalities. Given the recent success of Large Language Models (LLMs) as role-playing agents, we examine whether LLMs exhibit emotional stereotypes when assigned nationality-specific personas. Specifically, we investigate how different countries are represented in pre-trained LLMs through emotion attributions and whether these attributions align with cultural norms. To provide a deeper interpretive lens, we incorporate four key cultural dimensions, namely Power Distance, Uncertainty Avoidance, Long-Term Orientation, and Individualism, derived from Hofstede’s cross-cultural framework. Our analysis reveals significant nationality-based differences, with emotions such as shame, fear, and joy being disproportionately assigned across regions. Furthermore, we observe notable misalignment between LLM-generated and human emotional responses, particularly for negative emotions, highlighting the presence of reductive and potentially biased stereotypes in LLM outputs.

From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models

LLM-as-a-Judge has emerged as a promising alternative to human evaluators across various tasks, yet inherent biases—particularly position bias, the tendency to favor solutions based on their position within the prompt—compromise its reliability. This exploratory study evaluates position bias in LLM judges across pairwise and list-wise comparison settings, introducing three metrics: repetition stability, position consistency, and preference fairness. Our experiments, involving 15 LLM judges across MTBench and DevBench with 22 tasks and approximately 40 solution-generating models, result in over 150,000 evaluation instances. We identify Judge-Level, Candidate-Level, and Task-Level factors contributing to bias. The findings confirm that position bias is not due to random chance and varies significantly across judges and tasks. While position bias is weakly influenced by the length of prompt components, it is strongly affected by the quality gap between solutions. Our agreement and disagreement analysis among judges further provides insights into the distribution of judging difficulty across the dataset, and highlights the potential for dataset modifications.

Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge

The Political Compass Test (PCT) and similar surveys are commonly used to assess political bias in auto-regressive LLMs. Our rigorous statistical experiments show that while changes to standard generation parameters have minimal effect on PCT scores, prompt phrasing and fine-tuning individually and together can significantly influence results. Interestingly, fine-tuning on politically rich vs. neutral datasets does not lead to different shifts in scores. We also generalize these findings to a similar popular test called 8 Values. Humans do not change their responses to questions when prompted differently ("answer this question" vs "state your opinion"), or after exposure to politically neutral text, such as mathematical formulae. But the fact that the models do so raises concerns about the validity of these tests for measuring model bias, and paves the way for deeper exploration into how political and social views are encoded in LLMs.

A Detailed Factor Analysis for the Political Compass Test: Navigating Ideologies of Large Language Models

Effectively learning language patterns that provoke empathetic expression is vital to creating emotionally intelligent technologies; however, this problem has historically been overlooked. We address this gap by proposing the new task of empathy cause identification: a challenging task aimed at pinpointing specific triggers prompting empathetic responses in communicative settings. We correspondingly introduce AcnEmpathize-Cause, a novel dataset consisting of 4K cause-identified sentences, and explore various models to evaluate and demonstrate the dataset's efficacy. This research not only contributes to the understanding of empathy in textual communication but also paves the way for the development of AI systems capable of more nuanced and supportive interactions.

Unveiling Empathic Triggers in Online Interactions via Empathy Cause Identification

Lipograms are a unique form of constrained writing where all occurrences of a particular letter are excluded from the text, typified by the novel Gadsby (Wright, 1939), which daringly avoids all usage of the letter 'e'. In this study, we explore the power of modern large language models (LLMs) by transforming the novel The Great Gatsby (Fitzgerald, 1925) into a fully 'e'-less text. We experimented with a range of techniques, from baseline methods like synonym replacement to sophisticated generative models enhanced with beam search and named entity analysis. We show that excluding up to 3.6% of the most common letters (up to the letter 'u') had minimal impact on the text’s meaning, although translation fidelity rapidly and predictably decays with stronger lipogram constraints. Our work highlights the surprising flexibility of English under strict constraints.

Gatsby without the ‘E’: Creating Lipograms with LLMs

By augmenting Large Language Models (LLMs) with external tools, their capacity to solve complex problems has been significantly enhanced. However, despite ongoing advancements in the parsing capabilities of LLMs, incorporating all available tools simultaneously in the prompt remains impractical due to the vast number of external tools. Consequently, it is essential to provide LLMs with a precise set of tools tailored to the specific task, considering both quantity and quality. Current tool retrieval methods primarily focus on refining the ranking list of tools and directly packaging a fixed number of top-ranked tools as the tool set. However, these approaches often fail to equip LLMs with the optimal set of tools prior to execution, since the optimal number of tools for different tasks could be different, resulting in inefficiencies such as redundant or unsuitable tools, which impede immediate access to the most relevant tools. This paper addresses the challenge of recommending precise toolsets for LLMs. We introduce the problem of tool recommendation, define its scope, and propose a novel Precision-driven Tool Recommendation (PTR) approach. PTR captures an initial, concise set of tools by leveraging historical tool bundle usage and dynamically adjusts the tool set by performing tool matching, culminating in a multi-view-based tool addition. Additionally, we present a new dataset, RecTools, and a metric, TRACC, designed to evaluate the effectiveness of tool recommendation for LLMs. We further validate our design choices through comprehensive experiments, demonstrating promising accuracy across two open benchmarks and our RecTools dataset.

Task-Aligned Tool Recommendation for Large Language Models

Large language models can rephrase and restructure natural language effectively, but their potential for reformulating graph encodings remains underexplored despite the significant impact of encoding choices on performance.
In this work, we introduce ReGraph, a reinforcement learning-based approach that guides language models to reformulate graph encodings for improved task alignment.
We demonstrate that reformulating graph encodings enhances reasoning and yields consistent performance gains on graph question answering tasks.
Our results show that language models often prefer specific graph encodings, even if they are suboptimal for the task at hand.

ReGraph: Learning to Reformulate Graph Encodings with Large Language Models

Four billion people cannot access vital information or be heard simply because of the language they speak—a human rights crisis where technology advances have left speakers of most of the world’s 7,000+ languages digitally invisible. This talk explores why languages matter in the digital space generally and in Language AI specifically, through real-world examples from CLEAR Global’s work across health, education, and agriculture sectors. We showcase practical tools developed to inform decisions (Language Use Data Platform), collect quality-controlled voice data (TWB Voice), and demonstrate how the NLP community can actively use these resources to address critical gaps in language technology. Drawing from projects spanning crisis response, educational equity, and access to agricultural information, we illustrate the transformative impact of language-inclusive technology. From conversational AI chatbots serving COVID-19 information in Lingala, Hausa, and Kanuri, to exploring EdTech solutions for mother tongue education in the Marma community in Bangladesh, to evaluating how synthetic voice data can be used for African ASR at lower costs than traditional data collection costs—our work demonstrates that marginalized languages can achieve competitive NLP performance with appropriate data and community engagement. Throughout our work, CLEAR Global’s 100,000+ volunteer linguist community provides the foundation for quality-controlled translations, data collection, cultural validation, and ensuring that technology development remains grounded in the needs and expertise of native speakers. This talk invites the NLP community to collaborate in ensuring that the right to information and to be heard doesn’t depend on which language you speak.

Bridging the Digital Language Divide: Building NLP Solutions for the Four Billion Left Behind

Recent advances in large language models (LLMs) have revolutionized how humans and AI systems work, learn, and interact, creating new opportunities for collaboration while also raising new challenges. In this talk, we explore the evolving landscape of human–AI collaboration from three perspectives. The first part focuses on teaming by rethinking human–AI collaboration through a large-scale audit of the future of work, highlighting mismatches between what humans want and current AI capabilities. We then talk about how we can create general user modeling from computer use to support robust memory modeling and proactive AI assistance during collaboration. The last part discusses how to evaluate human–AI collaboration, moving away from exams toward studying how people work with LLMs on diverse tasks. Overall, this talk demonstrates how to develop AI systems that are not just tools, but meaningful collaborators working alongside us, helping us grow, and adapting to who we are.

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Do LLMs Need Inherent Reasoning Before Reinforcement Learning? A Study in Korean Self-Correction

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