United States

Social biases based on regional identity (or regional bias) are often observed in Indian contexts on major online social networks and require critical attention. However, due to large linguistic and cultural diversity, high annotation costs, and inherent human biases, very little annotated data exists on regional biases in the Indian context. Recently, Large Language Models (LLMs) have garnered attention for the automatic annotation of text. However, such annotation efforts are largely limited to English texts, and LLMs often perform poorly when applied to low-resource languages. Therefore, this paper focuses on understanding the capabilities and challenges of popular open-source LLMs in annotating Indian regional biases. We utilize the recently proposed IndRegBias dataset, which consists of Indian regionally biased social media comments in both English and code-mixed formats.
First, we assess the annotation capabilities of LLMs in a zero-shot setting and critically analyze their performance across different writing styles, including code-mixing, transliteration, and English. We find that the majority of LLMs exhibit low agreement with human annotations (measured using Cohen’s kappa). Consequently, we extend our study by fine-tuning the models using 50% of the data and evaluating them on the remaining 50%. We observe a significant improvement in annotation agreement (kappa) for all the LLMs. To further assess the capabilities of the fine-tuned models, we evaluate them on 500 newly collected social media comments discussing regional issues in India. The results show that most fine-tuned LLMs outperform their zero-shot counterparts when annotating these new comments.

ACL 2026

Annotating Indian Regional Biases using Large Language Models: Evaluation and Analysis

indian regional bias

large language models

natural language processing

zero-shot

fine-tuning

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
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NAACL 2024

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

workshop paper

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Updating bilingual dictionary entries is a tedious, time-consuming, and highly subjective task, especially when a new sense in the source language requires identifying an appropriate translation equivalent. To date, there have been no attempts to automatize the discovery of new bilingual sense entries. Related tasks such as Word-level Bilingual Dictionary Induction and cross-lingual embedding alignment do not account for polysemy and are not applied to lexicographic data.
In contrast to their monolingual counterparts, bilingual dictionaries fall short in terms of completeness, detail with respect to examples and glosses, and diachronic information.
We introduce a novel NLP task, Sense-Level Bilingual Dictionary Induction (\textsc{SenseBDI}), at the intersection of lexical semantics, cross-lingual, and diachronic NLP.
We construct a dataset of time-stamped sense-level bilingual dictionary entries by aligning two bilingual dictionaries, two monolingual dictionaries, and the multilingual resource BabelNet, thereby enriching bilingual entries with monolingual source-language information.
We propose a baseline based on nearest-neighbor search over cross-lingual embeddings of glosses and usages. We find that usages contribute more strongly than glosses, with substantial variation across language pairs and discuss task-specific challenges with regards to target language polysemy and future directions such as transfer to real-world scenarios.

Towards Sense-level Bilingual Dictionary Induction

Suicide ideation detection models are typically evaluated using aggregate performance metrics, yet little is known about how they internally represent psychologically meaningful risk factors. In high-stakes mental health applications, understanding these internal representations is essential for safety, transparency, and responsible deployment. In this work, we move beyond accuracy and analyze how suicide detection models trained on original and topic-augmented datasets encode psychological risk factors in their internal representation space. Using visualization and geometric analysis, we examine the coherence and separability of topic-related features. Our results show that topic-aware augmentation increases the clarity and distinctness of underrepresented psychosocial risk factors such as immigration, family issues, and financial crisis. These findings suggest that augmentation not only improves model performance but also leads to more structured and interpretable internal representations.

Beyond Accuracy: Interpreting Topic Representation in Suicide Ideation Detection Models

Stance detection identifies whether a text expresses support, opposition, or neutrality toward a target and is central to applications such as political analysis and misinformation monitoring. With the shift toward large language models (LLMs), stance classification increasingly relies on prompting and lightweight adaptation. Yet the generalization behavior of open-source LLMs across new targets and domains remains uneven. We conduct a large-scale diagnostic study of four open-source LLMs (3B–24B parameters), examining how model size, prompting strategies, and Low-Rank Adaptation (LoRA) interact across in-target, cross-target, and cross-domain settings. Across 912 experiments, three patterns emerge: (1) larger models improve prompting-based in-target performance, but this advantage diminishes after fine-tuning; (2) LoRA boosts in-target accuracy yet often harms cross-context transfer; (3) optimal prompting depends on model size. These results reveal a consistent tension between specialization and generalization, offering practical guidance for configuring LLM-based stance detection under transfer.

Diagnosing Generalization in Open-Source LLMs for Stance Detection

News headlines and summaries shape how events are interpreted through selective emphasis and omission, a phenomenon commonly referred to as framing. Large language models are now routinely used to generate such content, yet existing evaluation frameworks largely overlook this dimension. We introduce Frame In, Frame Out (FIFO), the first large-scale benchmark for measuring framing presence in LLM-generated news summaries, grounded in the widely used XSum dataset. FIFO combines 15,499 jury-annotated examples with 320 expert-labeled instances ($\kappa = 0.61$) to validate and calibrate model-based annotations. Using FIFO, we analyze measured framing rates across 27 summarization models. We find that LLM-generated summaries often exhibit higher calibrated framing rates than human-written references, with substantial variation across topics and training regimes, including elevated rates in scientific and public health summaries. Our results establish framing as an underexplored and consequential dimension of summarization quality.

Frame In, Frame Out: Measuring Framing Bias in LLM-Generated News Summaries

We disentangle multilingual sentence embeddings into language-dependent and language-agnostic components, leveraging the latter to improve cross-lingual similarity estimation.
Previous studies on this approach have trained disentanglers by combining intra-component constraints, which either align or disalign language-dependent embeddings or language-agnostic embeddings, with inter-component constraints across both embeddings.
However, when and how these constraints are effective remains unclear.
Our experiments on sentence similarity estimation and machine translation quality estimation revealed that while intra-component constraints and the combination of both constraints are effective for encoder-based multilingual sentence embeddings, inter-component constraints are effective for decoder-based ones.
Furthermore, our detailed analysis revealed distinct roles: intra-component constraints improve uniformity within the embedding space, while inter-component constraints enhance cross-lingual alignment between parallel sentences.

Mitigating Language Bias in Multilingual Sentence Embeddings for Cross-Lingual Similarity Estimation

Masked language modeling has become a standard pretraining objective for training encoder-based language models. In this approach, certain tokens in the input are masked, and the model learns to predict them using the surrounding context. This process enables the model to capture both syntactic and semantic properties of language. Conventionally, the tokens selected for masking are chosen at random, which may not always yield the most effective learning signals. In this work, we examine a token masking strategy based on entropy distribution. We use the model’s entropy over token predictions to identify which tokens should be masked. This method aims to target tokens that are more informative and uncertain to improve the training efficacy. We also propose a novel self-masking approach that enhances training efficiency without relying on an external reference model. Experimental results demonstrate that our method achieves an average performance improvement of 5% in GLUE scores compared to the baseline. Further, we experiment with combining knowledge distillation with entropy masking, resulting in the best overall results.

Entropy-aware Masking for Masked Language Modeling

In recent years, several Speech Language Models (SLMs) that represent speech and written text jointly have been presented. The question then emerges about how model-internal mechanisms are similar and different when operating in the two modalities. We focus on how these systems encode, store, and retrieve factual knowledge, which has previously been investigated for text-only models. To investigate mechanisms behind the storage and recall of factual association in SLMs, we leverage Causal Mediation Analysis, a technique previously applied to text-based models.
Initial results using SpiritLM, a multimodal model integrating discrete speech tokens reveal discrepancies between text-to-text and speech-to-text results, suggesting that the emergent mechanisms for factual recall are only partially carried over from the text to the speech modality. These results advance our understanding of how internal mechanisms encode factual associations in SLMs while contributing insights for improving speech-enabled AI systems.

Do Factual Recall Mechanisms Carry over from Text to Speech in Multimodal Language Models?

Creating effective dialogue systems for mental health support requires high-quality multi-turn counseling dialogue data, yet collecting real counselor-client conversations presents significant challenges, including privacy concerns, high costs, and limited scalability. We present \textbf{Interactive Agents}, a novel framework that simulates naturalistic counseling dialogues through controlled LLM-to-LLM interactions. The framework introduces two key innovations: (1) a personalized client agent that maintains consistent psychological characteristics throughout a session, and (2) a counselor agent that implements a theoretically grounded three-stage therapeutic model comprising the exploration, insight, and action phases. Through rigorous evaluation using both automatic metrics and professional-counselor assessments based on the Working Alliance Inventory, we demonstrate that our framework generates therapeutically valid dialogues that are comparable in quality to human-generated sessions. Models fine-tuned on our proposed synthetic dataset (SimPsyDial) achieve state-of-the-art performance in a standard pairwise chatbot-arena evaluation of LLM-based counselors. Our framework provides a scalable, privacy-preserving method for generating high-quality counseling dialogue data while maintaining professional therapeutic standards.

Interactive Agents: Simulating Counselor-Client Psychological Counseling via Role-Playing LLM-to-LLM Interactions

While zero-shot instructional prompts like "Let's think step-by-step'' have revolutionized Large Language Model performance, we lack systematic understanding of why: which specific words drive their effectiveness, and how do these patterns vary across tasks and models? We introduce the ZIP score (Zero-shot Importance of Perturbation), a metric that quantifies individual word importance through controlled, semantically meaningful perturbations. To enable rigorous evaluation, we also introduce the first ground-truth benchmark for prompt interpretability, a set of validation prompts with predetermined keywords where ZIP achieves 95.8% accuracy compared to 65.8% for LIME. Analyzing six flagship models across seven prompts and multiple task domains, we find that word importance is task-dependent ("step-by-step'' dominates mathematical reasoning; "think'' matters more for common-sense tasks), varies systematically across model families, and correlates inversely with model performance, suggesting prompts have greatest impact on tasks where models struggle. Our findings advance prompt science, providing both practical guidance for prompt engineering and theoretical understanding of how instructional language shapes model behavior.

ZIP: Quantifying Which Words Matter in Zero-Shot Instructional Prompts

Language-model (LM) surprisal is widely used as a proxy for contextual predictability and has been reported to correlate with metaphor novelty judgments. However, surprisal is tightly intertwined with lexical frequency. We explore this interaction on metaphor novelty ratings using two different word frequency measures. We analyse surprisal estimates from eight Pythia model sizes and 154 training checkpoints. Across settings, word frequency is a stronger predictor of metaphor novelty than surprisal. Across training stages, the surprisal--novelty association peaks at an early stage and then falls again, mirroring a similarly timed increase in the surprisal--frequency association. These results suggest that the often-reported optimal LM surprisal settings may incorrectly associate contextual predictability with metaphor novelty and processing difficulty, whereas lexical frequency may be the major underlying factor.

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