India

In this paper, we address the persistent challenges that figurative language expressions pose for natural language processing (NLP) systems, particularly in low-resource languages such as Konkani. We present a hybrid model that integrates a pre-trained Multilingual BERT (mBERT) with a bidirectional LSTM and a linear classifier. This architecture is fine-tuned on a newly introduced annotated dataset for metaphor classification, developed as part of this work. To improve the model’s efficiency, we implement a gradient-based attention head pruning strategy. For metaphor classification, the pruned model achieves an accuracy of 78%. We also applied our pruning approach to expand on an existing idiom classification task, achieving 83% accuracy. These results demonstrate the effectiveness of attention head pruning for building efficient NLP tools in underrepresented languages.

IJCNLP-AACL 2025

Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT

metaphor classification

attention head pruning

low-resource language

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

Self-supervised speech models have demonstrated the ability to learn rich acoustic representations. However, interpreting which specific phonological or acoustic features these models leverage within their highly polysemantic activations remains challenging. In this paper, we propose a straightforward and unsupervised probing method for model interpretability. We extract the activations from the final MLP layer of a pretrained HuBERT model and train a sparse autoencoder (SAE) using dictionary learning techniques to generate an over-complete set of latent representations. Analyzing these latent codes, we observe that a small subset of high-variance units consistently aligns with phonetic events, suggesting their potential utility as interpretable acoustic detectors. Our proposed method does not require labeled data beyond raw audio, providing a lightweight and accessible tool to gain insights into the internal workings of self-supervised speech models.

Interpretable Sparse Features for Probing Self-Supervised Speech Models

Transformer based models, specially large language models (LLMs) dominate the field of NLP with their mass adoption in tasks such as text generation, summarization and fake news detection. These models offer ease of deployment and reliability for most applications, however, they require significant amounts of computational power for training as well as inference. This poses challenges in their adoption in resource-constrained applications, specially in the open-source community where compute availability is usually scarce. This work proposes a graph-based approach for Environmental Claim Detection, exploring Graph Neural Networks (GNNs) and Hyperbolic Graph Neural Networks (HGNNs) as lightweight yet effective alternatives to transformer-based models. Re-framing the task as a graph classification problem, we transform claim sentences into dependency parsing graphs, utilizing a combination of word2vec \& learnable part-of-speech (POS) tag embeddings for the node features and encoding syntactic dependencies in the edge relations. Our results show that our graph-based models, particularly HGNNs in the poincaré space (P-HGNNs), achieve performance superior to the state-of-the-art on environmental claim detection while using up to **30x fewer parameters**. We also demonstrate that HGNNs benefit vastly from explicitly modeling data in hierarchical (tree-like) structures, enabling them to significantly improve over their euclidean counterparts.

Efficient Environmental Claim Detection with Hyperbolic Graph Neural Networks

Continual learning (CL) presents a significant challenge for large pre-trained models, primarily due to catastrophic forgetting and the high computational cost of sequential knowledge updating. Parameter-Efficient Transfer Learning (PETL) methods offer reduced computational burdens but often struggle to effectively mitigate forgetting. This paper introduces Stacked Low-Rank Adaptation (SLoRA), a novel parameter-efficient approach that leverages the additive composition of task-specific, frozen low-rank adapters to enable modular continual learning with inherent support for explicit knowledge modification. SLoRA was evaluated on vision benchmarks, BERT-base, and the 1-billion-parameter Llama-3.2-1B model. Experiments demonstrated that SLoRA almost completely eliminated catastrophic forgetting, achieving a final average accuracy of 92.75\% on Llama-3.2-1B while perfectly preserving prior task performance. Furthermore, SLoRA is computationally efficient, enabling up to a 15x training speed-up over full fine-tuning with 99.7\% fewer trainable parameters per update. SLoRA offers a compelling balance of forgetting mitigation, parameter efficiency, and modularity, representing a promising direction for developing adaptable and efficient lifelong knowledgeable foundation models.

Stacked LoRA: Isolated Low-Rank Adaptation for Lifelong Knowledge Management

Reliable evaluation of Question Answering (QA) systems in low-resource Indic languages presents a significant challenge due to limited annotated datasets, linguistic diversity, and suitable evaluation metrics. Languages such as Sindhi, Manipuri, Dogri, Konkani, and Maithili are particularly underrepresented, creating difficulty in assessing Large Language Models (LLMs) on QA tasks. Existing metrics, including BLEU, ROUGE-L, and BERTScore, are effective in machine translation and high-resource settings; however, they often fail in low-resource QA due to score compression, zero-inflation, and poor scale alignment. To overcome this, LRMGS (Language-Robust Metric for Generative QA) is introduced to capture semantic and lexical agreement while preserving the score scale across languages. LRMGS is evaluated across 8 Indic languages and multiple LLMs, demonstrating consistently higher concordance with reference-based chrF++ scores, measured using the Concordance Correlation Coefficient (CCC). Experimental results indicate that LRMGS provides more accurate discrimination of system performance in very low-resource languages compared to existing metrics. This work establishes a robust and interpretable framework for evaluating QA systems in low-resource Indic languages, supporting more reliable multilingual model assessment.

LRMGS: A Language-Robust Metric for Evaluating Question Answering in Very Low-Resource Indic Languages

Aspect-based summarization aims to generate summaries that highlight specific aspects of a text, enabling more personalized and targeted summaries. However, its application to books remains unexplored due to the difficulty of constructing reference summaries for long text. To address this challenge, we propose BookAsSumQA, a QA-based evaluation framework for aspect-based book summarization. BookAsSumQA automatically generates aspect-specific QA pairs from a narrative knowledge graph to evaluate summary quality based on its question-answering performance. Our experiments using BookAsSumQA revealed that while LLM-based approaches showed higher accuracy on shorter texts, RAG-based methods become more effective as document length increases, making them more efficient and practical for aspect-based book summarization.

BookAsSumQA: An Evaluation Framework for Aspect-Based Book Summarization via Question Answering

Large language models (LLMs) excel across diverse natural language processing tasks but remain opaque and unreliable. This thesis investigates how LLM reasoning can be made both interpretable and reliable through systematic analysis of internal dynamics and targeted interventions. Unlike prior work that examines reasoning broadly, this research focuses on two representative domains: puzzle solving, where reasoning steps can be precisely tracked, and ontological inference, where hierarchical structures constrain valid reasoning. The central questions are: (1) How can systematic error patterns in domain specific reasoning be detected through layer wise probing and mitigated through targeted interventions? (2) How can probing frameworks and middle layer analyses reveal and enhance the computational mechanisms underlying inference? By combining probing methods, middle layer investigations, and probe guided interventions, the work aims to uncover interpretable reasoning patterns, identify systematic failure modes, and develop adaptive enhancement strategies. The expected outcome is a domain grounded framework that advances both theoretical understanding of neural reasoning and the design of practical, trustworthy AI systems.

Thesis Proposal: Interpretable Reasoning Enhancement in Large Language Models through Puzzle and Ontological Task Analysis

Inference-time computation is a critical yet challenging paradigm for enhancing the reasoning performance of large language models (LLMs). While existing strategies improve reasoning stability and consistency, they suffer from notable limitations: self-correction often reinforces the model's initial biases, and Multi-Agent Collaboration (MAC) often fails due to the lack of efficient coordination mechanisms, leading to collective errors. Although high-performing verifiers can detect reasoning errors, making them reliable requires substantial training. To address these challenges, we introduce a novel inference-time framework - **Adaptive Coopetition (AdCo)** - in which LLM agents utilize **an adaptive, UCB-based 'coopetition' mechanism**. At each round, agents leverage coarse verifier signals to determine whether to collaborate or compete, further iteratively refining their reasoning based on peer feedback. Without relying on high-performance verifiers, our adaptive strategy achieves significant performance gains on mathematical reasoning benchmarks, yielding **a 20\% relative improvement** over baselines on the more challenging dataset. Our approach remains robust and consistent in terms of accuracy under different sample sizes and configurations. This adaptive, signal-guided 'coopetition' framework enhances reasoning robustness by leveraging both
model knowledge diversity and reasoning trace measure, while also promoting uncertainty-driven exploration, especially when participants have comparable capabilities. From this perspective, our work offers a fresh lens on inference-time computation and paves the way for more resilient multi-agent LLM systems.

Adaptive Coopetition: Leveraging Coarse Verifier Signals for Resilient Multi-Agent LLM Reasoning

We investigate whether Large Language Models (LLMs) exhibit human-like cognitive patterns under four established frameworks from
psychology: Thematic Apperception Test (TAT), Framing Bias, Moral Foundations Theory (MFT), and Cognitive Dissonance. We evaluated several proprietary and open-source models using structured prompts and automated scoring. Our findings reveal that these models often produce coherent narratives, show susceptibility to positive framing, exhibit moral judgments aligned with Liberty/Oppression concerns, and demonstrate self-contradictions tempered by extensive rationalization. Such behaviors mirror human cognitive tendencies yet
are shaped by their training data and alignment methods. We discuss the implications for AI transparency, ethical deployment, and future
work that bridges cognitive psychology and AI safety.

AI Through the Human Lens: Investigating Cognitive Theories in Machine Psychology

Developing effective healthcare dialog systems requires controlling conversations to offer clear insight into the system’s understanding and to address the lack of patient-oriented conversational datasets. Moreover, evaluating these systems is equally challenging and requires user studies for robust evaluation. These challenges are even more pronounced when addressing the needs of minority populations with low health literacy and numeracy. This thesis proposal focuses on designing conversational architectures that deliver self-care information to African American patients with heart failure.

Neuro-symbolic approaches provide a promising direction by integrating symbolic reasoning with the generative capabilities of Large Language Models (LLMs). In this proposal, we explore various approaches to creating a hybrid dialog model by combining the strengths of task-oriented dialog systems with the integration of neuro-symbolic rules into a Language Model (LM)/LLM-based dialog system, thereby controlling the dialog system. We propose a hybrid conversational system that uses schema graphs to control the flow of dialogue, while leveraging LLMs to generate responses grounded in these schemas. We will also conduct a user study to evaluate the system's effectiveness.

Thesis Proposal: A NeuroSymbolic Approach to Control Task-Oriented Dialog Systems

Improving the performance of neural machine translation for low-resource languages is challenging due to the limited availability of parallel corpora. However, recently available Large Language Models (LLM) have demonstrated superior performance in various natural language processing tasks, including translation. In this work, we propose to incorporate an LLM into a Machine Translation (MT) model as a prior distribution to leverage its translation capabilities. The LLM acts as a teacher, instructing the student MT model about the target language. We conducted an experiment in four language pairs: English ⇔ German and English ⇔ Hindi. This resulted in improved BLEU and COMET scores in a low-resource setting.

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Interpretable Sparse Features for Probing Self-Supervised Speech Models

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