India

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.

IJCNLP-AACL 2025

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

question-answering

evaluation framework

summarization

### 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).

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

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.

Enriching the Low-Resource Neural Machine Translation with Large Language Model

Large language models have the potential to generate explanations for their own predictions in a variety of styles based on user instructions. Recent research has examined whether these self-explanations faithfully reflect the models' actual behavior and has found that they often lack faithfulness. However, the question of how to improve faithfulness remains underexplored. Moreover, because different explanation styles have superficially distinct characteristics, it is unclear whether improvements observed in one style also arise when using other styles. This study analyzes the effects of training for faithful self-explanations and the extent to which these effects generalize, using three classification tasks and three explanation styles. We construct one-word constrained explanations that are likely to be faithful using a feature attribution method, and use these pseudo-faithful self-explanations for continual learning on instruction-tuned models. Our experiments demonstrate that training can improve self-explanation faithfulness across all classification tasks and explanation styles, and that these improvements also show signs of generalization to the multi-word settings and to unseen tasks. Furthermore, we find consistent cross-style generalization among three styles, suggesting that training may contribute to a broader improvement in faithful self-explanation ability.

Investigating Training and Generalization in Faithful Self-Explanations of Large Language Models

While Large Language Models (LLMs) have shown remarkable performance in various Natural Language Processing (NLP) tasks, their effectiveness seem to be heavily biased toward high-resource languages. This proposal aims to address this gap by developing efficient training strategies for low-resource languages. We propose various techniques for efficient learning in simluated low-resource settings for English. We then plan to adapt these methods for low-resource languages. We plan to experiment with both natural language generation and understanding models. We evaluate the models on similar benchmarks as the BabyLM challenge for English. For other languages, we plan to use treebanks and translation techniques to create our own silver test set to evaluate the low-resource LMs.

Thesis Proposal: Efficient Methods for Natural Language Generation/Understanding Systems

Most models struggle with deception detection in diplomatic conversations due to extreme class imbalance and a lack of "social awareness" in how speakers and listeners are represented. To our end, we present CORAL (COntextual Representation and Asymmetric Learning), a model that learns socially-aware representations—capturing both speaker behavioral traits and their power differentials (social hierarchy)—and couples them with a principled learning framework designed to handle deception’s rarity. CORAL constructs embeddings that capture two key social signals: stable speaker traits through learned signatures and dynamic interaction contexts through power differentials. These embeddings form the input to a classifier trained with a Positive-Unlabeled (PU) learning objective, designed for the asymmetric nature of deception (rare lies versus abundant, unlabeled truths). By synergizing representation and learning, CORAL achieves a state-of-the-art Macro F1-score of 0.65 on the highly imbalanced Peskov Diplomacy dataset, surpassing models that tackle these challenges in isolation. Our findings highlight that advancing socially complex NLP tasks requires aligning richer representations with objectives tailored to data asymmetry.

CORAL: A Synergistic Approach to Deception Detection with Socially-Aware Representations and Positive-Unlabeled Learning

The relationship between tokenizer algorithm (e.g., Byte-Pair Encoding (BPE), Unigram), morphological alignment, tokenization quality (e.g., compression efficiency), and downstream performance remains largely unclear, particularly for languages with complex morphology. In this paper, we conduct a comprehensive evaluation of tokenizers using small-sized BERT models---from pre-training through fine-tuning---for Telugu (agglutinative), along with preliminary evaluation in Hindi (primarily fusional with some agglutination) and English (fusional). To evaluate morphological alignment of tokenizers in Telugu, we create a dataset containing gold morpheme segmentations of 600 derivational and 7000 inflectional word forms.

Our experiments reveal two key findings for Telugu. First, the choice of tokenizer algorithm is the most significant factor influencing performance, with Unigram-based tokenizers consistently outperforming BPE across most settings. Second, while better morphological alignment shows a moderate, positive correlation with performance on text classification and structure prediction tasks, its impact is secondary to the tokenizer algorithm. Notably, hybrid approaches that use morphological information for pre-segmentation significantly boost the performance of BPE, though not Unigram. Our results further showcase the need for comprehensive intrinsic evaluation metrics for tokenizers that could explain downstream performance trends consistently.

Rethinking Tokenization for Rich Morphology: The Dominance of Unigram over BPE and Morphological Alignment

Semantic role labeling (SRL) is a fundamental task in natural language processing that is crucial for achieving deep semantic understanding. Despite the success of large language models (LLMs) in several downstream NLP tasks, key tasks such as SRL remain a challenge for LLMs. Hence, in this study, we attempt to instantiate the efficacy of LLMs for the task of SRL via Question answering. Toward that goal, we investigate the effectiveness of five different LLMs (Llama, Mistral, Qwen, OpenChat, Gemini) using zero-shot and few-shot prompting. Our findings indicate that few-shot prompting enhances the performance of all models. Although Gemini outperformed others by a margin of 11%, Qwen and Llama are not too far behind. Additionally, we conduct a comprehensive error analysis to shed light on the cases where LLMs fail. This study offers valuable insights into the performance of LLMs for structured prediction and the effectiveness of simple prompting techniques in the Question-Answering framework for SRL.

Next from IJCNLP-AACL 2025

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

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