India

Due to the rapid growth of social media platforms, these tools have become essential for monitoring information during ongoing disaster events. However, extracting valuable insights requires real-time processing of vast amounts of data. A major challenge in existing systems is their exposure to event-related biases, which negatively affects their ability to generalize to emerging events. While recent advancements in debiasing and causal learning offer promising solutions, they remain underexplored in the disaster event domain. In this work, we approach bias mitigation through a causal lens and propose a method to reduce event- and domain-related biases, enhancing generalization to future events. Our approach outperforms multiple baselines by up to +1.9% F1 and significantly improves a PLM-based classifier across three disaster classification tasks.

IJCNLP-AACL 2025

Generalizing to Unseen Disaster Events: A Causal View

crisis informatics

debiasing

causality

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!

To gain access to this event page you are required to register. Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

poster

### 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.

International enterprises, organizations, and hospitals collect large amounts of multi-modal data stored in databases, text documents, images, and videos. While there has been recent progress in the separate fields of multi-modal data exploration as well as in database systems that automatically translate natural language questions to database query languages, the research challenge of querying both structured databases and unstructured modalities (e.g., texts, images) in natural language remains largely unexplored.
In this paper, we propose M$^2$EX, a system that enables multi-modal data exploration via language agents. Our approach is based on the following research contributions: (1) Our system is inspired by a real-world use case that enables users to explore multi-modal information systems. (2) M$^2$EX leverages an LLM-based agentic AI framework to decompose a natural language question into subtasks such as text-to-SQL generation and image analysis and to orchestrate modality-specific experts in an efficient query plan. (3) Experimental results on multi-modal datasets, encompassing relational data, text, and images, demonstrate that our system outperforms state-of-the-art multi-modal exploration systems, excelling in both accuracy and various performance metrics, including query latency, API costs, and planning efficiency, thanks to the more effective utilization of the reasoning capabilities of LLMs.

Multi-Modal Data Exploration via Language Agents

Generation of Artificial Intelligence (AI) texts in important works has become a common practice that can be used to misuse and abuse AI at various levels. Traditional AI detectors often rely on document-level classification, which struggles to identify AI content in hybrid or slightly edited texts designed to avoid detection, leading to concerns about the model's efficiency, which makes it hard to distinguish between human-written and AI-generated texts. A sentence-level sequence labeling model proposed to detect transitions between human- and AI-generated text, leveraging nuanced linguistic signals overlooked by document-level classifiers. By this method, detecting and segmenting AI and human-written text within a single document at the token-level granularity is achieved. Our model combines the state-of-the-art pre-trained Transformer models, incorporating Neural Networks (NN) and Conditional Random Fields (CRFs). This approach extends the power of transformers to extract semantic and syntactic patterns, and the neural network component to capture enhanced sequence-level representations, thereby improving the boundary predictions by the CRF layer, which enhances sequence recognition and further identification of the partition between Human- and AI-generated texts. The evaluation is performed on two publicly available benchmark datasets containing collaborative human and AI-generated texts. Our experimental comparisons are with zero-shot detectors and the existing state-of-the-art models, along with rigorous ablation studies to justify that this approach, in particular, can accurately detect the spans of AI texts in a completely collaborative text.

Fine-Grained Detection of AI-Generated Text Using Sentence-Level Segmentation

Large language models are increasingly adapted to downstream tasks through fine-tuning. Full supervised fine-tuning (SFT) and parameter-efficient fine-tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), are two dominant approaches. While PEFT methods are widely used for their computational efficiency, the implications of their configurations (e.g., rank) remain under-explored in downstream Q&A tasks and generalisation. In this work, we perform a comprehensive evaluation across multiple reasoning and recall datasets, conducting a rank sweep to quantify the trade-off between SFT and PEFT. We also compare the accuracy of PEFT and SFT models across in-domain and out-of-domain adaptation, highlighting distinct generalisation behaviour and task-specific forgetting. We demonstrate that LoRA achieves competitive and in some cases superior performance compared to SFT, particularly on reasoning tasks at specific rank values. Additionally, we analyze the internal representations via spectral features and layer-wise attention structures, offering insights into representational drift and structural changes in attention patterns.

How Much is Too Much? Exploring LoRA Rank Trade-offs for Retaining Knowledge and Domain Robustness

Multimodal sarcasm detection is challenging, especially in low-resource settings where subtle image-text contradictions are hard to learn due to scarce annotated data, which hinders the model's performance. Parameter-efficient fine-tuning (PEFT) methods like adapters, LoRA, and prompt tuning reduce overfitting but struggle to reach optimal performance due to limited supervision from few-shot data. We propose 
PEKD, a unified framework that enhances PEFT methods via distillation from an expert model trained on large-scale sarcasm data, which acts as the teacher. To mitigate unreliable signals from the teacher, we introduce an entropy-aware gating mechanism that dynamically adjusts the distillation strength based on teacher confidence. Experiments on two public datasets demonstrate that our PEKD framework enables PEFT methods to outperform both prior parameter-efficient approaches and large multimodal models, achieving strong results in the few-shot scenario. The framework is modular and adaptable to a wide range of multimodal models and tasks.

Teaching Sarcasm: Few-Shot Multimodal Sarcasm Detection via Distillation to a Parameter-Efficient Student

Large language models (LLMs) power modern AI applications, but processing sensitive data on untrusted servers raises privacy concerns. Homomorphic encryption (HE) enables computation on encrypted data for secure inference. However, neural text generation requires decoding methods like argmax and sampling, which are non-polynomial and thus computationally expensive under encryption, creating a significant performance bottleneck. We introduce cutmax, an HE-friendly argmax algorithm that reduces ciphertext operations compared to prior methods, enabling practical greedy decoding under encryption. We also propose the first HE-compatible nucleus (top-p) sampling method, leveraging cutmax for efficient stochastic decoding with provable privacy guarantees. Both techniques are polynomial, supporting efficient inference in privacy-preserving settings. Moreover, their differentiability facilitates gradient-based sequence-level optimization as a polynomial alternative to straight-through estimators. We further provide strong theoretical guarantees for cutmax, proving its convergence via exponential amplification of the gap ratio between the maximum and runner-up elements. Evaluations on realistic LLM outputs show latency reductions of $24\times$–$35\times$ over baselines, advancing secure text generation.

Efficient Decoding Methods for Language Models on Encrypted Data

The spread of fake news during the COVID-19 pandemic era triggered widespread chaos and confusion globally, causing public panic and misdirected health behavior. Automated fact checking in non-English languages is challenging due to the low availability of trusted resources. There are several prior work that attempted automated fact checking in multilingual settings.  However, most of them fine-tune pre-trained language models (PLMs) and only produce veracity prediction without providing explanations. The absence of explanatory reasoning in these models reduces the credibility of their predictions.  This paper proposes a multi-agent explainable cross-lingual fake news detection method that leverages credible English evidence and Large Language Models (LLMs) to verify and generate explanations for non-English claims, overcoming the scarcity of non-English evidence. The experimental results show that the proposed method performs well across three non-English written multilingual COVID-19 datasets in terms of veracity predictions and explanations. Our source code is available online. (https://github.com/bassamtiano/crosslingual_efnd)

Multi-Agent Cross-Lingual Veracity Assessment for Explainable Fake News Detection

Evaluating Theory of Mind (ToM) in Large Language Models (LLMs) is an important area of research for understanding the social intelligence of AI. Recent ToM benchmarks have made significant strides in enhancing the complexity, comprehensiveness, and practicality of evaluation. However, while the focus has been on constructing "more difficult'' or "more comprehensive'' tasks, there has been insufficient systematic analysis of the structural factors that inherently determine the difficulty of ToM reasoning—that is, "what'' makes reasoning difficult. To address this challenge, we propose a new dataset generation framework for ToM evaluation named AnaToM. To realize an "Anatomy of Difficulty'' in ToM reasoning, AnaToM strictly controls structural parameters such as the number of entities and the timeline in a story. This parameter control enables the isolation and identification of factors affecting the ToM of LLMs, allowing for a more precise examination of their reasoning mechanisms. The proposed framework provides a systematic methodology for diagnosing the limits of LLM reasoning abilities and offers new guidelines for future benchmark design.

AnaToM: A Dataset Generation Framework for Evaluating Theory of Mind Reasoning Toward the Anatomy of Difficulty through Structurally Controlled Story Generation

The discipline of physics stands as a cornerstone of human intellect, driving the evolution of technology and deepening our understanding of the fundamental principles of the cosmos. Contemporary literature includes some works centered on the task of solving physics problems—a crucial domain of natural language reasoning. In this paper, we evaluate the performance of frontier LLMs in solving physics problems, both mathematical and descriptive. We also employ a plethora of inference-time techniques and agentic frameworks to improve the performance of the models. This includes the verification of proposed solutions in a cumulative fashion by other, smaller LLM agents, and we perform a comparative analysis of the performance that the techniques entail. There are significant improvements when the multi-agent framework is applied to problems that the models initially perform poorly on. Furthermore, we introduce a new evaluation benchmark for physics problems, PhysicsEval, consisting of 19,609 problems sourced from various physics textbooks and their corresponding correct solutions scraped from physics forums and educational websites. Our code and data are publicly available at https://github.com/areebuzair/PhysicsEval.

PhysicsEval: Inference-Time Techniques to Improve the Reasoning Proficiency of Large Language Models on Physics Problems

Causal reasoning is central to language understanding, yet remains under-resourced in Bangla. In this paper, we introduce the first large-scale dataset for causal inference in Bangla, consisting of over 11663 sentences annotated for causal sentence types (explicit, implicit, non-causal) and token-level spans for causes, effects, and connectives. The dataset captures both simple and complex causal structures across diverse domains such as news, education, and health. We further benchmark a suite of state-of-the-art instruction-tuned large language models, including LLaMA 3.3 70B, Gemma 2 9B, Qwen 32B, and DeepSeek, under zero-shot and three-shot prompting conditions. Our analysis reveals that while LLMs demonstrate moderate success in explicit causality detection, their performance drops significantly on implicit and span-level extraction tasks. This work establishes a foundational resource for Bangla causal understanding and highlights key challenges in adapting multilingual LLMs for structured reasoning in low-resource languages.

Benchmarking Bangla Causality: A Dataset of Implicit and Explicit Causal Sentences and Cause-Effect Relations

In the legal domain, Fact-based Judgment Prediction and Explanation (FJPE) aims to predict judicial outcomes and generate grounded explanations using only factual information, mirroring early-phase legal reasoning. Motivated by the overwhelming case backlog in the Indian judiciary, we introduce TathyaNyaya, the first large-scale, expert-annotated dataset for FJPE in the Indian context. Covering judgments from the Supreme Court and multiple High Courts, the dataset comprises four complementary components, NyayaFacts, NyayaScrape, NyayaSimplify, and NyayaFilter, that facilitate diverse factual modeling strategies. Alongside, we present FactLegalLlama, an instruction-tuned LLaMa-3-8B model fine-tuned to generate faithful, fact-grounded explanations. While FactLegalLlama trails transformer baselines in raw prediction accuracy, it excels in generating interpretable explanations, as validated by both automatic metrics and legal expert evaluation. Our findings show that fact-only inputs and preprocessing techniques like text simplification and fact filtering can improve both interpretability and predictive performance. Together, TathyaNyaya and FactLegalLlama establish a robust foundation for realistic, transparent, and trustworthy AI applications in the Indian legal system.

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