India

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 &quot;more difficult&#39;&#39; or &quot;more comprehensive&#39;&#39; tasks, there has been insufficient systematic analysis of the structural factors that inherently determine the difficulty of ToM reasoning—that is, &quot;what&#39;&#39; makes reasoning difficult. To address this challenge, we propose a new dataset generation framework for ToM evaluation named AnaToM. To realize an &quot;Anatomy of Difficulty&#39;&#39; 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.

IJCNLP-AACL 2025

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

dataset generation

theory of mind

large language models

story generation

evaluation

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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.

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.

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.

TathyaNyaya and FactLegalLlama: Advancing Factual Judgment Prediction and Explanation in the Indian Legal Context

Open-domain Information Extraction (IE) plays an essential role in constructing large-scale knowledge bases and supports downstream applications such as Question Answering, Text Summarization, etc. While most prior research in IE has centered around extracting categorical relational tuples (e.g., president of, located in), the extraction of numerical relations (e.g., literacy rate, area, molecular weight), that link quantitative mentions to corresponding entities, remains relatively underexplored. This work addresses this gap by targeting the extraction of open-domain numeric assertions, which require identifying both the relevant entity and the appropriate measuring attribute associated with a quantity in natural language text. We begin by refining an existing OpenIE system through a rule-based approach where retrieving implicit measuring attributes for a quantity mention becomes the main challenge. To overcome this, we propose a neural framework that jointly identifies the relevant entity for a numeric mention and infers the measuring attribute to relate them, using contextual cues in the sentence. Experimental evaluation shows that our proposed model outperforms the baseline and a general-purpose large language model with a significantly large margin.

Extracting Numeric Assertions from Text

Synthetic reviews mislead users and erode trust in online marketplaces, and the advent of Large Language Models (LLMs) makes detecting such AI-generated content increasingly challenging due to their human-like fluency and coherence. In the literature, LLM-generated review detection datasets are limited to one or a few domains, with reviews generated by only a few LLMs. Consequently, datasets are limited in diversity in terms of both domain coverage and review generation styles. Models trained on such datasets generalize poorly, lacking cross-model adaptation and struggling to detect diverse LLM-generated reviews in real-world, open-domain scenarios. To address this, we introduce DetectAIRev, a benchmark dataset for AI-generated review detection that includes human-written reviews from diverse domains and AI-generated reviews generated by various categories of LLMs. We evaluate the quality and reliability of the proposed dataset through several ablation studies and human evaluations. Furthermore, we propose an AI-generated text detection method ProtoFewRoBERTa, a few-shot framework that combines prototypical networks with RoBERTa embeddings, which learn discriminative features across multiple LLMs and human-written text using only a few labeled examples per class to discriminate between LLMs as the author for text author detection. We conduct our experiments on the DetectAIRev and a publicly available benchmark dataset. Our experimental results suggest that our proposed methods outperform the state-of-the-art baseline models in detecting AI-generated reviews and text detection.

Can You Really Trust That Review? ProtoFewRoBERTa and DetectAIRev: A Prototypical Few-Shot Method and Multi-Domain Benchmark for Detecting AI-Generated Reviews

Contemporary vision-language models (VLMs) perform well on existing multimodal reasoning benchmarks (78-85\% accuracy on MMMU, MathVista). Yet, these results fail to sufficiently distinguish true scientific reasoning articulation capabilities from pattern-matching. To address this gap, we introduce \textbf{mmJEE-Eval}, a multimodal bilingual (English and Hindi) benchmark comprising 1,460 questions from India's JEE Advanced examination (2019-2025) spanning pre-college Physics, Chemistry, and Mathematics domains. Our evaluation of 17 state-of-the-art models reveals that while frontier VLMs (GPT-5, Gemini 2.5 Pro/Flash) achieve 77-84\% accuracy on held-out 2025 questions, open-source models plateau at 37-45\% despite scaling to 400B parameters, a significant difference not observed on existing benchmarks. While closed frontiers from Google and OpenAI show high problem-solving accuracies (up to 100\% pass@3 scores), they fully collapse when the reasoning load is increased meta-cognitively (GPT-5 fixes just 5.2\% errors). Systematic ablations show mmJEE-Eval's difficulty stems from complexity and reasoning depth rather than memorization. Effectively, our benchmark segregates superior training and reasoning methodologies where alternatives fail. We publicly release our code and data: https://mmjee-eval.github.io

mmJEE-Eval: A Bilingual Multimodal Benchmark for Evaluating Scientific Reasoning in Vision-Language Models

Socio-demographic prompting (SDP) - prompting Large Language Models (LLMs) using demographic proxies to generate culturally aligned outputs - often shows LLM responses as stereotypical and biased. While effective in assessing LLMs' cultural competency, SDP is prone to confounding factors such as prompt sensitivity, decoding parameters, and the inherent difficulty of generation over discrimination tasks due to larger output spaces. These factors complicate interpretation, making it difficult to determine if the poor performance is due to bias or the task design. To address this, we use inverse socio-demographic prompting (ISDP), where we prompt LLMs to discriminate and predict the demographic proxy from actual and simulated user behavior from different users. We use the Goodreads-CSI dataset (Saha et al., 2025), which captures difficulty in understanding English book reviews for users from India, Mexico, and the USA, and test four LLMs: Aya-23, Gemma-2, GPT-4o, and LLaMA-3.1 with ISDP. Results show that models perform better with actual behaviors than simulated ones, contrary to what SDP suggests. However, performance with both behavior types diminishes and becomes nearly equal at the individual level, indicating limits to personalization.

To Generate or Discriminate? Methodological Considerations for Measuring Cultural Alignment in LLMs

Understanding how large language models (LLMs) reason across semantically distinct domains remains an open challenge. In this work, we investigate whether LLMs can connect personality traits to musical preferences, specifically chord progressions. Drawing on psychological theory and symbolic music structure, we introduce a novel benchmark that evaluates two interdependent tasks: (1) inferring personality traits from a textual context and (2) selecting a musically appropriate chord progression aligned with the inferred trait. We release a synthetic, expert-guided dataset grounded in Cattell's 16 Personality Factors (PF16), genre-conditioned chord structures, and diverse situational contexts. We explore multiple learning strategies, including fine-tuning task-specific corpora, model merging with LoRA adapters, and advanced prompt-based reasoning techniques such as verbalization. Additionally, we propose a teacher-student framework to evaluate the quality of model-generated explanations using a five-dimensional rubric. Our findings show that verbalization outperforms standard reasoning methods, achieving up to 11% improvement over zero-shot baselines.

Harmonious Minds: Benchmarking Intertwined Reasoning of Human Personality and Musical Preference

Multiple Choice Question (MCQ) answering is a widely used method for evaluating the performance of Large Language Models (LLMs). However, LLMs often exhibit selection bias in MCQ tasks, where their choices are influenced by factors like answer position or option symbols rather than the content. This bias undermines the reliability of MCQ as an evaluation framework. Most existing selection bias metrics require answer labels and measure divergences between prediction and answer distributions, but do not fully capture the consistency of a model’s predictions across different orderings of answer choices. Existing selection bias mitigation strategies have notable limitations: majority voting, though effective, is computationally prohibitive; calibration-based methods require validation sets and often fail to generalise across datasets. To address these gaps, we propose three key contributions: (1) a new unsupervised label-free \textbf{Permutation Bias Metric (PBM)} that directly quantifies inconsistencies in model predictions across answer permutations, providing a more precise measure of selection bias, (2) an efficient majority voting approach called Batch Question-Context KV caching (BaQCKV), to significantly reduce computational costs while preserving bias mitigation effectiveness, and (3) an unsupervised Low-Rank Adaptation (LoRA)-1 fine-tuning strategy based on our proposed metric and the BaQCKV that mitigates selection bias, providing a computationally efficient alternative that maintains model generalizability. Experiments across multiple MCQ benchmarks demonstrate that our approaches reduce bias, increasing consistency in accuracy while minimizing computational costs.

Quantifying and Mitigating Selection Bias in LLMs: A Transferable LoRA Fine-Tuning and Efficient Majority Voting Approach

Large language models (LLMs), despite their remarkable text generation capabilities, often hallucinate and generate text that is factually incorrect and not grounded in real-world knowledge. This poses serious risks in domains like healthcare, finance, and customer support. A typical way to use LLMs is via the APIs provided by LLM vendors where there is no access to model weights or options to fine-tune the model. Existing methods to detect hallucinations in such settings where the model access is restricted or constrained by resources typically require making multiple LLM API calls, increasing latency and API cost. We introduce CONFACTCHECK, an efficient hallucination detection approach that does not leverage any external knowledge base and works on the simple intuition that responses to factual probes within the generated text should be consistent within a single LLM and across different LLMs. Rigorous empirical evaluation on multiple datasets that cover both the generation of factual texts and the open generation shows that CONFACTCHECK can detect hallucinated facts efficiently using fewer resources and achieves higher accuracy scores compared to existing baselines that operate under similar conditions. Our code is available here.

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

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