AACL 2025 论文列表

Large Language Models (LLMs) are increasingly used as daily recommendation systems for tasks like education planning, yet their recommendations risk perpetuating societal biases. This paper empirically examines geographic, demographic, and economic biases in university and program suggestions from three open-source LLMs: LLaMA-3.1-8B, Gemma-7B, and Mistral-7B. Using 360 simulated user profiles varying by gender, nationality, and economic status, we analyze over 25,000 recommendations. Results show strong biases: institutions in the Global North are disproportionately favored, recommendations often reinforce gender stereotypes, and institutional repetition is prevalent. While LLaMA-3.1 achieves the highest diversity, recommending 481 unique universities across 58 countries, systemic disparities persist. To quantify these issues, we propose a novel, multi-dimensional evaluation framework that goes beyond accuracy by measuring demographic and geographic representation. Our findings highlight the urgent need for bias consideration in educational LMs to ensure equitable global access to higher education.

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 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

Reasoning over procedural sequences, where the order of steps directly impacts outcomes, is a critical capability for large language models (LLMs). In this work, we study the task of reconstructing globally ordered sequences from shuffled procedural steps, using a curated dataset of food recipes, a domain where correct sequencing is essential for task success. We evaluate several LLMs under zero-shot and few-shot settings and present a comprehensive evaluation framework that adapts established metrics from ranking and sequence alignment. These include Kendall’s Tau, Normalized Longest Common Subsequence (NLCS), and Normalized Edit Distance (NED), which capture complementary aspects of ordering quality. Our analysis shows that model performance declines with increasing sequence length, reflecting the added complexity of longer procedures. We also find that greater step displacement in the input, corresponding to more severe shuffling, leads to further degradation. These findings highlight the limitations of current LLMs in procedural reasoning, especially with longer and more disordered inputs.

Simultaneous speech translation (SiST) requires balancing translation quality and latency. While most SiST systems follow machine translation assumptions that prioritize full semantic accuracy to the source, human interpreters often omit less critical content to catch up with the speaker. This study investigates whether omission can be used to reduce latency while preserving meaning in SiST.We construct a dataset that includes omission using large language models (LLMs) and propose a Target-Duration Latency (TDL), target-based latency metric that measures the output length considering the start and end timing of translation. Our analysis shows that LLMs can omit less important words while retaining the core meaning. Furthermore, experimental results show that although standard metrics overlook the benefit of the model trained with proposed omission-involving dataset, alternative evaluation methods capture it, as omission leads to shorter outputs with acceptable quality.

The rapid progress of generative AI (Gen-AI) and large language models (LLMs) offers significant potential for geospatial applications, but simultaneously introduces critical privacy, security, and ethical risks. Existing general-purpose AI safety frameworks inadequately cover GeoAI-specific risks such as geolocation privacy violations and re-identification, with False Safe Rates exceeding 40% in some models. To address this, we present \texttt{GeoSAFE} (Geospatial Safety Assurance Framework and Evaluation), introducing the first GeoAI-specific safety taxonomy with six hazard categories and a multimodal \texttt{GeoSAFE-Dataset}. It includes 11694 textual prompts with explanations, augmented by real-world queries and images to reduce synthetic bias and reflect operational use. We benchmark model performance on detecting \texttt{unsafe} geospatial queries. Additionally, we present \texttt{GeoSAFEGuard}, an instruction-tuned LLM achieving 4.6% False Safe Rate, 0.4% False Unsafe Rate, and 97% F1-score on text-to-text evaluation of \texttt{GeoSAFE-Dataset}. An anonymous user-survey confirms human-\texttt{GeoSAFE} alignment emphasizing the urgent need for domain-specific safety evaluations as general-purpose LLMs fail to detect unsafe location-powered queries.

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)

Social networks extensively feature memes, particularly cartoon images, as a prevalent form of communication often conveying complex sentiments or harmful content. Detecting such content, particularly when it involves Bengali and English text, remains a multimodal challenge. This paper introduces CMBan, a novel and culturally relevant dataset of 2,641 annotated cartoon memes. It addresses meme classification based on their sentiment across five key categories: Humor, Sarcasm, Offensiveness, Motivational Content, and Overall Sentiment, incorporating both image and text features. Our curated dataset specifically aids in detecting nuanced offensive content and navigating complexities of pure Bengali, English, or code-mixed Bengali-English languages. Through rigorous experimentation involving over 12 multimodal models, including monolingual, multilingual, and proprietary architectures, and utilizing prompting methods like Chain-Of-Thought (CoT), findings suggest this cartoon-based, code-mixed meme content poses substantial understanding challenges. Experimental results demonstrate that closed models excel over open models. While the LoRA fine-tuning strategy equalizes performance across model architectures and improves classification of challenging aspects in multilingual meme contexts, this work advances meme classification by providing effective solution for detecting harmful content in multilingual meme contexts.

Despite significant progress in large language models (LLMs), their knowledge and evaluation continue to be centered around high-resource languages, leaving critical gaps in low-resource settings. This raises questions about how effectively LLMs handle subjects that require locally relevant knowledge. To address this challenge, we need a robust dataset that reflects the knowledge of underrepresented regions such as Bangladesh. In this paper, we present SOMAJGYAAN, a Bangla multiple-choice dataset consisting of 4,234 questions, annotated across five levels of difficulty. The questions are drawn from Bangladesh’s National Curriculum and Global Studies textbooks, covering a wide range of domains including History, Geography, Economics, Social Studies, Politics and Law, and Miscellaneous topics. Difficulty levels were assigned by four expert annotators to minimize annotation bias. The experiments reveal that closed-source LLMs perform better than open-source LLMs. While fine-tuning open-source models on improves their performance, they still fall short of matching closed-source LLMs. Our findings highlight the importance of culturally grounded evaluation datasets and task-specific adaptation to improve LLM performance in low-resource language settings.

To explore a more scalable path for adding multimodal capabilities to existing LLMs, this paper addresses a fundamental question: Can a unimodal LLM, relying solely on text, reason about its own informational needs and provide effective feedback to optimize a multimodal model? To answer this, we propose a method that enables a language agent to give feedback to a vision-language model (VLM) to adapt text generation to the agent’s preferences. Our results from different experiments affirm this hypothesis, showing that LLM preference feedback significantly enhances VLM descriptions. Using our proposed method, we find that the VLM can supply multimodal scene descriptions to help the LLM better understand multimodal context, leading to improvements of maximum 13% in absolute accuracy compared to the baseline multimodal approach. Furthermore, a human study validated our AI-driven feedback, showing a 64.6% preference alignment rate between the LLM’s choices and human judgments. Extensive experiments provide insights on how and why the method works and its limitations.

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.

Vision-Language Models (VLMs) have demonstrated impressive capabilities across a range of tasks, yet concerns about their potential biases persist. This work investigates the cultural biases in state-of-the-art VLMs by evaluating their performance on an image-based country identification task at the country level. Utilizing the geographically diverse Country211 (CITATION) dataset, we probe VLMs via open-ended questions, multiple-choice questions (MCQs), and include challenging multilingual and adversarial task settings. Our analysis aims to uncover disparities in model accuracy across different countries and question formats, providing insights into how training data distribution and evaluation methodologies may influence cultural biases in VLMs. The findings highlight significant variations in performance, suggesting that while VLMs possess considerable visual understanding, they inherit biases from their pre-training data and scale, which impact their ability to generalize uniformly across diverse global contexts.

Natural Language to SQL (NL2SQL) has seen significant advancements with large language models (LLMs). However, these models often depend on closed-source methods and high computational resources, posing challenges in data privacy and deployment. In contrast, small language models (SLMs) struggle with NL2SQL tasks, exhibiting poor performance and incompatibility with existing frameworks. To address these issues, we introduce \textbf{Feather-SQL}, a new lightweight framework tailored for SLMs. Feather-SQL improves SQL executability and accuracy through: (i) schema pruning and linking, (ii) multi-path and multi-candidate generation. Additionally, we introduce \textbf{1+1 Model Collaboration Paradigm}, which pairs a strong general-purpose chat model with a fine-tuned SQL model, combining strong analytical reasoning with high-precision SQL generation. Experimental results on BIRD demonstrate that Feather-SQL improves NL2SQL performance on SLMs, with around 10% boost for models without fine-tuning. The proposed paradigm raises the accuracy ceiling of SLMs to 54.76%, highlighting its effectiveness.

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.

Social norms are implicit, culturally grounded expectations that guide interpersonal communication. Unlike factual commonsense, norm reasoning is subjective, context-dependent, and varies across cultures—posing challenges for computational models. Prior works provide valuable normative annotations but mostly target isolated utterances or synthetic dialogues, limiting their ability to capture the fluid, multi-turn nature of real-world conversations. In this work, we present Norm-RAG, a retrieval-augmented, agentic framework for nuanced social norm inference in multi-turn dialogues. Norm-RAG models utterance-level attributes including communicative intent, speaker roles, interpersonal framing, and linguistic cues and grounds them in structured normative documentation retrieved via a novel Semantic Chunking approach. This enables interpretable and context-aware reasoning about norm adherence and violation across multilingual dialogues. We further introduce MINDS (Multilingual Interactions with Norm-Driven Speech), a bilingual dataset comprising 31 multi-turn Mandarin-English and Spanish-English conversations. Each turn is annotated for norm category and adherence status using multi-annotator consensus, reflecting cross-cultural and realistic norm expression. Our experiments show that Norm-RAG improves norm detection and generalization, demonstrates improved performance for culturally adaptive and socially intelligent dialogue systems.

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 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.

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.

We introduce PHLoRA (Pronounced “flora”) (Post-hoc LoRA), a simple yet powerful method to extract low-rank adaptation adapters from full-rank fine-tuned models without requiring access to training data or gradients. By computing the low-rank decomposition of weight differences between a base model and its fine-tuned counterpart, our method reconstructs adapter modules that can be merged or dynamically routed at inference time via S-LoRA, AdapterFusion, or served in scalable, industry settings using platforms like NVIDIA NIM.This approach amortizes latency overhead across requests and yields substantial cost savings. Unlike prior work that trains each adapter explicitly, our approach decouples fine-tuning from adapter generation, allowing adapter extraction from existing full-rank models or third-party checkpoints. Experiments on text, image, and video benchmarks using the Amazon Nova model family demonstrate that extracted adapters preserve high energy from the full weight delta, can be pruned safely, and yield negligible degradation in downstream task performance when re-merged. Overall, PHLoRA provides a practical path for making all existing full-rank checkpoints adapter-ready, democratizing scalable inference for all models.

Multimodal models play a key role in empathy detection, but their performance can suffer when modalities provide conflicting cues. To understand these failures, we examine cases where unimodal and multimodal predictions diverge. Using fine-tuned models for text, audio, and video, along with a gated fusion model, we find that such disagreements often reflect underlying ambiguity, as evidenced by annotator uncertainty. Our analysis shows that dominant signals in one modality can mislead fusion when unsupported by others. We also observe that humans, like models, do not consistently benefit from multimodal input. These insights position disagreement as a useful diagnostic signal for identifying challenging examples and improving empathy system robustness.

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.