ymoslem/acl-abstracts huggingface dataset:
Generation-Augmented and Embedding Fusion in Document-Level Event Argument Extraction (2025)

Document-level event argument extraction is a crucial task that aims to extract arguments from the entire document, beyond sentence-level analysis. Prior classification-based models still fail to explicitly capture significant relationships and heavily relies on large-scale datasets. In this study, we propose a novel approach called Generation-Augmented and Embedding Fusion. This approach first uses predefined templates and generative language models to produce an embedding capturing role relationship information, then integrates it into the foundational embedding derived from a classification model through a noval embedding fusion mechanism. We conduct the extensive experiments on the RAMS and WikiEvents datasets to demonstrate that our approach is more effective than the baselines, and that it is also data-efficient in low-resource scenarios.
C3LRSO: A Chinese Corpus for Complex Logical Reasoning in Sentence Ordering (2025)

Sentence ordering is the task of rearranging a set of unordered sentences into a coherent and logically consistent sequence. Recent work has primarily used pre-trained language models, achieving significant success in the task. However, existing sentence ordering corpora are predominantly in English, and comprehensive benchmark datasets for non-English languages are unavailable. Meanwhile, current datasets often insert specific markers into paragraphs, inadvertently making the logical sequence between sentences more apparent and reducing the models' ability to handle genuinely unordered sentences in real applications. To address these limitations, we develop C3LRSO, a high-quality Chinese sentence ordering dataset that overcomes the aforementioned shortcomings by providing genuinely unordered sentences without artificial segmentation cues. Furthermore, given the outstanding performance of large language models on NLP tasks, we evaluate these models on our dataset for this task. Additionally, we propose a simple yet effective parameter-free approach that outperforms existing methods on this task. Experiments demonstrate the challenging nature of the dataset and the strong performance of our proposed method. These findings highlight the potential for further research in sentence ordering and the development of more robust language models. Our dataset is freely available at https://github.com/JasonGuo1/C3LRSO.
KIA: Knowledge-Guided Implicit Vision-Language Alignment for Chest X-Ray Report Generation (2025)

Report generation (RG) faces challenges in understanding complex medical images and establishing cross-modal semantic alignment in radiology image-report pairs. Previous methods often overlook fine-grained cross-modal interaction, leading to insufficient understanding of detailed information. Recently, various large multimodal models have been proposed for image-text tasks. However, such models still underperform on rare domain tasks like understanding complex medical images. To address these limitations, we develop a new framework of Knowledge-guided Implicit vision-language Alignment for radiology report generation, named KIA. To better understand medical reports and images and build alignment between them, multi-task implicit alignment is creatively introduced, forming comprehensive understanding of medical images and reports. Additionally, to further meet medical refinement requirements, we design novel masking strategies guided by medical knowledge to enhance pathological observation and anatomical landm
On the Human-level Performance of Visual Question Answering (2025)

Visual7W has been widely used in assessing multiple-choice visual question-answering (VQA) systems. This paper reports on a replicated human experiment on Visual7W with the aim of understanding the human-level performance of VQA. The replication was not entirely successful because human participants performed significantly worse when answering {\textquotedblleft}where{\textquotedblright}, {\textquotedblleft}when{\textquotedblright}, and {\textquotedblleft}how{\textquotedblright} questions in compared to other question types. An error analysis discovered that the failure was a consequence of the non-deterministic distractors in Visual7W. GPT-4V was then evaluated using and was compared to the human-level performance. The results embody that, when evaluating models' capacity on Visual7W, the performance is not necessarily the higher, the better.
Representing the Under-Represented: Cultural and Core Capability Benchmarks for Developing Thai Large Language Models (2025)

The rapid advancement of large language models (LLMs) has highlighted the need for robust evaluation frameworks that assess their core capabilities, such as reasoning, knowledge, and commonsense, leading to the inception of certain widely-used benchmark suites such as the H6 benchmark. However, these benchmark suites are primarily built for the English language, and there exists a lack thereof for under-represented languages, in terms of LLM development, such as Thai. On the other hand, developing LLMs for Thai should also include enhancing the cultural understanding as well as core capabilities. To address these dual challenge in Thai LLM research, we propose two key benchmarks: Thai-H6 and Thai Cultural and Linguistic Intelligence Benchmark (ThaiCLI). Through a thorough evaluation of various LLMs with multi-lingual capabilities, we provide a comprehensive analysis of the proposed benchmarks and how they contribute to Thai LLM development. Furthermore, we have made both the datasets and evaluation code publicly available to encourage further research and development for Thai LLMs.
CONTRANS: Weak-to-Strong Alignment Engineering via Concept Transplantation (2025)

Ensuring large language models (LLM) behave consistently with human goals, values, and intentions is crucial for their safety but yet computationally expensive. To reduce the computational cost of alignment training of LLMs, especially for those with a huge number of parameters, and to reutilize learned value alignment, we propose ConTrans, a novel framework that enables weak-to-strong alignment transfer via concept transplantation. From the perspective of representation engineering, ConTrans refines concept vectors in value alignment from a source LLM (usually a weak yet aligned LLM). The refined concept vectors are then reformulated to adapt to the target LLM (usually a strong yet unaligned base LLM) via affine transformation. In the third step, ConTrans transplants the reformulated concept vectors into the residual stream of the target LLM. Experiments demonstrate the successful transplantation of a wide range of aligned concepts from 7B models to 13B and 70B models across multiple LLMs and LLM families. Remarkably, ConTrans even surpasses instruction-tuned models in terms of truthfulness. Experiment results validate the effectiveness of both inter-LLM-family and intra-LLM-family concept transplantation. Our work successfully demonstrates an alternative way to achieve weak-to-strong alignment generalization and control.
Idea23D: Collaborative LMM Agents Enable 3D Model Generation from Interleaved Multimodal Inputs (2025)

With the success of 2D diffusion models, 2D AIGC content has already transformed our lives. Recently, this success has been extended to 3D AIGC, with state-of-the-art methods generating textured 3D models from single images or text. However, we argue that current 3D AIGC methods still don`t fully unleash human creativity. We often imagine 3D content made from multimodal inputs, such as what it would look like if my pet bunny were eating a doughnut on the table. In this paper, we explore a novel 3D AIGC approach: generating 3D content from IDEAs. An IDEA is a multimodal input composed of text, image, and 3D models. To our knowledge, this challenging and exciting 3D AIGC setting has not been studied before. We propose the new framework Idea23D, which combines three agents based on large multimodal models (LMMs) and existing algorithmic tools. These three LMM-based agents are tasked with prompt generation, model selection, and feedback reflection. They collaborate and critique each other in a fully automated loop, without human intervention. The framework then generates a text prompt to create 3D models that align closely with the input IDEAs. We demonstrate impressive 3D AIGC results that surpass previous methods. To comprehensively assess the 3D AIGC capabilities of Idea23D, we introduce the Eval3DAIGC-198 dataset, containing 198 multimodal inputs for 3D generation tasks. This dataset evaluates the alignment between generated 3D content and input IDEAs. Our user study and quantitative results show that Idea23D significantly improves the success rate and accuracy of 3D generation, with excellent compatibility across various LMM, Text-to-Image, and Image-to-3D models. Code and dataset are available at https://idea23d.github.io/.
Learning from Impairment: Leveraging Insights from Clinical Linguistics in Language Modelling Research (2025)

This position paper investigates the potential of integrating insights from language impairment research and its clinical treatment to develop human-inspired learning strategies and evaluation frameworks for language models (LMs). We inspect the theoretical underpinnings underlying some influential linguistically motivated training approaches derived from neurolinguistics and, particularly, aphasiology, aimed at enhancing the recovery and generalization of linguistic skills in aphasia treatment, with a primary focus on those targeting the syntactic domain. We highlight how these insights can inform the design of rigorous assessments for LMs, specifically in their handling of complex syntactic phenomena, as well as their implications for developing human-like learning strategies, aligning with efforts to create more sustainable and cognitively plausible natural language processing (NLP) models.
Efficient Cross-modal Prompt Learning with Semantic Enhancement for Domain-robust Fake News Detection (2025)

With the development of multimedia technology, online social media has become a major medium for people to access news, but meanwhile, it has also exacerbated the dissemination of multi-modal fake news. An automatic and efficient multi-modal fake news detection (MFND) method is urgently needed. Existing MFND methods usually conduct cross-modal information interaction at later stage, resulting in insufficient exploration of complementary information between modalities. Another challenge lies in the differences among news data from different domains, leading to the weak generalization ability in detecting news from various domains. In this work, we propose an efficient Cross-modal Prompt Learning with Semantic enhancement method for Domain-robust fake news detection (CPLSD). Specifically, we design an efficient cross-modal prompt interaction module, which utilizes prompt as medium to realize lightweight cross-modal information interaction in the early stage of feature extraction, enabling to exploit rich modality complementary information. We design a domain-general prompt generation module that can adaptively blend domain-specific news features to generate domain-general prompts, for improving the domain generalization ability of the model. Furthermore, an image semantic enhancement module is designed to achieve image-to-text translation, fully exploring the semantic discriminative information of the image modality. Extensive experiments conducted on three MFND benchmarks demonstrate the superiority of our proposed approach over existing state-of-the-art MFND methods.
AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs (2025)

Arabic, with its rich diversity of dialects, remains significantly underrepresented in Large Language Models, particularly in dialectal variations. We address this gap by introducing seven synthetic datasets in dialects alongside Modern Standard Arabic (MSA), created using Machine Translation (MT) combined with human post-editing. We present AraDiCE, a benchmark for Arabic Dialect and Cultural Evaluation. We evaluate LLMs on dialect comprehension and generation, focusing specifically on low-resource Arabic dialects. Additionally, we introduce the first-ever fine-grained benchmark designed to evaluate cultural awareness across the Gulf, Egypt, and Levant regions, providing a novel dimension to LLM evaluation. Our findings demonstrate that while Arabic-specific models like Jais and AceGPT outperform multilingual models on dialectal tasks, significant challenges persist in dialect identification, generation, and translation. This work contributes {\ensuremath{\approx}}45K post-edited samples, a cultural benchmark, and highlights the importance of tailored training to improve LLM performance in capturing the nuances of diverse Arabic dialects and cultural contexts. We have released the dialectal translation models and benchmarks developed in this study (https://huggingface.co/datasets/QCRI/AraDiCE)
Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation (2025)

Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet either through semantic matching or distance scoring functions. However, it appears that semantic matching diminishes the separability of entities, while the distance scoring function weakens the semantics of entities. To address this issue, we propose a novel quaternion knowledge graph embedding model. Our model combines semantic matching with entity`s geometric distance to better measure the plausibility of triplets. Specifically, in the quaternion space, we perform a right rotation on the head entity and a reverse rotation on the tail entity to learn the rich semantic features. Then, we utilize distance adaptive translations to learn the geometric distance between entities. Furthermore, we provide mathematical proofs to demonstrate our model can handle complex logical relationships. Extensive experimental results and analyses show our model significantly outperforms previous models on well-known knowledge graph completion benchmark datasets. Our code is available at https://anonymous.4open.science/r/l2730.
How Credible Is an Answer From Retrieval-Augmented LLMs? Investigation and Evaluation With Multi-Hop QA (2025)

Retrieval-augmented Large Language Models (RaLLMs) are reshaping knowledge acquisition, offering long-form, knowledge-grounded answers through advanced reasoning and generation capabilities. Despite the emergence of impactful systems like WebGPT and New Bing, the reliability of RaLLMs, especially in complex situations, is under scrutiny. Our study tackles this concern by evaluating RaLLMs' question-answering performance using a novel benchmark focusing on Correctness and Groundedness. Correctness measures the logical soundness of the responses, and Groundedness checks for support by relevant references. We introduce an automated model-based evaluation pipeline for multi-hop question-answering tasks, revealing RaLLMs' proneness to generating inaccuracies when dealing with flawed or partial knowledge. To improve accuracy, we introduce two reasoning strategies, Self-Reflection' and Self-Completion,' enabling RaLLMs to identify and fill knowledge gaps, significantly improving answer quality without extensive model retraining.
Is Parameter Collision Hindering Continual Learning in LLMs? (2025)

Large Language Models (LLMs) often suffer from catastrophic forgetting when learning multiple tasks sequentially, making continual learning (CL) essential for their dynamic deployment. Existing state-of-the-art (SOTA) methods, such as O-LoRA, typically focus on constructing orthogonality tasks to decouple parameter interdependence from various domains.In this paper, we reveal that building non-collision parameters is a more critical factor in addressing CL challenges. Our theoretical and experimental analyses demonstrate that non-collision parameters provide better task orthogonality, which is a sufficient but unnecessary condition. Furthermore, knowledge from multiple domains will be preserved in non-collision parameter subspaces, making it more difficult to forget previously seen data. Leveraging this insight, we propose Non-collision Low-Rank Adaptation (N-LoRA), a simple yet effective approach leveraging low collision rates to enhance CL in LLMs. Experimental results on multiple CL benchmarks indicate that N-LoRA achieves superior performance (+2.9{\%}), higher task orthogonality ({\texttimes}4.1times), and lower parameter collision ({\texttimes}58.1times) than SOTA methods.
Jump To Hyperspace: Comparing Euclidean and Hyperbolic Loss Functions for Hierarchical Multi-Label Text Classification (2025)

Hierarchical Multi-Label Text Classification (HMTC) is a challenging machine learning task where multiple labels from a hierarchically organized label set are assigned to a single text. In this study, we examine the effectiveness of Euclidean and hyperbolic loss functions to improve the performance of BERT models on HMTC, which very few previous studies have adopted. We critically evaluate label-aware losses as well as contrastive losses in the Euclidean and hyperbolic space, demonstrating that hyperbolic loss functions perform comparably with non-hyperbolic loss functions on four commonly used HMTC datasets in most scenarios. While hyperbolic label-aware losses perform the best on low-level labels, the overall consistency and micro-averaged performance is compromised. Additionally, we find that our contrastive losses are less effective for HMTC when deployed in the hyperbolic space than non-hyperbolic counterparts. Our research highlights that with the right metrics and training objectives, hyperbolic space does not provide any additional benefits compared to Euclidean space for HMTC, thereby prompting a reevaluation of how different geometric spaces are used in other AI applications.
Exploring the Limitations of Detecting Machine-Generated Text (2025)

Recent improvements in the quality of the generations by large language models have spurred research into identifying machine-generated text. Such work often presents high-performing detectors. However, humans and machines can produce text in different styles and domains, yet the the performance impact of such on machine generated text detection systems remains unclear. In this paper, we audit the classification performance for detecting machine-generated text by evaluating on texts with varying writing styles. We find that classifiers are highly sensitive to stylistic changes and differences in text complexity, and in some cases degrade entirely to random classifiers. We further find that detection systems are particularly susceptible to misclassify easy-to-read texts while they have high performance for complex texts, leading to concerns about the reliability of detection systems. We recommend that future work attends to stylistic factors and reading difficulty levels of human-written and machine-generated text.
Boosting Text-to-SQL through Multi-grained Error Identification (2025)

Text-to-SQL is a technology that converts natural language questions into executable SQL queries, allowing users to query and manage relational databases more easily. In recent years, large language models have significantly advanced the development of text-to-SQL. However, existing methods often overlook validation of the generated results during the SQL generation process. Current error identification methods are mainly divided into self-correction approaches based on large models and feedback methods based on SQL execution, both of which have limitations. We categorize SQL errors into three main types: system errors, skeleton errors, and value errors, and propose a multi-grained error identification method. Experimental results demonstrate that this method can be integrated as a plugin into various methods, providing effective error identification and correction capabilities.
Know When to Fuse: Investigating Non-English Hybrid Retrieval in the Legal Domain (2025)

Hybrid search has emerged as an effective strategy to offset the limitations of different matching paradigms, especially in out-of-domain contexts where notable improvements in retrieval quality have been observed. However, existing research predominantly focuses on a limited set of retrieval methods, evaluated in pairs on domain-general datasets exclusively in English. In this work, we study the efficacy of hybrid search across a variety of prominent retrieval models within the unexplored field of law in the French language, assessing both zero-shot and in-domain scenarios. Our findings reveal that in a zero-shot context, fusing different domain-general models consistently enhances performance compared to using a standalone model, regardless of the fusion method. Surprisingly, when models are trained in-domain, we find that fusion generally diminishes performance relative to using the best single system, unless fusing scores with carefully tuned weights. These novel insights, among others, expand the applicability of prior findings across a new field and language, and contribute to a deeper understanding of hybrid search in non-English specialized domains.
MPID: A Modality-Preserving and Interaction-Driven Fusion Network for Multimodal Sentiment Analysis (2025)

The advancement of social media has intensified interest in the research direction of Multimodal Sentiment Analysis (MSA). However, current methodologies exhibit relative limitations, particularly in their fusion mechanisms that overlook nuanced differences and similarities across modalities, leading to potential biases in MSA. In addition, indiscriminate fusion across modalities can introduce unnecessary complexity and noise, undermining the effectiveness of the analysis. In this essay, a Modal-Preserving and Interaction-Driven Fusion Network is introduced to address the aforementioned challenges. The compressed representations of each modality are initially obtained through a Token Refinement Module. Subsequently, we employ a Dual Perception Fusion Module to integrate text with audio and a separate Adaptive Graded Fusion Module for text and visual data. The final step leverages text representation to enhance composite representation. Our experiments on CMU-MOSI, CMU-MOSEI, and CH-SIMS datasets demonstrate that our model achieves state-of-the-art performance.
Towards Efficient and Robust VQA-NLE Data Generation with Large Vision-Language Models (2025)

Natural Language Explanation (NLE) aims to elucidate the decision-making process by providing detailed, human-friendly explanations in natural language. It helps demystify the decision-making processes of large vision-language models (LVLMs) through the use of language models. While existing methods for creating a Vision Question-Answering with Natural Language Explanation (VQA-NLE) datasets can provide explanations, they heavily rely on human annotations that are time-consuming and costly. In this study, we propose a novel approach that leverages LVLMs to efficiently generate high-quality synthetic VQA-NLE datasets. By evaluating our synthetic data samples, we showcase how advanced prompting techniques can lead to the production of high-quality VQA-NLE data. Our findings indicate that this proposed method achieves up to 20x faster than human annotation, with only a minimal decrease in qualitative metrics, achieving robust quality that is nearly equivalent to human-annotated data. Furthermore, we show that incorporating visual prompts significantly enhances the relevance of text generation. Our study paves the way for a more efficient and robust automated generation of multi-modal NLE data, offering a promising solution to the problem.