ymoslem/acl-abstracts huggingface dataset:
GAProtoNet: A Multi-head Graph Attention-based Prototypical Network for Interpretable Text Classification (2025)

Pretrained transformer-based Language Models (LMs) are well-known for their ability to achieve significant improvement on text classification tasks with their powerful word embeddings, but their \textit{black-box} nature, which leads to a lack of interpretability, has been a major concern. In this work, we introduce GAProtoNet, a novel \textit{white-box} Multi-head Graph Attention-based Prototypical Network designed to explain the decisions of text classification models built with LM encoders. In our approach, the input vector and prototypes are regarded as nodes within a graph, and we utilize multi-head graph attention to selectively construct edges between the input node and prototype nodes to learn an interpretable prototypical representation. During inference, the model makes decisions based on a linear combination of activated prototypes weighted by the attention score assigned for each prototype, allowing its choices to be transparently explained by the attention weights and the prototypes. Experiments on multiple public datasets show our approach achieves superior results without sacrificing the accuracy of the original black-box LMs. We also compare with four alternative prototypical network variations and our approach achieves the best accuracy and F1 among all. Our case study and visualization of prototype clusters also demonstrate the efficiency in explaining the decisions of black-box models built with LMs.
Few-shot domain adaptation for named-entity recognition via joint constrained k-means and subspace selection (2025)

Named-entity recognition (NER) is a task that typically requires large annotated datasets, which limits its applicability across domains with varying entity definitions. This paper addresses few-shot NER, aiming to transfer knowledge to new domains with minimal supervision. Unlike previous approaches that rely solely on limited annotated data, we propose a weakly-supervised algorithm that combines small labeled datasets with large amounts of unlabeled data. Our method extends the k-means algorithm with label supervision, cluster size constraints, and domain-specific discriminative subspace selection. This unified framework achieves state-of-the-art results in few-shot NER, demonstrating its effectiveness in leveraging unlabeled data and adapting to domain-specific challenges.
An Efficient Retrieval-Based Method for Tabular Prediction with LLM (2025)

Tabular prediction, a well-established problem in machine learning, has consistently garnered significant research attention within academia and industry. Recently, with the rapid development of large language models (LLMs), there has been increasing exploration of how to apply LLMs to tabular prediction tasks. Many existing methods, however, typically rely on extensive pre-training or fine-tuning of LLMs, which demands considerable computational resources. To avoid this, we propose a retrieval-based approach that utilizes the powerful capabilities of LLMs in representation, comprehension, and inference. Our approach eliminates the need for training any modules or performing data augmentation, depending solely on information from target dataset. Experimental results reveal that, even without specialized training for tabular data, our method exhibits strong predictive performance on tabular prediction task, affirming its practicality and effectiveness.
AIGT: AI Generative Table Based on Prompt (2025)

Tabular data, which accounts for over 80{\%} of enterprise data assets, is vital in various fields. With growing concerns about privacy protection and data-sharing restrictions, generating high-quality synthetic tabular data has become essential. Recent advancements show that large language models (LLMs) can effectively generate realistic tabular data by leveraging semantic information and overcoming the challenges of high-dimensional data that arise from one-hot encoding. However, current methods do not fully utilize the rich information available in tables. To address this, we introduce AI Generative Table based on prompt enhancement, a novel approach that utilizes metadata information, such as table descriptions and schemas, as prompts to generate ultra-high-quality synthetic data. To overcome the token limit constraints of LLMs, we propose long-token partitioning algorithms that enable AIGT to model tables of any scale. AIGT achieves state-of-the-art performance on 14 out of 20 public datasets and two real industry datasets within the Alipay risk control system.
IRR: Image Review Ranking Framework for Evaluating Vision-Language Models (2025)

Large-scale Vision-Language Models (LVLMs) process both images and text, excelling in multimodal tasks such as image captioning and description generation. However, while these models excel at generating factual content, their ability to generate and evaluate texts reflecting perspectives on the same image, depending on the context, has not been sufficiently explored. To address this, we propose IRR: Image Review Rank, a novel evaluation framework designed to assess critic review texts from multiple perspectives. IRR evaluates LVLMs by measuring how closely their judgments align with human interpretations. We validate it using a dataset of images from 15 categories, each with five critic review texts and annotated rankings in both English and Japanese, totaling over 2,000 data instances. Our results indicate that, although LVLMs exhibited consistent performance across languages, their correlation with human annotations was insufficient, highlighting the need for further advancements. These findings highlight the limitations of current evaluation methods and the need for approaches that better capture human reasoning in Vision {\&} Language tasks.
Development of Numerical Error Detection Tasks to Analyze the Numerical Capabilities of Language Models (2025)

Numbers are used to describe quantities in various scenarios in daily life; therefore, numerical errors can significantly affect the meaning of the entire sentence, and even a single-letter error can be fatal. Detecting numerical errors often requires a high level of commonsense and is difficult even with the recent large language models (LLMs). In this study, we create a benchmark dataset of numerical error detection that uses automatically generated numerical errors. In our analysis, we classify the numerical errors based on the properties of the errors and investigate the ability of the model from several perspectives, including the error class, error size, and passage domain. The experimental results indicate that GPT-3.5, GPT-4, and Llama-3-Instruct (8B) perform well in the numerical error detection task; however, they are not as accurate as humans. We find that the LLMs misidentified correct numbers as errors more frequently than the humans did. In particular, the analysis demonstrates that the current LLMs still need improvement for detecting numerical errors requiring calculations or extensive prior knowledge.
Searching for Structure: Investigating Emergent Communication with Large Language Models (2025)

Human languages have evolved to be structured through repeated language learning and use. These processes introduce biases that operate during language acquisition and shape linguistic systems toward communicative efficiency. In this paper, we investigate whether the same happens if artificial languages are optimised for implicit biases of Large Language Models (LLMs). To this end, we simulate a classical referential game in which LLMs learn and use artificial languages. Our results show that initially unstructured holistic languages are indeed shaped to have some structural properties that allow two LLM agents to communicate successfully. Similar to observations in human experiments, generational transmission increases the learnability of languages, but can at the same time result in non-humanlike degenerate vocabularies. Taken together, this work extends experimental findings, shows that LLMs can be used as tools in simulations of language evolution, and opens possibilities for future human-machine experiments in this field.
Decoding Decoded: Understanding Hyperparameter Effects in Open-Ended Text Generation (2025)

Decoding strategies for generative large language models (LLMs) are a critical but often underexplored aspect of text generation tasks. Guided by specific hyperparameters, these strategies aim to transform the raw probability distributions produced by language models into coherent, fluent text. In this study, we undertake a large-scale empirical assessment of a range of decoding methods, open-source LLMs, textual domains, and evaluation protocols to determine how hyperparameter choices shape the outputs. Our experiments include both factual (e.g., news) and creative (e.g., fiction) domains, and incorporate a broad suite of automatic evaluation metrics alongside human judgments. Through extensive sensitivity analyses, we distill practical recommendations for selecting and tuning hyperparameters, noting that optimal configurations vary across models and tasks. By synthesizing these insights, this study provides actionable guidance for refining decoding strategies, enabling researchers and practitioners to achieve higher-quality, more reliable, and context-appropriate text generation outcomes.
Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems (2025)

Retrieval-Augmented Generation (RAG) has recently gained significant attention for its enhanced ability to integrate external knowledge sources into open-domain question answering (QA) tasks. However, it remains unclear how these models address fairness concerns, particularly with respect to sensitive attributes such as gender, geographic location, and other demographic factors. First, as language models evolve to prioritize utility, like improving exact match accuracy, fairness considerations may have been largely overlooked. Second, the complex, multi-component architecture of RAG methods poses challenges in identifying and mitigating biases, as each component is optimized for distinct objectives. In this paper, we aim to empirically evaluate fairness in several RAG methods. We propose a fairness evaluation framework tailored to RAG, using scenario-based questions and analyzing disparities across demographic attributes. Our experimental results indicate that, despite recent advances in utility-driven optimization, fairness issues persist in both the retrieval and generation stages. These findings underscore the need for targeted interventions to address fairness concerns throughout the RAG pipeline. The dataset and code used in this study are publicly available at this GitHub Repository.
CUTE: A Multilingual Dataset for Enhancing Cross-Lingual Knowledge Transfer in Low-Resource Languages (2025)

Large Language Models (LLMs) demonstrate exceptional zero-shot capabilities in various NLP tasks, significantly enhancing user experience and efficiency. However, this advantage is primarily limited to resource-rich languages. For the diverse array of low-resource languages, support remains inadequate, with the scarcity of training corpora considered the primary cause. We construct and open-source CUTE (Chinese, Uyghur, Tibetan, English) dataset, consisting of two 25GB sets of four-language corpora (one parallel and one non-parallel), obtained through machine translation. CUTE encompasses two resource-rich languages (Chinese and English) and two low-resource languages (Uyghur and Tibetan). Prior to constructing CUTE, human assessment validates that the machine translation quality between Chinese-Uyghur and Chinese-Tibetan approaches that of Chinese-English translation. CUTE represents the largest open-source corpus for Uyghur and Tibetan languages to date, and we demonstrate its effectiveness in enhancing LLMs' ability to process low-resource languages while investigating the role of corpus parallelism in cross-lingual transfer learning. The CUTE corpus and related models are made publicly available to the research community.
How Ambiguous Are the Rationales for Natural Language Reasoning? A Simple Approach to Handling Rationale Uncertainty (2025)

The quality of rationales is essential in the reasoning capabilities of language models. Rationales not only enhance reasoning performance in complex natural language tasks but also justify model decisions. However, obtaining impeccable rationales is often impossible. Our study aims to investigate how ambiguous rationales play in model performances of natural language reasoning. We first assess the ambiguity of rationales through the lens of entropy and uncertainty in model prior beliefs, exploring its impact on task performance. We then propose a simple way to guide models to choose between two different reasoning paths depending on the ambiguity of the rationales. Our empirical results demonstrate that this approach leads to robust performance, particularly in adversarial scenarios where rationale quality is inconsistent.
Planning with Multi-Constraints via Collaborative Language Agents (2025)

The rapid advancement of neural language models has sparked a new surge of intelligent agent research. Unlike traditional agents, large language model-based agents (LLM agents) have emerged as a promising paradigm for achieving artificial general intelligence (AGI) due to their superior reasoning and generalization capabilities. Effective planning is crucial for the success of LLM agents in real-world tasks, making it a highly pursued topic in the community. Current planning methods typically translate tasks into executable action sequences. However, determining a feasible or optimal sequence for complex tasks with multiple constraints at fine granularity, which often requires compositing long chains of heterogeneous actions, remains challenging. This paper introduces Planning with Multi-Constraints (PMC), a zero-shot methodology for collaborative LLM-based multi-agent systems that simplifies complex task planning with constraints by decomposing it into a hierarchy of subordinate tasks. Each subtask is then mapped into executable actions. PMC was assessed on two constraint-intensive benchmarks, TravelPlanner and API-Bank. Notably, PMC achieved an average 42.68{\%} success rate on TravelPlanner, significantly higher than GPT-4 (2.92{\%}), and outperforming GPT-4 with ReAct on API-Bank by 13.64{\%}, showing the immense potential of integrating LLM with multi-agent systems. We also show that PMC works with small LLM as the planning core, e.g., LLaMA-3.1-8B.
Enhancing Nursing and Elderly Care with Large Language Models: An AI-Driven Framework (2025)

This paper explores the application of large language models (LLMs) in nursing and elderly care, focusing on AI-driven patient monitoring and interaction. We introduce a novel Chinese nursing dataset and implement incremental pre-training (IPT) and supervised fine-tuning (SFT) techniques to enhance LLM performance in specialized tasks. Using LangChain, we develop an interactable nursing assistant capable of real-time care and personalized interventions. Experimental results demonstrate significant improvements, paving the way for AI-driven solutions to meet the growing demands of healthcare in aging populations.
A High-Quality Text-Rich Image Instruction Tuning Dataset via Hybrid Instruction Generation (2025)

Large multimodal models still struggle with text-rich images because of inadequate training data. Self-Instruct provides an annotation-free way for generating instruction data, but its quality is poor, as multimodal alignment remains a hurdle even for the largest models. In this work, we propose LLaVAR-2, to enhance multimodal alignment for text-rich images through hybrid instruction generation between human annotators and large language models. Specifically, it involves detailed image captions from human annotators, followed by the use of these annotations in tailored text prompts for GPT-4o to curate a dataset. It also implements several mechanisms to filter out low-quality data, and the resulting dataset comprises 424k high-quality pairs of instructions. Empirical results show that models fine-tuned on this dataset exhibit impressive enhancements over those trained with self-instruct data.
Cross-Lingual Knowledge Projection and Knowledge Enhancement for Zero-Shot Question Answering in Low-Resource Languages (2025)

Knowledge bases (KBs) in low-resource languages (LRLs) are often incomplete, posing a challenge for developing effective question answering systems over KBs in those languages. On the other hand, the size of training corpora for LRL language models is also limited, restricting the ability to do zero-shot question answering using multilingual language models. To address these issues, we propose a two-fold approach. First, we introduce LeNS-Align, a novel cross-lingual mapping technique which improves the quality of word alignments extracted from parallel English-LRL text by combining lexical alignment, named entity recognition, and semantic alignment. LeNS-Align is applied to perform cross-lingual projection of KB triples. Second, we leverage the projected KBs to enhance multilingual language models' question answering capabilities by augmenting the models with Graph Neural Networks embedding the projected knowledge. We apply our approach to map triples from two existing English KBs, ConceptNet and DBpedia, to create comprehensive LRL knowledge bases for four low-resource South African languages. Evaluation on three translated test sets show that our approach improves zero-shot question answering accuracy by up to 17{\%} compared to baselines without KB access. The results highlight how our approach contributes to bridging the knowledge gap for low-resource languages by expanding knowledge coverage and question answering capabilities.
FarExStance: Explainable Stance Detection for Farsi (2025)

We introduce FarExStance, a new dataset for explainable stance detection in Farsi. Each instance in this dataset contains a claim, the stance of an article or social media post towards that claim, and an extractive explanation which provides evidence for the stance label. We compare the performance of a fine-tuned multilingual RoBERTa model to several large language models in zero-shot, few-shot, and parameter-efficient fine-tuned settings on our new dataset. On stance detection, the most accurate models are the fine-tuned RoBERTa model, the LLM Aya-23-8B which has been fine-tuned using parameter-efficient fine-tuning, and few-shot Claude-3.5-Sonnet. Regarding the quality of the explanations, our automatic evaluation metrics indicate that few-shot GPT-4o generates the most coherent explanations, while our human evaluation reveals that the best Overall Explanation Score (OES) belongs to few-shot Claude-3.5-Sonnet. The fine-tuned Aya-32-8B model produced explanations most closely aligned with the reference explanations.
Unveiling Language Competence Neurons: A Psycholinguistic Approach to Model Interpretability (2025)

As large language models (LLMs) advance in their linguistic capacity, understanding how they capture aspects of language competence remains a significant challenge. This study therefore employs psycholinguistic paradigms, which are well-suited for probing deeper cognitive aspects of language processing, to explore neuron-level representations in language model across three tasks: sound-shape association, sound-gender association, and implicit causality. Our findings indicate that while GPT-2-XL struggles with the sound-shape task, it demonstrates human-like abilities in both sound-gender association and implicit causality. Targeted neuron ablation and activation manipulation reveal a crucial relationship: When GPT-2-XL displays a linguistic ability, specific neurons correspond to that competence; conversely, the absence of such an ability indicates a lack of specialized neurons. This study is the first to utilize psycholinguistic experiments to investigate deep language competence at the neuron level, providing a new level of granularity in model interpretability and insights into the internal mechanisms driving language ability in the transformer-based LLM.
Cross-Dialect Information Retrieval: Information Access in Low-Resource and High-Variance Languages (2025)

A large amount of local and culture-specific knowledge (e.g., people, traditions, food) can only be found in documents written in dialects. While there has been extensive research conducted on cross-lingual information retrieval (CLIR), the field of cross-dialect retrieval (CDIR) has received limited attention. Dialect retrieval poses unique challenges due to the limited availability of resources to train retrieval models and the high variability in non-standardized languages. We study these challenges on the example of German dialects and introduce the first German dialect retrieval dataset, dubbed WikiDIR, which consists of seven German dialects extracted from Wikipedia. Using WikiDIR, we demonstrate the weakness of lexical methods in dealing with high lexical variation in dialects. We further show that commonly used CLIR methods such as query translation or zero-shot cross-lingual transfer with multilingual encoders do not transfer well to extremely low-resource setups, motivating the need for resource-lean and dialect-specific retrieval models.
MoKA:Parameter Efficiency Fine-Tuning via Mixture of Kronecker Product Adaption (2025)

With the rapid development of large language models (LLMs), traditional full-parameter fine-tuning methods have become increasingly expensive in terms of computational resources and time costs. For this reason, parameter efficient fine-tuning (PEFT) methods have emerged. Among them, Low-Rank Adaptation (LoRA) is one of the current popular PEFT methods, which is widely used in large language models. However, the low-rank update mechanism of LoRA somewhat limits its ability to approximate full-parameter fine-tuning during the training process. In this paper, we propose a novel PEFT framework, MoKA (Mixture of Kronecker Product Adaptation), which combines the Kronecker product with the Mixture-of-Experts (MoE) method. By replacing the low-rank decomposition of the weight update matrix with Kronecker products and utilizing a sparse MoE architecture, MoKA achieves parameter efficiency and better model performance. Additionally, we design an efficient routing module to further compress the parameter size. We conduct extensive experiments on the GLUE benchmark, E2E NLG Challenge, and instruction tuning tasks for LLMs. The results demonstrate that MoKA outperforms existing PEFT methods.
AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator (2025)

Artificial intelligence has significantly revolutionized healthcare, particularly through large language models (LLMs) that demonstrate superior performance in static medical question answering benchmarks. However, evaluating the potential of LLMs for real-world clinical applications remains challenging due to the intricate nature of doctor-patient interactions. To address this, we introduce AI Hospital, a multi-agent framework emulating dynamic medical interactions between Doctor as player and NPCs including Patient and Examiner. This setup allows for more practical assessments of LLMs in simulated clinical scenarios. We develop the Multi-View Medical Evaluation (MVME) benchmark, utilizing high-quality Chinese medical records and multiple evaluation strategies to quantify the performance of LLM-driven Doctor agents on symptom collection, examination recommendations, and diagnoses. Additionally, a dispute resolution collaborative mechanism is proposed to enhance medical interaction capabilities through iterative discussions. Despite improvements, current LLMs (including GPT-4) still exhibit significant performance gaps in multi-turn interactive scenarios compared to non-interactive scenarios. Our findings highlight the need for further research to bridge these gaps and improve LLMs' clinical decision-making capabilities. Our data, code, and experimental results are all open-sourced at https://github.com/LibertFan/AI{\_}Hospital.