ymoslem/acl-abstracts huggingface dataset:
Pangram at GenAI Detection Task 3: An Active Learning Approach to Machine-Generated Text Detection (2025)

We pretrain an autoregressive LLM-based detector on a wide variety of datasets, domains, languages, prompt schemes, and LLMs used to generate the AI portion of the dataset. We aggressively employ several augmentation strategies and preprocessing strategies to improve robustness. We then mine the RAID train set for the AI examples with the largest error based on the original classifier, and mix those examples and their human-written counterparts back into the training set. We then retrain the detector until convergence.
LuxVeri at GenAI Detection Task 3: Cross-Domain Detection of AI-Generated Text Using Inverse Perplexity-Weighted Ensemble of Fine-Tuned Transformer Models (2025)

This paper presents our approach for Task 3 of the GenAI content detection workshop at COLING-2025, focusing on Cross-Domain Machine-Generated Text (MGT) Detection. We propose an ensemble of fine-tuned transformer models, enhanced by inverse perplexity weighting, to improve classification accuracy across diverse text domains. For Subtask A (Non-Adversarial MGT Detection), we combined a fine-tuned RoBERTa-base model with an OpenAI detector-integrated RoBERTa-base model, achieving an aggregate TPR score of 0.826, ranking 10th out of 23 detectors. In Subtask B (Adversarial MGT Detection), our fine-tuned RoBERTa-base model achieved a TPR score of 0.801, securing 8th out of 22 detectors. Our results demonstrate the effectiveness of inverse perplexity-based weighting for enhancing generalization and performance in both non-adversarial and adversarial MGT detection, highlighting the potential for transformer models in cross-domain AI-generated content detection.
BBN-U.Oregon`s ALERT system at GenAI Content Detection Task 3: Robust Authorship Style Representations for Cross-Domain Machine-Generated Text Detection (2025)

This paper presents BBN-U.Oregon`s system, ALERT, submitted to the Shared Task 3: Cross-Domain Machine-Generated Text Detection. Our approach uses robust authorship-style representations to distinguish between human-authored and machine-generated text (MGT) across various domains. We employ an ensemble-based authorship attribution (AA) system that integrates stylistic embeddings from two complementary subsystems: one that focuses on cross-genre robustness with hard positive and negative mining strategies and another that captures nuanced semantic-lexical-authorship contrasts. This combination enhances cross-domain generalization, even under domain shifts and adversarial attacks. Evaluated on the RAID benchmark, our system demonstrates strong performance across genres and decoding strategies, with resilience against adversarial manipulation, achieving 91.8{\%} TPR at FPR=5{\%} on standard test sets and 82.6{\%} on adversarial sets.
Random at GenAI Detection Task 3: A Hybrid Approach to Cross-Domain Detection of Machine-Generated Text with Adversarial Attack Mitigation (2025)

Machine-generated text (MGT) detection has gained critical importance in the era of large language models, especially for maintaining trust in multilingual and cross-domain applica- tions. This paper presents Task 3 Subtask B: Adversarial Cross-Domain MGT Detection for in the COLING 2025 DAIGenC Workshop. Task 3 emphasizes the complexity of detecting AI-generated text across eight domains, eleven generative models, and four decoding strate- gies, with an added challenge of adversarial manipulation. We propose a robust detection framework transformer embeddings utilizing Domain-Adversarial Neural Networks (DANN) to address domain variability and adversarial robustness. Our model demonstrates strong performance in identifying AI-generated text under adversarial conditions while highlighting condition scope of future improvement.
MOSAIC at GENAI Detection Task 3 : Zero-Shot Detection Using an Ensemble of Models (2025)

MOSAIC introduces a new ensemble approach that combines several detector models to spot AI-generated texts. The method enhances the reliability of detection by integrating insights from multiple models, thus addressing the limitations of using a single detector model which often results in performance brittleness. This approach also involves using a theoretically grounded algorithm to minimize the worst-case expected encoding size across models, thereby optimizing the detection process. In this submission, we report evaluation results on the RAID benchmark, a comprehensive English-centric testbed for machine-generated texts. These results were obtained in the context of the {\textquotedblleft}Cross-domain Machine-Generated Text Detection{\textquotedblright} shared task. We show that our model can be competitive for a variety of domains and generator models, but that it can be challenged by adversarial attacks and by changes in the text generation strategy.
GenAI Content Detection Task 3: Cross-Domain Machine Generated Text Detection Challenge (2025)

Recently there have been many shared tasks targeting the detection of generated text from Large Language Models (LLMs). However, these shared tasks tend to focus either on cases where text is limited to one particular domain or cases where text can be from many domains, some of which may not be seen during test time. In this shared task, using the newly released RAID benchmark, we aim to answer whether or not models can detect generated text from a large, yet fixed, number of domains and LLMs, all of which are seen during training. Over the course of three months, our task was attempted by 9 teams with 23 detector submissions. We find that multiple participants were able to obtain accuracies of over 99{\%} on machine-generated text from RAID while maintaining a 5{\%} False Positive Rate{---}suggesting that detectors are able to robustly detect text from many domains and models simultaneously. We discuss potential interpretations of this result and provide directions for future research.
Chat Bankman-Fried: an Exploration of LLM Alignment in Finance (2025)

Advancements in large language models (LLMs) have renewed concerns about AI alignment{---}the consistency between human and AI goals and values. As various jurisdictions enact legislation on AI safety, the concept of alignment must be defined and measured across different domains. This paper proposes an experimental framework to assess whether LLMs adhere to ethical and legal standards in the relatively unexplored context of finance. We prompt ten LLMs to impersonate the CEO of a financial institution and test their willingness to misuse customer assets to repay outstanding corporate debt. Beginning with a baseline configuration, we adjust preferences, incentives and constraints, analyzing the impact of each adjustment with logistic regression. Our findings reveal significant heterogeneity in the baseline propensity for unethical behavior of LLMs. Factors such as risk aversion, profit expectations, and regulatory environment consistently influence misalignment in ways predicted by economic theory, although the magnitude of these effects varies across LLMs. This paper highlights the benefits and limitations of simulation-based, ex-post safety testing. While it can inform financial authorities and institutions aiming to ensure LLM safety, there is a clear trade-off between generality and cost.
GraphRAG Analysis for Financial Narrative Summarization and A Framework for Optimizing Domain Adaptation (2025)

Large Language Models (LLMs) have shown promise in summarizing complex documents, but their limitations in handling lengthy documents and capturing global information hinder their performance in tasks like Query-Focused Summarization (QFS). We explore GraphRAG, a retrieval-augmented generation approach that utilizes a globally summarized knowledge graph derived from an LLM. We apply GraphRAG to the Financial Narrative Summarization (FNS) dataset, which consists of lengthy financial reports. Our results show that a naive RAG approach outperforms GraphRAG in terms of comprehensiveness, directness, conciseness and completeness. However, we demonstrate that optimizing entity and relation extraction using an LLM as an optimizer can enhance GraphRAG`s performance. Our study highlights the need for domain-specific optimization to improve GraphRAG`s capabilities for summarization tasks in facts-heavy domains like finance. We propose an optimization framework that extends GraphRAG`s original domain adaptation strategy by incorporating entity and relations optimization, leading to improved performance in capturing relevant entities and relationships. Our findings contribute to the development of more effective summarization models for complex documents in finance and other domains.
BuDDIE: A Business Document Dataset for Multi-task Information Extraction (2025)

The field of visually rich document understanding (VRDU) aims to solve a multitude of well-researched NLP tasks in the multi-modal domain. Several datasets exist for research on specific tasks of VRDU, such as document classification (DC), key entity extraction (KEE), entity linking, visual question answering (VQA), inter alia. These datasets cover documents like invoices and receipts with sparse annotations such that they support one or two co-related tasks (e.g., entity extraction and entity linking). Unfortunately, only focusing on a single specific type of documents or task is not representative of how documents often need to be processed in the wild {--} where variety in style and requirements is expected. In this paper, we introduce BuDDIE: Business Document Dataset for Information Extraction, the first multi-task dataset of 1665 real-world business documents that contains rich and dense annotations for DC, KEE, and VQA. Our dataset consists of publicly available business entity documents from US state government websites. The documents are structured and vary in their style and layout across states and types (e.g., forms, certificates, reports, etc.). We provide data variety and quality metrics for BuDDIE as well as a series of baselines for each task. Our baselines cover traditional textual, multi-modal, and large language model approaches to VRDU.
FinMoE: A MoE-based Large Chinese Financial Language Model (2025)

Large-scale language models have demonstrated remarkable success, achieving strong performance across a variety of general tasks. However, when applied to domain-specific fields, such as finance, these models face challenges due to the need for both specialized knowledge and robust general capabilities. In this paper, we introduce FinMoE, a MOE-based large-scale Chinese financial language model that bridges the gap between general language models and domain-specific requirements. FinMoE employs a dense MoE architecture, where all expert networks are simultaneously activated and dynamically combined to effectively integrate general linguistic understanding with domain-specific financial expertise. Experimental results demonstrate that FinMoE achieves state-of-the-art performance on both general-purpose and financial benchmarks at a comparable scale, validating its ability to balance domain specialization with general knowledge and reasoning.
Bridging the Gap: Efficient Cross-Lingual NER in Low-Resource Financial Domain (2025)

We present an innovative and efficient modeling framework for cross-lingual named entity recognition (NER), leveraging the strengths of knowledge distillation and consistency training. Our approach distills knowledge from an XLM-RoBERTa model pre-trained on a high-resource source language (English) to a student model, which then undergoes semi-supervised consistency training with KL divergence loss on a low-resource target language (Arabic). We focus our application on the financial domain, using a small, sourced dataset of financial transactions as seen in SMS messages Using datasets comprising SMS messages in English and Arabic containing financial transaction information, we aim to transfer NER capabilities from English to Arabic with minimal labeled Arabic samples. The framework generalizes named entity recognition from English to Arabic, achieving F1 scores of 0.74 on the Arabic financial transaction dataset and 0.61 on the WikiANN dataset, surpassing or closely competing with models that have 1.7 and 5.3 more parameters, respectively, while efficiently training it on a single T4 GPU. Our experiments show that using a small number of labeled data for low-resource cross-lingual NER applications is a wiser choice than utilizing zero-shot techniques while also using up fewer resources. This framework holds significant potential for developing multilingual applications, particularly in regions where digital interactions span English and low-resource languages.
Evaluating Financial Literacy of Large Language Models through Domain Specific Languages for Plain Text Accounting (2025)

Large language models (LLMs) have proven highly effective for a wide range of tasks, including code generation. Recently, advancements in their capabilities have shown promise in areas like mathematical reasoning, chain-of-thought processes and self-reflection. However, their effectiveness in domains requiring nuanced understanding of financial contexts, such as accounting, remains unclear. In this study, we evaluate how well LLMs perform in generating code for domain-specific languages (DSLs) in accounting, using Beancount as a case study. We create a set of tasks based on common financial ratios, to evaluate the numeracy and financial literacy of LLMs. Our findings reveal that while LLMs are state-of-the art in generative tasks, they struggle severely with accounting, often producing inaccurate calculations and misinterpreting financial scenarios. We characterize these shortcomings through a comprehensive evaluation, shedding light on the limitations of LLMs in understanding and handling money-related tasks.
Synthetic Data Generation Using Large Language Models for Financial Question Answering (2025)

Recent research has shown excellent performance of large language models (LLMs) for answering questions requiring multi-step financial reasoning. While the larger models have been used with zero-shot or few-shot prompting, the smaller variants need fine-tuning on training data containing questions and the corresponding answers that includes detailed reasoning demonstrations. To alleviate the significant cost of creating a data set with complex questions and corresponding answers, we explore the use of synthetic data for financial question answering using a multi-step LLM based approach to generate question as well as the answers with reasoning steps. We consider standard as well as conversational financial question answering scenarios. We experiment with synthetic data generation for three different real financial reasoning problems that already have manually collected data sets created with the help of financial experts. Using the same document sources, we use the proposed LLM based approach to generate synthetic questions and answers. To measure the effectiveness, we train multiple small language models (SLMs) on these synthetic data and compare the performance with that of the same SLMs trained on the real data. We further perform extensive experimental analysis generating important evidence on the potential of using synthetic data in financial reasoning tasks.
Concept-Based RAG Models: A High-Accuracy Fact Retrieval Approach (2025)

This study introduces a concept-based methodology to optimize Retrieval-Augmented Generation (RAG) tasks by assessing dataset certainty using entropy-based metrics and concept extraction techniques. Unlike traditional methods focused on reducing LLM hallucinations or modifying data structures, this approach evaluates inherent knowledge uncertainty from an LLM perspective. By pre-processing documents with LLMs, the concept-based method significantly enhances precision in tasks demanding high accuracy, such as legal, finance, or formal document responses.
Training LayoutLM from Scratch for Efficient Named-Entity Recognition in the Insurance Domain (2025)

Generic pre-trained neural networks may struggle to produce good results in specialized domains like finance and insurance. This is due to a domain mismatch between training data and downstream tasks, as in-domain data are often scarce due to privacy constraints. In this work, we compare different pre-training strategies for LayoutLM. We show that using domain-relevant documents improves results on a named-entity recognition (NER) problem using a novel dataset of anonymized insurance-related financial documents called PAYSLIPS. Moreover, we show that we can achieve competitive results using a smaller and faster model.
AveniBench: Accessible and Versatile Evaluation of Finance Intelligence (2025)

Over the last few years, there has been great interest in applying large language models (LLMs) to problems in the finance industry, and the field needs a robust LLM benchmark to support this work. Current financial LLM benchmarks contain simple tasks which are not representative of real use cases and have test sets with licences that do not allow commercial use. In response, we release AveniBench, a permissively licensed benchmark that tests a group of six key finance-related skills: tabular reasoning, numerical reasoning, question answering, long context modelling, summarisation and dialogue. We refactor the test sets to ensure that metrics are comparable, providing a unified framework. Furthermore, AveniBench introduces two task difficulty modes, easy and hard, enabling scalable evaluation based on real-world deployment needs. We use our benchmark to evaluate a diverse set of 20 widely used LLMs, from small open-weight models to proprietary systems like GPT-4. This evaluation initiates our public leaderboard, providing valuable insights for future academic research and commercial development.
Forecasting Credit Ratings: A Case Study where Traditional Methods Outperform Generative LLMs (2025)

Large Language Models (LLMs) have been shown to perform well for many downstream tasks. Transfer learning can enable LLMs to acquire skills that were not targeted during pre-training. In financial contexts, LLMs can sometimes beat well-established benchmarks. This paper investigates how well LLMs perform at forecasting corporate credit ratings. We show that while LLMs are very good at encoding textual information, traditional methods are still very competitive when it comes to encoding numeric and multimodal data. For our task, current LLMs perform worse than a more traditional XGBoost architecture that combines fundamental and macroeconomic data with high-density text-based embedding features. We investigate the degree to which the text encoding methodology affects performance and interpretability.
Investigating the effectiveness of length based rewards in DPO for building Conversational Financial Question Answering Systems (2025)

In this paper, we address the numerical reasoning challenges of financial question-answering systems. We propose a two-stage approach where models first generate intermediate calculations and then produce the final answer. We perform two set of experiments to evaluate the performance of our approach. In the first, we compare single-step and multi-step approaches, demonstrating that incorporating intermediate calculations significantly improves numerical accuracy. In the second experiment, we compare traditional DPO and iterative DPO (iDPO) with length-regularized DPO. We show that while traditional DPO reduced parsing errors, it introduces verbosity; iDPO improves reasoning iteratively but faces diminishing returns. On the other hand, Length-regularized DPO reduces verbosity of intermediate calculation as well as enhances numerical accuracy across all models. These results highlight the potential of combining intermediate reasoning steps with domain-specific optimizations to build robust financial question-answering systems.
CreditLLM: Constructing Financial AI Assistant for Credit Products using Financial LLM and Few Data (2025)

Facilitating financial technology with the large-language model (LLM) has been developing in recent years. To address the challenges in one of the biggest world-wide markets, China, Chinese-expertise financial LLM has also been studied. The related works focus on conventional NLP tasks in finance, while developing LLM for specific tasks is also required. Besides, in the credit loan business, the existing AI-based approaches are largely related to Credit like Credit rating and Fraud prediction, while credit product customization is still missing. In China, Inclusive Finance and Rural Finance become two hot topics that raise critical challenges in flexibly customizing credit products to meet the variable fund requirements of small {\&} micro businesses, individual businesses, and agricultural businesses of local character. In this paper, the credit product customization is studied by developing an LLM-based financial AI assistant for the credit loan business. It is proposed to satisfy the business requirements of customer counseling, recommendation, and question-answers regarding credit loans. The proposed LLM is developed by Chinese prompt data automatically constructed based on a small set of real-world credit products. The experiments demonstrate its effectiveness in credit loan-related ability while maintaining comparable performance in conventional finance NLP tasks.
Modeling Interactions Between Stocks Using LLM-Enhanced Graphs for Volume Prediction (2025)

Accurate trading volume prediction is essential for portfolio optimization, market regulation, and financial risk control. An effective method for predicting trading volume involves building a graph to model relations between stock. Recent research has enhanced these models by integrating stock news to improve forecasting ability. However, existing approaches primarily integrate news data as auxiliary features for nodes in Graph Neural Networks (GNNs), overlooking the relational information between stocks embedded in news. To address this, we propose LLM-Enhanced Dynamic Graph Neural Network (LED-GNN), a framework that constructs dynamic graphs using inter-stock relationships extracted from news via a large language model (LLM)-centered pipeline, combined with graphs learned from historical price-volume data. A dynamic GNN then processes these graphs to generate predictions. Evaluated on a real-world dataset, TOPIX, with Reuters Financial News, LED-GNN consistently outperformed all baseline models, achieving a 2{\%} improvement over the strongest baseline.
Financial Named Entity Recognition: How Far Can LLM Go? (2025)

The surge of large language models (LLMs) has revolutionized the extraction and analysis of crucial information from a growing volume of financial statements, announcements, and business news. Recognition for named entities to construct structured data poses a significant challenge in analyzing financial documents and is a foundational task for intelligent financial analytics. However, how effective are these generic LLMs and their performance under various prompts are yet need a better understanding. To fill in the blank, we present a systematic evaluation of state-of-the-art LLMs and prompting methods in the financial Named Entity Recognition (NER) problem. Specifically, our experimental results highlight their strengths and limitations, identify five representative failure types, and provide insights into their potential and challenges for domain-specific tasks.