ymoslem/acl-abstracts huggingface dataset:
Deloitte (Drocks) at the Financial Misinformation Detection Challenge Task: Enhancing Misinformation Detection through Instruction-Tuned Models (2025)

Large Language Models (LLMs) are capable of producing highly fluent and convincing text; however, they can sometimes include factual errors and misleading information. Consequently, LLMs have emerged as tools for the rapid and cost-effective generation of financial misinformation, enabling bad actors to harm individual investors and attempt to manipulate markets. In this study, we instruction-tune Generative Pre-trained Transformers (GPT-4o-mini) to detect financial misinformation and produce concise explanations for why a given claim or statement is classified as misinformation, leveraging the contextual information provided. Our model achieved fourth place in Financial Misinformation Detection (FMD) shared task with a micro F1 score of 0.788 and a ROUGE-1 score of 0.743 on the private test set of FACT-checking within the FINancial domain (FIN-FACT) dataset provided by the shared task organizers.
Capybara at the Financial Misinformation Detection Challenge Task: Chain-of-Thought Enhanced Financial Misinformation Detection (2025)

Financial misinformation poses a significant threat to investment decisions and market stability. Recently, the application of Large Language Models (LLMs) for detecting financial misinformation has gained considerable attention within the natural language processing (NLP) community. The Financial Misinformation Detection (FMD) challenge @ Coling 2025 serves as a valuable platform for collaboration and innovation. This paper presents our solution to FMD challenge. Our approach involves using search engines to retrieve the summarized high-quality information as supporting evidence and designing a financial domain-specific chain-of-thought to enhance the reasoning capabilities of LLMs. We evaluated our method on both commercial closed-source LLMs (GPT-family) and open-source models (Llama-3.1-8B and QWen). The experimental results domonstrate that the proposed method improves veracity prediction performance. However, the quality of the generated explanations remains relatively poor. In the paper, we present the experimental findings and provides an in depth analysis of these results.
A Scalable Framework for Legal Text Understanding in Regulatory and Financial Contexts. (2025)

This study presents a comprehensive approach to developing a domain-specific large language model (LLM) for regulatory and financial text interpretation. A specialized corpus was constructed through large-scale scraping of financial and regulatory documents across domains such as compliance, licensing, and financial reporting. The data was preprocessed using GPT-4o-mini with prompt engineering to retain critical information and remove noise. We further pre-trained a LLaMA-3.1-8B model on the curated corpus and fine-tuned it using an instruction dataset covering nine tasks from the Coling 2025 Regulations Challenge, including acronym expansion, regulatory question-answering, and XBRL-based financial analytics, employing QLoRA to reduce memory requirements. The model exhibits a slight improvement from baseline answering complex regulatory questions (detailed QA) and expanding acronyms. This study demonstrates the potential of domain-specific LLMs in regulatory text interpretation and lays the groundwork for future research in specialized NLP evaluation methodologies.
Audit-FT at the Regulations Challenge Task: An Open-Source Large Language Model for Audit (2025)

Intelligent auditing represents a crucial advancement in modern audit practices, enhancing both the quality and efficiency of audits within the realm of artificial intelligence. With the rise of large language model (LLM), there is enormous potential for intelligent models to contribute to audit domain. However, general LLMs applied in audit domain face the challenges of lacking specialized knowledge and the presence of data biases. To overcome these challenges, this study introduces AuditWen, an open-source audit LLM by fine-tuning Qwen with constructing instruction data from audit domain. We first outline the application scenarios for LLMs in the audit and extract requirements that shape the development of LLMs tailored for audit purposes. We then propose an audit LLM, called AuditWen, by fine-tuning Qwen with constructing 30k instruction dataset from 15 audit tasks and 3 layers. In evaluation stage, we proposed a benchmark with 5k instructions that covers a set of critical audit tasks derived from the application scenarios. With the benchmark, we compare AuditWen with other existing LLMs from information extraction, question answering and document generation. The experimental results demonstrate superior performance of AuditWen both in question understanding and answer generation, making it an immediately valuable tool for audit.
FinMind-Y-Me at the Regulations Challenge Task: Financial Mind Your Meaning based on THaLLE (2025)

This paper presents our submission to the COLING 2025 regulation challenge, focusing on nine tasks in the regulatory and financial domains. The challenge aims to advance large language models beyond general-purpose capabilities, adapting them for regulatory and financial tasks using a unified framework of task-specific prompts and input templates. We propose a sequential fine-tuning approach that integrates reasoning-based training, tailored system prompts, and Chain-of-Thought (CoT) inference to optimize task-specific performance. This method improves accuracy and reliability across diverse tasks. Notably, CoT inference demonstrates exceptional effectiveness in handling complex scenarios and tasks requiring specific answer patterns, such as named entity recognition and financial calculations. Our model achieved an overall score of 54.801{\%}, ranking 1st among all teams and becoming the top performer in the challenge. These results highlight the effectiveness of sequential fine-tuning, advanced reasoning techniques, and fine-tuned prompts in improving performance and scalability for complex regulatory and financial applications.
FinNLP-FNP-LLMFinLegal-2025 Shared Task: Regulations Challenge (2025)

Financial large language models (FinLLMs) have been applied to various tasks in business, finance, accounting, and auditing. Complex financial regulations and standards are critical to financial services, which LLMs must comply with. However, FinLLMs' performance in understanding and interpreting financial regulations has rarely been studied. Therefore, we organize the Regulations Challenge, a shared task at COLING FinNLP-FNP-LLMFinLegal-2025. It encourages the academic community to explore the strengths and limitations of popular LLMs. We create 9 novel tasks and corresponding question sets. In this paper, we provide an overview of these tasks and summarize participants' approaches and results. We aim to raise awareness of FinLLMs' professional capability in financial regulations and industry standards.
IntelliChain Stars at the Regulations Challenge Task: A Large Language Model for Financial Regulation (2025)

We present our approach to the COLING-2025 Regulations Challenge, which evaluates large language models (LLMs) on nine regulatory tasks, such as abbreviation recognition and financial data extraction. To address challenges like domain-specific terminologies and dynamic regulatory contexts, we developed a robust data construction pipeline, integrating proprietary Chinese regulatory data, Fin-GPT datasets, and financial Q{\&}A data. The pipeline applied, but was not limited to, language filtering, semantic screening, and deduplication, resulting in a 30,000-example dataset combining financial regulations and general financial data. Using this dataset, we fine-tuned Llama 3.2-3B-Instruct to create Reg-LLaMA, a specialized model that outperformed baselines on the Regulations Challenge and PIXIU datasets. These results demonstrate the effectiveness of domain-specific data construction in advancing LLMs for regulatory tasks, paving the way for reliable and interpretable AI in regulated industries.
Fin-DBQA Shared-task: Database Querying and Reasoning (2025)

This paper presents the results of the Fin-DBQA shared task based on a question-answering dataset, focusing on database querying and reasoning. The dataset, consisting of 400 questions grouped into 40 conversations, evaluates language models' abilities to answer sequential questions with complex reasoning and multi-hop queries in a multi-turn conversational question-answering setting. Each sample includes the question, answer, database queries, querying result (tables), and a program (series of operations) that produces the answer from the result. We received 52 submissions from three participants, with scores significantly surpassing the baselines. One participant submitted a paper detailing a prompt-based solution using large language models with additional data preprocessing that helps improve the overall performance.
Adapt LLM for Multi-turn Reasoning QA using Tidy Data (2025)

This paper presents our submission to the Fin-DBQA shared task at the 9th FinNLP workshop. The task involves answering finance-focused questions in a multi-turn environment, requiring step-by-step reasoning and Python code generation. We propose a novel approach to tackle this multidimensional problem by pre-processing the data into tidy data format so that each column represents a variable and each row an observation. Our experiments demonstrate that using the tidy data format allows all models to surpass SOTA, with GPT-4o achieving a 50.62{\%} accuracy on the DBQR-QA benchmark achieving second place on the shared task leaderboard. These findings suggest that transforming data into the tidy data format enhances reasoning capabilities, reduces syntax errors, and improves performance on table-reasoning QA tasks. The code is available online.
FinNLP-FNP-LLMFinLegal @ COLING 2025 Shared Task: Agent-Based Single Cryptocurrency Trading Challenge (2025)

Despite the promise of large language models based agent framework in stock trading task, their capabilities for comprehensive analysis and multiple different financial assets remain largely unexplored, such as cryptocurrency trading. To evaluate the capabilities of LLM-based agent framework in cryptocurrency trading, we introduce an LLMs-based financial shared task featured at COLING 2025 FinNLP-FNP-LLMFinLegal workshop, named Agent-based Single Cryptocurrency Trading Challenge. This challenge includes two cryptocurrencies: BitCoin and Ethereum. In this paper, we provide an overview of these tasks and datasets, summarize participants' methods, and present their experimental evaluations, highlighting the effectiveness of LLMs in addressing cryptocurrency trading challenges. To the best of our knowledge, the Agent-based Single Cryptocurrency Trading Challenge is one of the first challenges for assessing LLMs in the financial area. In consequence, we provide detailed observations and take away conclusions for future development in this area.
Sam`s Fans at the Crypto Trading Challenge Task: A Threshold-Based Decision Approach Based on FinMem Framework (2025)

The advancements of large language models (LLMs) demonstrate the value of pre-training on diverse datasets, enabling these models to excel across a wide range of tasks while adapting effectively to specialized applications. This study presents an approach to enhance LLMs' ability to process and trade based on cryptocurrency data across different time horizons. We fine-tuned two established language models, Llama-3.1-8b and Qwen2.5-7b, to effectively interpret and utilize temporal market data provided by the FinMem framework. Our methodology enables these models to analyze multi-period market data from FinMem, including price movements and momentum indicators, to execute effective cryptocurrency trading decisions. Results show that this fine-tuning approach improves the models' capacity to analyze market conditions and inform trading decisions based on multi-period market dynamics.
300k/ns team at the Crypto Trading Challenge Task: Enhancing the justification of accurate trading decisions through parameter-efficient fine-tuning of reasoning models (2025)

In this paper, we address the Agent-Based Sin- gle Cryptocurrency Trading Challenge, focus- ing on decision-making for trading Bitcoin and Etherium. Our approach utilizes fine- tuning a Mistral AI model on a dataset com- prising summarized cryptocurrency news, en- abling it to make informed {\textquotedblleft}buy,{\textquotedblright} {\textquotedblleft}sell,{\textquotedblright} or {\textquotedblleft}hold{\textquotedblright} decisions and articulate its reasoning. The model integrates textual sentiment analysis and contextual reasoning with real-time mar- ket trends, demonstrating the potential of Large Language Models (LLMs) in high-stakes finan- cial decision-making. The model achieved a notable accuracy, highlighting its capacity to manage risk while optimizing returns. This work contributes to advancing AI-driven so- lutions for cryptocurrency markets and offers insights into the practical deployment of LLMs in real-time trading environments. We made our model publicly available.
DiffZOO: A Purely Query-Based Black-Box Attack for Red-teaming Text-to-Image Generative Model via Zeroth Order Optimization (2025)

Current text-to-image (T2I) synthesis diffusion models raise misuse concerns, particularly in creating prohibited or not-safe-for-work (NSFW) images. To address this, various safety mechanisms and red teaming attack methods are proposed to enhance or expose the T2I model`s capability to generate unsuitable content. However, many red teaming attack methods assume knowledge of the text encoders, limiting their practical usage. In this work, we rethink the case of purely black-box attacks without prior knowledge of the T2l model. To overcome the unavailability of gradients and the inability to optimize attacks within a discrete prompt space, we propose DiffZOO which applies Zeroth Order Optimization to procure gradient approximations and harnesses both C-PRV and D-PRV to enhance attack prompts within the discrete prompt domain. We evaluated our method across multiple safety mechanisms of the T2I diffusion model and online servers. Experiments on multiple state-of-the-art safety mechanisms show that DiffZOO attains an 8.5{\%} higher average attack success rate than previous works, hence its promise as a practical red teaming tool for T2l models.
MedOdyssey: A Medical Domain Benchmark for Long Context Evaluation Up to 200K Tokens (2025)

Numerous advanced Large Language Models (LLMs) now support context lengths up to 128K, and some extend to 200K. Some benchmarks in the generic domain have also followed up on evaluating long-context capabilities. In the medical domain, tasks are distinctive due to the unique contexts and need for domain expertise, necessitating further evaluation. However, despite the frequent presence of long texts in medical scenarios, evaluation benchmarks of long-context capabilities for LLMs in this field are still rare. In this paper, we propose MedOdyssey, the first medical long-context benchmark with seven length levels ranging from 4K to 200K tokens. MedOdyssey consists of two primary components: the medical-context {\textquotedblleft}needles in a haystack{\textquotedblright} task and a series of tasks specific to medical applications, together comprising 10 datasets. The first component includes challenges such as counter-intuitive reasoning and novel (unknown) facts injection to mitigate knowledge leakage and data contamination of LLMs. The second component confronts the challenge of requiring professional medical expertise. Especially, we design the {\textquoteleft}{\textquotedblleft}Maximum Identical Context{\textquotedblright} principle to improve fairness by guaranteeing that different LLMs observe as many identical contexts as possible. Our experiment evaluates advanced proprietary and open-source LLMs tailored for processing long contexts and presents detailed performance analyses. This highlights that LLMs still face challenges and need for further research in this area. Our code and data are released in the repository: \url{https://github.com/JOHNNY-fans/MedOdyssey}.
Can LLMs Learn Macroeconomic Narratives from Social Media? (2025)

This study empirically tests the $Narrative Economics$ hypothesis, which posits that narratives (ideas that are spread virally and affect public beliefs) can influence economic fluctuations. We introduce two curated datasets containing posts from X (formerly Twitter) which capture economy-related narratives (Data will be shared upon paper acceptance). Employing Natural Language Processing (NLP) methods, we extract and summarize narratives from the tweets. We test their predictive power for $macroeconomic$ forecasting by incorporating the tweets' or the extracted narratives' representations in downstream financial prediction tasks. Our work highlights the challenges in improving macroeconomic models with narrative data, paving the way for the research community to realistically address this important challenge. From a scientific perspective, our investigation offers valuable insights and NLP tools for narrative extraction and summarization using Large Language Models (LLMs), contributing to future research on the role of narratives in economics.
Code-Optimise: Self-Generated Preference Data for Correctness and Efficiency (2025)

Code Language Models have been trained togenerate accurate solutions, typically with noregard for runtime. On the other hand, previousworks that explored execution optimisationhave observed corresponding drops infunctional correctness. To that end, we introduceCode-Optimise, a framework that incorporatesboth correctness (passed, failed) andruntime (quick, slow) as learning signals viaself-generated preference data. Our frameworkis both lightweight and robust as it dynamicallyselects solutions to reduce overfitting whileavoiding a reliance on larger models for learningsignals. Code-Optimise achieves significantimprovements in pass@k while decreasingthe competitive baseline runtimes by anadditional 6{\%} for in-domain data and up to3{\%} for out-of-domain data. As a by-product,the average length of the generated solutionsis reduced by up to 48{\%} on MBPP and 23{\%}on HumanEval, resulting in faster and cheaperinference. The generated data and codebaseis open-sourced at https://github.com/huawei-noah/HEBO/tree/Code{\_}Optimise.
People will agree what I think: Investigating LLM`s False Consensus Effect (2025)

Large Language Models (LLMs) have been recently adopted in interactive systems requiring communication. As the false belief in a model can harm the usability of such systems, LLMs should not have cognitive biases that humans have. Psychologists especially focus on the False Consensus Effect (FCE), a cognitive bias where individuals overestimate the extent to which others share their beliefs or behaviors, because FCE can distract smooth communication by posing false beliefs. However, previous studies have less examined FCE in LLMs thoroughly, which needs more consideration of confounding biases, general situations, and prompt changes. Therefore, in this paper, we conduct two studies to examine the FCE phenomenon in LLMs. In Study 1, we investigate whether LLMs have FCE. In Study 2, we explore how various prompting styles affect the demonstration of FCE. As a result of these studies, we identified that popular LLMs have FCE. Also, the result specifies the conditions when FCE becomes more or less prevalent compared to normal usage.
LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain (2025)

Instruction tuning is an important step in making language models useful for direct user interaction. However, the legal domain is underrepresented in typical instruction datasets (e.g., only 10 out of 1600+ tasks in Super-NaturalInstructions). To study whether instruction tuning on legal datasets is necessary for strong legal reasoning, we aggregate 58 annotated legal datasets and write instructions for each, creating LawInstruct. LawInstruct covers 17 global jurisdictions, 24 languages and a total of 12M examples across diverse tasks such as legal QA, summarization of court cases, and legal argument mining. We evaluate our models on LegalBench, measuring legal reasoning across five categories in 162 challenging and realistic legal tasks, and MMLU, to measure potential drops in general reasoning capabilities. We find that legal-specific instruction tuning on Flan-T5 {--} yielding FLawN-T5 {--} improves performance on LegalBench across all model sizes, with an aggregate increase of 15 points or 50{\%} over Flan-T5 for the base size. No model size shows performance drops in MMLU. We publish LawInstruct as a resource for further study of instruction tuning in the legal domain.
Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations (2025)

In the rapidly evolving field of natural language processing, dialogue systems primarily employ a single-step dialogue paradigm. Although this paradigm is commonly adopted, it lacks the depth and fluidity of human interactions and does not appear natural. We introduce a novel **Step**-by-Step Dialogue Paradigm (Stephanie), designed to mimic the ongoing dynamic nature of human conversations. By employing a dual learning strategy and a further-split post-editing method, we generated and utilized a high-quality step-by-step dialogue dataset to fine-tune existing large language models, enabling them to perform step-by-step dialogues. We thoroughly present Stephanie. Tailored automatic and human evaluations are conducted to assess its effectiveness compared to the traditional single-step dialogue paradigm. We will release code, Stephanie datasets, and Stephanie LLMs to facilitate the future of chatbot eras.
ConShift: Sense-based Language Variation Analysis using Flexible Alignment (2025)

We introduce ConShift, a family of alignment-based algorithms that enable semantic variation analysis at the sense-level. Using independent senses of words induced from the context of tokens in two corpora, sense-enriched word embeddings are aligned using self-supervision and a flexible matching mechanism. This approach makes it possible to test for multiple sense-level language variations such as sense gain/presence, loss/absence and broadening/narrowing, while providing explanation of the changes through visualization of related concepts. We illustrate the utility of the method with sense- and word-level semantic shift detection results for multiple evaluation datasets in diachronic settings and dialect variation in the synchronic setting.
ToVo: Toxicity Taxonomy via Voting (2025)

Existing toxic detection models face significant limitations, such as lack of transparency, customization, and reproducibility. These challenges stem from the closed-source nature of their training data and the paucity of explanations for their evaluation mechanism. To address these issues, we propose a dataset creation mechanism that integrates voting and chain-of-thought processes, producing a high-quality open-source dataset for toxic content detection. Our methodology ensures diverse classification metrics for each sample and includes both classification scores and explanatory reasoning for the classifications.We utilize the dataset created through our proposed mechanism to train our model, which is then compared against existing widely-used detectors. Our approach not only enhances transparency and customizability but also facilitates better fine-tuning for specific use cases. This work contributes a robust framework for developing toxic content detection models, emphasizing openness and adaptability, thus paving the way for more effective and user-specific content moderation solutions.