ymoslem/acl-abstracts huggingface dataset:
The Only Way is Ethics: A Guide to Ethical Research with Large Language Models (2025)

There is a significant body of work looking at the ethical considerations of large language models (LLMs): critiquing tools to measure performance and harms; proposing toolkits to aid in ideation; discussing the risks to workers; considering legislation around privacy and security etc. As yet there is no work that integrates these resources into a single practical guide that focuses on LLMs; we attempt this ambitious goal. We introduce LLM Ethics Whitepaper, which we provide as an open and living resource for NLP practitioners, and those tasked with evaluating the ethical implications of others' work. Our goal is to translate ethics literature into concrete recommendations for computer scientists. LLM Ethics Whitepaper distils a thorough literature review into clear Do`s and Don`ts, which we present also in this paper. We likewise identify useful toolkits to support ethical work. We refer the interested reader to the full LLM Ethics Whitepaper, which provides a succinct discussion of ethical considerations at each stage in a project lifecycle, as well as citations for the hundreds of papers from which we drew our recommendations. The present paper can be thought of as a pocket guide to conducting ethical research with LLMs.
Should We Use a Fixed Embedding Size? Customized Dimension Sizes for Knowledge Graph Embedding (2025)

Knowledge Graph Embedding (KGE) aims to project entities and relations into a low-dimensional space, so as to enable Knowledge Graphs (KGs) to be effectively used by downstream AI tasks. Most existing KGs (e.g. Wikidata) suffer from the data imbalance issue, i.e., the occurrence frequencies vary significantly among different entities. Current KGE models use a fixed embedding size, leading to overfitting for low-frequency entities and underfitting for high-frequency ones. A simple method is to manually set embedding sizes based on frequency, but this is not feasible due to the complexity and the large number of entities. To this end, we propose CustomizE, which customizes embedding sizes in a data-driven way, assigning larger sizes for high-frequency entities and smaller sizes for low-frequency ones. We use bilevel optimization for stable learning of representations and sizes. It is noteworthy that our framework is universal and flexible, which is suitable for various KGE models. Experiments on link prediction tasks show its superiority over state-of-the-art baselines.
Chinese Automatic Readability Assessment Using Adaptive Pre-training and Linguistic Feature Fusion (2025)

Chinese Automatic Readability Assessment (ARA) aims to classify the reading difficulty of Chinese texts. To address the issues of insufficient high-quality training data and underutilization of linguistic features in existing methods, we propose a method that combines adaptive pre-training with feature fusion based on an interactive attention mechanism. First, we enhance the model`s ability to capture different text difficulties through domain- and task-specific adaptive pre-training. Then, we propose an Adaptive Task-guided Corpus Filtering (ATCF) method, utilizing embeddings generated by the pre-trained model and applying nearest-neighbor search along with a sample balancing mechanism to ensure comprehensive learning across various difficulty levels. Finally, we propose an Interactive Attention-Driven Feature Fusion method that integrates linguistic and deep features, providing rich difficulty information to the model. Experiments on Chinese textbook dataset demonstrate that our method achieves state-of-the-art (SOTA) performance. Transfer learning experiments further indicate that our approach generalizes well to extracurricular reading and Chinese as a Foreign Language (CFL) ARA tasks.
Multitask-Bench: Unveiling and Mitigating Safety Gaps in LLMs Fine-tuning (2025)

Recent breakthroughs in Large Language Models (LLMs) have led to their adoption across a wide range of tasks, ranging from code generation to machine translation and sentiment analysis, etc. Red teaming/Safety alignment efforts show that fine-tuning models on benign (non-harmful) data could compromise safety. However, it remains unclear to what extent this phenomenon is influenced by different variables, including fine-tuning task, model calibrations, etc. This paper explores the task-wise safety degradation due to fine-tuning on downstream tasks such as summarization, code generation, translation, and classification across various calibration. Our results reveal that: 1) Fine-tuning LLMs for code generation and translation leads to the highest degradation in safety guardrails. 2) LLMs generally have weaker guardrails for translation and classification, with 73-92{\%} of harmful prompts answered, across baseline and other calibrations, falling into one of two concern categories. 3) Current solutions, including guards and safety tuning datasets, lack cross-task robustness. To address these issues, we developed a new multitask safety dataset effectively reducing attack success rates across a range of tasks without compromising the model`s overall helpfulness. Our work underscores the need for generalized alignment measures to ensure safer and more robust models.
Unmasking the Imposters: How Censorship and Domain Adaptation Affect the Detection of Machine-Generated Tweets (2025)

The rapid development of large language models (LLMs) has significantly improved the generation of fluent and convincing text, raising concerns about their potential misuse on social media platforms. We present a comprehensive methodology for creating nine Twitter datasets to examine the generative capabilities of four prominent LLMs: Llama 3, Mistral, Qwen2, and GPT4o. These datasets encompass four censored and five uncensored model configurations, including 7B and 8B parameter base-instruction models of the three open-source LLMs. Additionally, we perform a data quality analysis to assess the characteristics of textual outputs from human, {\textquotedblleft}censored,{\textquotedblright} and {\textquotedblleft}uncensored models,{\textquotedblright} employing semantic meaning, lexical richness, structural patterns, content characteristics, and detector performance metrics to identify differences and similarities. Our evaluation demonstrates that {\textquotedblleft}uncensored{\textquotedblright} models significantly undermine the effectiveness of automated detection methods. This study addresses a critical gap by exploring smaller open-source models and the ramifications of {\textquotedblleft}uncensoring,{\textquotedblright} providing valuable insights into how domain adaptation and content moderation strategies influence both the detectability and structural characteristics of machine-generated text.
Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning (2025)

This paper focuses on sarcasm detection, which aims to identify whether given statements convey criticism, mockery, or other negative sentiment opposite to the literal meaning. To detect sarcasm, humans often require a comprehensive understanding of the semantics in the statement and even resort to external commonsense to infer the fine-grained incongruity. However, existing methods lack commonsense inferential ability when they face complex real-world scenarios, leading to unsatisfactory performance. To address this problem, we propose a novel framework for sarcasm detection, which conducts incongruity reasoning based on commonsense augmentation, called EICR. Concretely, we first employ retrieval-augmented large language models to supplement the missing but indispensable commonsense background knowledge. To capture complex contextual associations, we construct a dependency graph and obtain the optimized topology via graph refinement. We further introduce an adaptive reasoning skeleton that integrates prior rules to extract sentiment-inconsistent subgraphs explicitly. To eliminate the possible spurious relations between words and labels, we employ adversarial contrastive learning to enhance the robustness of the detector. Experiments conducted on five datasets demonstrate the effectiveness of EICR.
Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning (2025)

Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks. Advances in prompt engineering and fine-tuning techniques have further enhanced their ability to address complex reasoning challenges. However, these advanced capabilities are often exclusive to models exceeding 100 billion parameters. Although Chain-of-Thought (CoT) fine-tuning methods have been explored for smaller models (under 10 billion parameters), they typically depend on extensive CoT training data, which can introduce inconsistencies and limit effectiveness in low-data settings. To overcome these limitations, this paper introduce a new reasoning strategy Solution Guidance (SG) and a plug-and-play training paradigm Solution-Guidance Fine-Tuning (SGFT) for enhancing the reasoning capabilities of small language models. SG focuses on problem understanding and decomposition at the semantic and logical levels, rather than specific computations, which can effectively improve the SLMs' generalization and reasoning abilities. With only a small amount of SG training data, SGFT can fine-tune a SLM to produce accurate problem-solving guidances, which can then be flexibly fed to any SLM as prompts, enabling it to generate correct answers directly. Experimental results demonstrate that our method significantly improves the performance of SLMs on various reasoning tasks, enhancing both their practicality and efficiency within resource-constrained environments.
LOG: A Local-to-Global Optimization Approach for Retrieval-based Explainable Multi-Hop Question Answering (2025)

Multi-hop question answering (MHQA) aims to utilize multi-source intensive documents retrieved to derive the answer. However, it is very challenging to model the importance of knowledge retrieved. Previous approaches primarily emphasize single-step and multi-step iterative decomposition or retrieval, which are susceptible to failure in long-chain reasoning due to the progressive accumulation of erroneous information. To address this problem, we propose a novel Local-tO-Global optimized retrieval method (LOG) to discover more beneficial information, facilitating the MHQA. In particular, we design a pointwise conditional v-information based local information modeling to cover usable documents with reasoning knowledge. We also improve tuplet objective loss, advancing multi-examples-aware global optimization to model the relationship between scattered documents. Extensive experimental results demonstrate our proposed method outperforms prior state-of-the-art models, and it can significantly improve multi-hop reasoning, notably for long-chain reasoning.
KG-TRICK: Unifying Textual and Relational Information Completion of Knowledge for Multilingual Knowledge Graphs (2025)

Multilingual knowledge graphs (KGs) provide high-quality relational and textual information for various NLP applications, but they are often incomplete, especially in non-English languages. Previous research has shown that combining information from KGs in different languages aids either Knowledge Graph Completion (KGC), the task of predicting missing relations between entities, or Knowledge Graph Enhancement (KGE), the task of predicting missing textual information for entities. Although previous efforts have considered KGC and KGE as independent tasks, we hypothesize that they are interdependent and mutually beneficial. To this end, we introduce KG-TRICK, a novel sequence-to-sequence framework that unifies the tasks of textual and relational information completion for multilingual KGs. KG-TRICK demonstrates that: i) it is possible to unify the tasks of KGC and KGE into a single framework, and ii) combining textual information from multiple languages is beneficial to improve the completeness of a KG. As part of our contributions, we also introduce WikiKGE10++, the largest manually-curated benchmark for textual information completion of KGs, which features over 25,000 entities across 10 diverse languages.
Impromptu Cybercrime Euphemism Detection (2025)

Detecting euphemisms is essential for content security on various social media platforms, but existing methods designed for detecting euphemisms are ineffective in impromptu euphemisms. In this work, we make a first attempt to an exploration of impromptu euphemism detection and introduce the Impromptu Cybercrime Euphemisms Detection (ICED) dataset. Moreover, we propose a detection framework tailored to this problem, which employs context augmentation modeling and multi-round iterative training. Our detection framework mainly consists of a coarse-grained and a fine-grained classification model. The coarse-grained classification model removes most of the harmless content in the corpus to be detected. The fine-grained model, impromptu euphemisms detector, integrates context augmentation and multi-round iterations training to better predicts the actual meaning of a masked token. In addition, we leverage ChatGPT to evaluate the mode`s capability. Experimental results demonstrate that our approach achieves a remarkable 76-fold improvement compared to the previous state-of-the-art euphemism detector.
ALIS: Aligned LLM Instruction Security Strategy for Unsafe Input Prompt (2025)

In large language models, existing instruction tuning methods may fail to balance the performance with robustness against attacks from user input like prompt injection and jailbreaking. Inspired by computer hardware and operating systems, we propose an instruction tuning paradigm named Aligned LLM Instruction Security Strategy (ALIS) to enhance model performance by decomposing user inputs into irreducible atomic instructions and organizing them into instruction streams which will guide the response generation of model. ALIS is a hierarchical structure, in which user inputs and system prompts are treated as user and kernel mode instructions respectively. Based on ALIS, the model can maintain security constraints by ignoring or rejecting the input instructions when user mode instructions attempt to conflict with kernel mode instructions. To build ALIS, we also develop an automatic instruction generation method for training ALIS, and give one instruction decomposition task and respective datasets. Notably, the ALIS framework with a small model to generate instruction streams still improve the resilience of LLM to attacks substantially without any lose on general capabilities.
ProTOD: Proactive Task-oriented Dialogue System Based on Large Language Model (2025)

Large Language Model (LLM)-based Task-Oriented Dialogue (TOD) systems show promising performance in helping users achieve specific goals in a zero-shot setting. However, existing systems engage with users in a reactive manner, relying on a basic single-query mechanism with the knowledge base and employing passive policy planning. The proactive TOD systems, which can provide potentially helpful information and plan cross-domain multi-task dialogue policies, have not been well studied. In addition, effective evaluation methods are also lacking. To address these issues, we propose ProTOD, a novel LLM-based proactive TOD framework designed to improve system proactivity and goal completion. First, we design an adaptive exploratory retrieval mechanism to dynamically navigate domain knowledge. Second, we introduce a two-stage passive-to-proactive policy planner that effectively organizes knowledge and actions relationship. Finally, we develop two distinct user simulators with different personalities to simulate real-world interactions and propose a new error measure called Human-targeted Policy Edit Rate (HPER) for evaluation. Experimental results show that ProTOD achieves state-of-the-art (SOTA) performance, improving goal completion rates by 10{\%} while significantly enhancing the proactive engagement.
Towards Multilingual spoken Visual Question Answering system using Cross-Attention (2025)

Visual question answering (VQA) poses a multi-modal translation challenge that requires the analysis of both images and questions simultaneously to generate appropriate responses. Although VQA research has mainly focused on text-based questions in English, speech-based questions in English and other languages remain largely unexplored. Incorporating speech could significantly enhance the utility of VQA systems, as speech is the primary mode of human communication. To address this gap, this work implements a speech-based VQA system and introduces the textless multilingual visual question answering (TM-VQA) dataset, featuring speech-based questions in English, German, Spanish, and French. This TM-VQA dataset contains 658,111 pairs of speech-based questions and answers based on 123,287 images. Finally, a novel, cross-attention-based unified multi-modal framework is presented to evaluate the efficacy of the TM-VQA dataset. The experimental results indicate the effectiveness of the proposed unified approach over the cascaded framework for both text and speech-based VQA systems. Dataset can be accessed at https://github.com/Synaptic-Coder/TM-VQA.
Detecting Conversational Mental Manipulation with Intent-Aware Prompting (2025)

Mental manipulation severely undermines mental wellness by covertly and negatively distorting decision-making. While there is an increasing interest in mental health care within the natural language processing community, progress in tackling manipulation remains limited due to the complexity of detecting subtle, covert tactics in conversations. In this paper, we propose Intent-Aware Prompting (IAP), a novel approach for detecting mental manipulations using large language models (LLMs), providing a deeper understanding of manipulative tactics by capturing the underlying intents of participants. Experimental results on the MentalManip dataset demonstrate superior effectiveness of IAP against other advanced prompting strategies. Notably, our approach substantially reduces false negatives, helping detect more instances of mental manipulation with minimal misjudgment of positive cases. The code of this paper is available at https://github.com/Anton-Jiayuan-MA/Manip-IAP.
MIGRATE: Cross-Lingual Adaptation of Domain-Specific LLMs through Code-Switching and Embedding Transfer (2025)

Large Language Models (LLMs) have rapidly advanced, with domain-specific expert models emerging to handle specialized tasks across various fields. However, the predominant focus on English-centric models demands extensive data, making it challenging to develop comparable models for middle and low-resource languages. To address this limitation, we introduce Migrate, a novel method that leverages open-source static embedding models and up to 3 million tokens of code-switching data to facilitate the seamless transfer of embeddings to target languages. Migrate enables effective cross-lingual adaptation without requiring large-scale domain-specific corpora in the target language, promoting the accessibility of expert LLMs to a diverse range of linguistic communities. Our experimental results demonstrate that Migrate significantly enhances model performance in target languages, outperforming baseline and existing cross-lingual transfer methods. This approach provides a practical and efficient solution for extending the capabilities of domain-specific expert models.
CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving (2025)

Code-switching is a widely prevalent linguistic phenomenon in multilingual societies like India. Building speech-to-text models for code-switched speech is challenging due to limited availability of datasets. In this work, we focus on the problem of spoken translation (ST) of code-switched speech in Indian languages to English text. We present a new end-to-end model architecture CoSTA that scaffolds on pretrained automatic speech recognition (ASR) and machine translation (MT) modules (that are more widely available for many languages). Speech and ASR text representations are fused using an aligned interleaving scheme and are fed further as input to a pretrained MT module; the whole pipeline is then trained end-to-end for spoken translation using synthetically created ST data. We also release a new evaluation benchmark for code-switched Bengali- English, Hindi-English, Marathi-English and Telugu-English speech to English text. CoSTA significantly outperforms many competitive cascaded and end-to-end multimodal baselines by up to 3.5 BLEU points.
Bridging the Language Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs (2025)

Large language models (LLMs) have revolutionized various domains but still struggle with non-Latin scripts and low-resource languages. This paper addresses the critical challenge of improving multilingual performance without extensive fine-tuning. We introduce a novel dynamic learning approach that optimizes prompt strategy, embedding model, and LLM per query at runtime. By adapting configurations dynamically, our method achieves significant improvements over static, best and random baselines. It operates efficiently in both offline and online settings, generalizing seamlessly across new languages and datasets. Leveraging Retrieval-Augmented Generation (RAG) with state-of-the-art multilingual embeddings, we achieve superior task performance across diverse linguistic contexts. Through systematic investigation and evaluation across18 diverse languages using popular question-answering (QA) datasets we show our approach results in 10-15{\%} improvements in multilingual performance over pre-trained models and 4x gains compared to fine-tuned, language-specific models.
Poetry in Pixels: Prompt Tuning for Poem Image Generation via Diffusion Models (2025)

The task of text-to-image generation has encountered significant challenges when applied to literary works, especially poetry. Poems are a distinct form of literature, with meanings that frequently transcend beyond the literal words. To address this shortcoming, we propose a PoemToPixel framework designed to generate images that visually represent the inherent meanings of poems. Our approach incorporates the concept of prompt tuning in our image generation framework to ensure that the resulting images closely align with the poetic content. In addition, we propose the PoeKey algorithm, which extracts three key elements in the form of emotions, visual elements, and themes from poems to form instructions which are subsequently provided to a diffusion model for generating corresponding images. Furthermore, to expand the diversity of the poetry dataset across different genres and ages, we introduce MiniPo, a novel multimodal dataset comprising 1001 children`s poems and images. Leveraging this dataset alongside PoemSum, we conducted both quantitative and qualitative evaluations of image generation using our PoemToPixel framework. This paper demonstrates the effectiveness of our approach and offers a fresh perspective on generating images from literary sources. The code and dataset used in this work are publicly available.
Argumentation and Domain Discourse in Scholarly Articles on the Theory of International Relations (2025)

We present the first dataset, an annotation scheme, discourse analysis, and baseline experiments on argumentation and domain content types in scholarly articles on political science, specifically on the theory of International Relations (IR). The dataset comprises over 1 600 sentences stemming from three foundational articles on Neo-Realism, Liberalism, and Constructivism. We show that our annotation scheme enables educationally-relevant insight into the scholarly IR discourse and that state-of-the-art classifiers, while effective in distinguishing basic argumentative elements (Claims and Support/Attack relations) reaching up to 0.97 micro F1 , require domain-specific training and fine-tuning on the more fine-grained tasks of relation and content type prediction.