ymoslem/acl-abstracts huggingface dataset:
Adaptive Retrieval-Augmented Generation for Conversational Systems (2025)

With the success of integrating large language models into the development of conversational systems, many studies have shown the effectiveness of retrieving and augmenting external knowledge for informative responses. While many existing studies agree on the necessity of Retrieval Augmented Generation (RAG), further investigation into the necessity and value of applying RAG to every turn of the conversation is needed. In this study, we propose to investigate the need for each turn of system response to be augmented with external knowledge. In particular, by leveraging human judgements on the binary choice of adaptive augmentation, we develop RAGate, a gating model, which models conversation context and relevant inputs to predict if a conversational system requires RAG for improved responses. We conduct extensive experiments on devising and applying RAGate to conversational models, joined with well-rounded analyses of various conversational scenarios. Our experimental results and analysis indicate the effective application of RAGate in RAG-based conversational systems in identifying if system responses require RAG to generate high-quality responses with high confidence. This study also identifies and shows the correlation between the generation`s confidence level and the relevance of the augmented knowledge. We have also released the implementation code and resources in https://github.com/wangxieric/RAGate.
Multimodal Generation with Consistency Transferring (2025)

Multimodal content generation has become an area of considerable interest. However, existing methods are hindered by limitations related to model constraints and training strategies: (1) Most current approaches rely on training models from scratch, resulting in inefficient training processes when extending these models; (2) There is a lack of constraints on adjacent steps within the models, leading to slow sampling and poor generation stability across various sampling methods. To address the issues, we introduce Multimodal Generation with Consistency Transferring (MGCT). The method introduces two key improvements: (1) A Model Consistency Transferring (MCT) strategy to acquire low-cost prior knowledge, increasing training efficiency and avoiding error accumulation; (2) A Layer Consistency Transferring (LCT) between adjacent steps, enhancing denoising capabilities at each step and improving model stability across various generation methods. These strategies ensure the consistency of jointly generated multimodal content and improving training efficiency. Experiments show that the algorithm enhances the model`s ability to capture actions and depict backgrounds more effectively. In both the AIST++ and Landscape datasets, it improves video generation speed by approximately 40{\%} and quality by about 39.3{\%}, while also achieving a slight 3{\%} improvement in audio quality over the baseline.
On the Impact of Noise in Differentially Private Text Rewriting (2025)

The field of text privatization often leverages the notion of *Differential Privacy* (DP) to provide formal guarantees in the rewriting or obfuscation of sensitive textual data. A common and nearly ubiquitous form of DP application necessitates the addition of calibrated noise to vector representations of text, either at the data- or model-level, which is governed by the privacy parameter $\varepsilon$. However, noise addition almost undoubtedly leads to considerable utility loss, thereby highlighting one major drawback of DP in NLP. In this work, we introduce a new sentence infilling privatization technique, and we use this method to explore the effect of noise in DP text rewriting. We empirically demonstrate that non-DP privatization techniques excel in utility preservation and can find an acceptable empirical privacy-utility trade-off, yet cannot outperform DP methods in empirical privacy protections. Our results highlight the significant impact of noise in current DP rewriting mechanisms, leading to a discussion of the merits and challenges of DP in NLP as well as the opportunities that non-DP methods present.
Teaching Large Language Models Number-Focused Headline Generation With Key Element Rationales (2025)

Number-focused headline generation is a summarization task requiring both high textual quality and precise numerical accuracy, which poses a unique challenge for Large Language Models (LLMs). Existing studies in the literature focus only on either textual quality or numerical reasoning and thus are inadequate to address this challenge. In this paper, we propose a novel chain-of-thought framework for using rationales comprising key elements of the Topic, Entities, and Numerical reasoning (TEN) in news articles to enhance the capability for LLMs to generate topic-aligned high-quality texts with precise numerical accuracy. Specifically, a teacher LLM is employed to generate TEN rationales as supervision data, which are then used to teach and fine-tune a student LLM. Our approach teaches the student LLM automatic generation of rationales with enhanced capability for numerical reasoning and topic-aligned numerical headline generation. Experiments show that our approach achieves superior performance in both textual quality and numerical accuracy.
Zero-Shot Strategies for Length-Controllable Summarization (2025)

Large language models (LLMs) struggle with precise length control, particularly in zero-shot settings. We conduct a comprehensive study evaluating LLMs' length control capabilities across multiple measures and propose practical methods to improve controllability. Our experiments with LLaMA 3 reveal stark differences in length adherence across measures and highlight inherent biases of the model. To address these challenges, we introduce a set of methods: length approximation, target adjustment, sample filtering, and automated revisions. By combining these methods, we demonstrate substantial improvements in length compliance while maintaining or enhancing summary quality, providing highly effective zero-shot strategies for precise length control without the need for model fine-tuning or architectural changes. With our work, we not only advance our understanding of LLM behavior in controlled text generation but also pave the way for more reliable and adaptable summarization systems in real-world applications.
SIMPLOT: Enhancing Chart Question Answering by Distilling Essentials (2025)

Recently, interpreting complex charts with logical reasoning has emerged as challenges due to the development of vision-language models. A prior state-of-the-art (SOTA) model has presented an end-to-end method that leverages the vision-language model to convert charts into table format utilizing Large Language Model (LLM) for reasoning. However, unlike natural images, charts contain a mix of essential and irrelevant information required for chart reasoning, and we discover that this characteristic can lower the performance of chart-to-table extraction. In this paper, we introduce SIMPLOT, a method designed to extract only the elements necessary for chart reasoning. The proposed method involves two steps: 1) training to mimic a simple plot that contains only the essential information from a complex chart for table extraction, followed by 2) performing reasoning based on the table. Our model enables accurate chart reasoning without the need for additional annotations or datasets, and its effectiveness is demonstrated through various experiments.
InstructAny2Pix: Image Editing with Multi-Modal Prompts (2025)

Image Editing has made incredible progress in recent years. Earliest work only supported caption-guided editing. Recently, free-form text instructions and reference images are incorporated to allow more flexibility. However, existing methods still struggle with complicated editing instructions involving multiple objects or reference images. We present InstructAny2Pix, a novel image editing model that leverages a multi-modal LLM to execute complicated edit instructions. Compared with previous, works, InstructAny2Pix extends the flexibility of edit instructions in three ways: First, it can perform complex instructions involving multiple object edits; Second, it supports interleaving text instructions with multiple reference images; Third, it supports audio and music inputs as part of edit prompts, unlocking many creative applications, such as album cover generation and music-inspired merchandise design. To evaluate the effectiveness of InstructAny2Pix, we propose two new benchmark datasets MM-Inst and Dream-booth++ consisting of human written, multi-modal prompts. InstructAny2Pix outperforms baselines in these two proposed multi-modal benchmarks, as well as conventional image editing benchmarks such as InstructPix2Pix.
Lost in Overlap: Exploring Logit-based Watermark Collision in LLMs (2025)

The proliferation of large language models (LLMs) in generating content raises concerns about text copyright. Watermarking methods, particularly logit-based approaches, embed imperceptible identifiers into text to address these challenges. However, the widespread usage of watermarking across diverse LLMs has led to an inevitable issue known as watermark collision during common tasks, such as paraphrasing or translation.In this paper, we introduce watermark collision as a novel and general philosophy for watermark attacks, aimed at enhancing attack performance on top of any other attacking methods. We also provide a comprehensive demonstration that watermark collision poses a threat to all logit-based watermark algorithms, impacting not only specific attack scenarios but also downstream applications.
Prompt-Guided Selective Masking Loss for Context-Aware Emotive Text-to-Speech (2025)

Emotional dialogue speech synthesis (EDSS) aims to generate expressive speech by leveraging the dialogue context between interlocutors. This is typically done by concatenating global representations of previous utterances as conditions for text-to-speech (TTS) systems. However, such approaches overlook the importance of integrating localized acoustic cues that convey emotion. To address this, we introduce a novel approach that utilizes a large language model (LLM) to generate holistic emotion tags based on prior dialogue context, while also pinpointing key words in the target utterance that align with the predicted emotional state. Furthermore, we enhance the emotional richness of synthesized speech by incorporating concentrated acoustic features of these key words through a novel selective audio masking loss function. This methodology not only improves emotional expressiveness, but also facilitates automatic emotion speech generation during inference by eliminating the need for manual emotion tag selection. Comprehensive subjective and objective evaluations and analyses demonstrate the effectiveness of the proposed approach.
Identifying and Mitigating Social Bias Knowledge in Language Models (2025)

Generating fair and accurate predictions plays a pivotal role in deploying pre-trained language models (PLMs) in the real world. However, existing debiasing methods may inevitably generate incorrect or nonsensical predictions as they are designed and evaluated to achieve parity across different social groups but leave aside individual commonsense facts, resulting in modified knowledge that elicits unreasonable or undesired predictions. This paper introduces a novel debiasing framework that first identifies the encoding locations of biases within language models and then applies the Fairness-Stamp (FAST). FAST focuses on fine-grained, individual bias mitigation and integrates a lightweight network into PLMs, specifically targeting identified biases while preserving essential knowledge and maintaining factual integrity. We also present BiaScope, a new benchmark comprising datasets and metrics designed to evaluate the retention of commonsense knowledge and the generalization across paraphrased social biases. Our extensive experiments across multiple datasets demonstrate that FAST surpasses state-of-the-art baselines with superior debiasing performance while not compromising the overall model capability for knowledge retention and downstream predictions. This highlights the potential of fine-grained debiasing strategies to achieve fairness in PLMs. Code will be publicly available.
Do Not Design, Learn: A Trainable Scoring Function for Uncertainty Estimation in Generative LLMs (2025)

Uncertainty estimation (UE) of generative large language models (LLMs) is crucial for evaluating the reliability of generated sequences. A significant subset of UE methods utilize token probabilities to assess uncertainty, aggregating multiple token probabilities into a single UE score using a scoring function. Existing scoring functions for probability-based UE, such as length-normalized scoring and semantic contribution-based weighting, are designed to solve certain aspects of the problem but exhibit limitations, including the inability to handle biased probabilities and complex semantic dependencies between tokens. To address these issues, in this work, we propose Learnable Response Scoring (LARS) function, a novel scoring function that leverages supervised data to capture complex dependencies between tokens and probabilities, thereby producing more reliable and calibrated response scores in computing the uncertainty of LLM generations. Our comprehensive experiments across question-answering and arithmetical reasoning tasks with various datasets demonstrate that LARS significantly outperforms existing scoring functions, achieving improvements of up to 16{\%} AUROC score.
Joint Learning Event-Specific Probe and Argument Library with Differential Optimization for Document-Level Multi-Event Extraction (2025)

Document-level multi-event extraction aims to identify a list of event types and corresponding arguments from the document. However, most of the current methods neglect the fine-grained difference among events in multi-event documents, which leads to event confusion and missing. This is also one of the reasons why the recall and F1-score of multi-event recognition are lower compared to single-event recognition. In this paper, we propose an event-specific probe-based method to sniff multiple events by querying each corresponding argument library, which uses a novel probe-label alignment method for differential optimization. In addition, the role contrastive loss and probe consistent loss are designed to fine-tune the fine-grained role differences and probe differences in each event. The experimental results on two general datasets show that our method outperforms the state-of-the-art method in the F1-score, especially in the recall of multi-events.
Synonym-unaware Fast Adversarial Training against Textual Adversarial Attacks (2025)

Numerous adversarial defense methods have been proposed to strengthen the robustness of Natural Language Processing (NLP) models against adversarial attacks. However, many of these methods rely on predetermined linguistic knowledge and assume that attackers' synonym candidates are known, which is often unrealistic. In this work, we investigate adversarial training in the embedding space and introduce a Fast Adversarial Training (FAT) method to improve the model robustness without requiring synonym awareness. FAT leverages single-step perturbation generation and effective perturbation initialization based on two key insights: (1) adversarial perturbations generated by single-step and multi-step gradient ascent are similar, and (2) perturbations generated on the same training sample across successive epochs exhibit resemblance. By employing single-step gradient ascent and leveraging historical perturbation information, FAT not only expedites the training process but also efficiently initializes perturbations. Extensive experiments demonstrate that FAT significantly enhances the robustness of popular NLP models under scenarios where synonyms are unknown, outperforming other defense baselines under various character-level and word-level attacks.
Tethering Broken Themes: Aligning Neural Topic Models with Labels and Authors (2025)

Topic models are a popular approach for extracting semantic information from large document collections. However, recent studies suggest that the topics generated by these models often do not align well with human intentions. Although metadata such as labels and authorship information are available, it has not yet been effectively incorporated into neural topic models. To address this gap, we introduce FANToM, a novel method to align neural topic models with both labels and authorship information. FANToM allows for the inclusion of this metadata when available, producing interpretable topics and author distributions for each topic. Our approach demonstrates greater expressiveness than conventional topic models by learning the alignment between labels, topics, and authors. Experimental results show that FANToM improves existing models in terms of both topic quality and alignment. Additionally, it identifies author interests and similarities.
Towards Zero-Shot Multimodal Machine Translation (2025)

Current multimodal machine translation (MMT) systems rely on fully supervised data (i.e sentences with their translations and accompanying images), which is costly to collect and prevents the extension of MMT to language pairs with no such data. We propose a method to bypass the need for fully supervised data to train MMT systems, using multimodal English data only. Our method ( ZeroMMT) consists in adapting a strong text-only machine translation (MT) model by training it jointly on two objectives: visually conditioned masked language modelling and the Kullback-Leibler divergence between the original MT and new MMT outputs. We evaluate on standard MMT benchmarks and on CoMMuTE, a contrastive test set designed to evaluate how well models use images to disambiguate translations. ZeroMMT obtains disambiguation results close to state-of-the-art MMT models trained on fully supervised examples. To prove that ZeroMMT generalizes to languages with no fully supervised training data, we extend CoMMuTE to three new languages: Arabic, Russian and Chinese. We also show that we can control the trade-off between disambiguation capabilities and translation fidelity at inference time using classifier-free guidance and without any additional data. Our code, data and trained models are publicly accessible.
Large-Scale Corpus Construction and Retrieval-Augmented Generation for Ancient Chinese Poetry: New Method and Data Insights (2025)

Ancient Chinese Poetry (ACP), a critical aspect of Chinese cultural heritage, presents unique challenges for Large Language Models (LLMs). One of the most pressing challenges is the significant hallucination issues faced by LLMs due to data scarcity and limited ability of general LLMs when dealing with ACP. To address these challenges, this paper constructs the ACP-Corpus, which encompasses 1.1 million ancient poems and 990K related texts, designed to enhance the training and performance of LLMs. Alongside this, we develop the ACP-QA dataset, comprising over 12 million question-answer pairs across 24 task categories, and the ACP-Eval dataset for rigorous evaluation purposes, containing 7,050 entries. Building on this resources, we propose the ACP-RAG framework, a specialized Retrieval-Augmented Generation (RAG) approach that significantly improves the performance of LLMs in the domain of ancient poetry from 49.2{\%} to 89.0{\%}. The ACP-RAG contains five modules of semantic coarse-grained retrieval, semantic fine-grained retrieval, keyword retrieval, keyword matching, and context filtering. Experiments show that ACP-RAG achieves a promising response accuracy of 89.0{\%}, surpassing existing LLMs by a remarkable margin. We believe this work not only advances the capabilities of LLMs in processing ancient Chinese poetry but also contributes to the preservation and innovative development within this rich literary tradition. The datasets and code are available at https://github.com/SCUT-DLVCLab/ACP-RAG.
OpenBioNER: Lightweight Open-Domain Biomedical Named Entity Recognition Through Entity Type Description (2025)

Biomedical Named Entity Recognition (BioNER) faces significant challenges in real-world applications due to limited annotated data and the constant emergence of new entity types, making zero-shot learning capabilities crucial. While Large Language Models (LLMs) possess extensive domain knowledge necessary for specialized fields like biomedicine, their computational costs often make them impractical. To address these challenges, we introduce OpenBioNER, a lightweight BERT-based cross-encoder architecture that can identify any biomedical entity using only its description, eliminating the need for retraining on new, unseen entity types. Through comprehensive evaluation on established biomedical benchmarks, we demonstrate that OpenBioNER surpasses state-of-the-art baselines, including specialized 7B NER LLMs and GPT-4o, achieving up to 10{\%} higher F1 scores while using 110M parameters only. Moreover, OpenBioNER outperforms existing small-scale models that match textual spans with entity types rather than descriptions, both in terms of accuracy and computational efficiency.
Dialetto, ma Quanto Dialetto? Transcribing and Evaluating Dialects on a Continuum (2025)

There is increasing interest in looking at dialects in NLP. However, most work to date still treats dialects as discrete categories. For instance, evaluative work in variation-oriented NLP for English often works with Indian English or African-American Venacular English as homogeneous categories, yet even within one variety there is substantial variation. We examine within-dialect variation and show that performance critically varies within categories. We measure speech-to-text performance on Italian dialects, and empirically observe a geographical performance disparity. This disparity correlates substantially (-0.5) with linguistic similarity to the highest performing dialect variety. We cross-examine our results against dialectometry methods, and interpret the performance disparity to be due to a bias towards dialects that are more similar to the standard variety in the speech-to-text model examined. We additionally leverage geostatistical methods to predict zero-shot performance at unseen sites, and find the incorporation of geographical information to substantially improve prediction performance, indicating there to be geographical structure in the performance distribution.
Linguistically Grounded Analysis of Language Models using Shapley Head Values (2025)

Understanding how linguistic knowledge is encoded in language models is crucial for improving their generalisation capabilities. In this paper, we investigate the processing of morphosyntactic phenomena, by leveraging a recently proposed method for probing language models via Shapley Head Values (SHVs). Using the English language BLiMP dataset, we test our approach on two widely used models, BERT and RoBERTa, and compare how linguistic constructions such as anaphor agreement and filler-gap dependencies are handled. Through quantitative pruning and qualitative clustering analysis, we demonstrate that attention heads responsible for processing related linguistic phenomena cluster together. Our results show that SHV-based attributions reveal distinct patterns across both models, providing insights into how language models organize and process linguistic information. These findings support the hypothesis that language models learn subnetworks corresponding to linguistic theory, with potential implications for cross-linguistic model analysis and interpretability in Natural Language Processing (NLP).