ymoslem/acl-abstracts huggingface dataset:
Hierarchical Speculative Decoding with Dynamic Window (2025)

Speculative Decoding (SD) utilizes an efficient draft model to generate multiple tokens, which are subsequently verified in parallel by a target model. This approach has shown significant potential for accelerating inference in large language models (LLMs), with performance heavily reliant on the hyperparameter $K${---}the window size. However, previous methods often depend on simple heuristics to select $K$ or dynamically adjust the window size, which may necessitate additional training or careful resource management to avoid competition.To address these challenges, we propose \textbf{H}ierarchical \textbf{S}peculative \textbf{D}ecoding with \textbf{D}ynamic \textbf{W}indow (HSDDW), a straightforward framework that eliminates the need for additional training. Specifically, we introduce a \textit{self-verify} mechanism that enables the draft model to autonomously decide when to stop generating tokens. Additionally, by integrating a hierarchical structure that leverages the capabilities of models of different sizes, we significantly enhance the overall speed of the system.HSDDW demonstrates competitive performance across four datasets, achieving notable speedups of $2.91\times$ on MT-Bench and $2.99\times$ on Alpaca, outperforming existing state-of-the-art methods.
Q-FAKER: Query-free Hard Black-box Attack via Controlled Generation (2025)

Many adversarial attack approaches are proposed to verify the vulnerability of language models. However, they require numerous queries and the information on the target model. Even black-box attack methods also require the target model`s output information. They are not applicable in real-world scenarios, as in hard black-box settings where the target model is closed and inaccessible. Even the recently proposed hard black-box attacks still require many queries and demand extremely high costs for training adversarial generators. To address these challenges, we propose Q-faker (Query-free Hard Black-box Attacker), a novel and efficient method that generates adversarial examples without accessing the target model. To avoid accessing the target model, we use a surrogate model instead. The surrogate model generates adversarial sentences for a target-agnostic attack. During this process, we leverage controlled generation techniques. We evaluate our proposed method on eight datasets. Experimental results demonstrate our method`s effectiveness including high transferability and the high quality of the generated adversarial examples, and prove its practical in hard black-box settings.
PRDetect: Perturbation-Robust LLM-generated Text Detection Based on Syntax Tree (2025)

As LLM-generated text becomes increasingly prevalent on the internet, often containing hallucinations or biases, detecting such content has emerged as a critical area of research.Recent methods have demonstrated impressive performance in detecting text generated entirely by LLMs.However, in real-world scenarios, users often introduce perturbations to the LLM-generated text, and the robustness of existing detection methods against these perturbations has not been sufficiently explored.This paper empirically investigates this challenge and finds that even minor perturbations can severely degrade the performance of current detection methods. To address this issue, we find that the syntactic tree is minimally affected by disturbances and exhibits distinct differences between human-written and LLM-generated text.Therefore, we propose a detection method based on syntactic trees, which can capture features invariant to perturbations.It demonstrates significantly improved robustness against perturbation on the HC3 and GPT-3.5-mixed datasets.Moreover, it also has the shortest time expenditure.We provide the code and data at https://github.com/thulx18/PRDetect.
Enabling Natural Zero-Shot Prompting on Encoder Models via Statement-Tuning (2025)

While Large Language Models (LLMs) exhibit remarkable capabilities in zero-shot and few-shot scenarios, they often require computationally prohibitive sizes. Conversely, smaller Masked Language Models (MLMs) like BERT and RoBERTa achieve state-of-the-art results through fine-tuning but struggle with extending to few-shot and zero-shot settings due to their architectural constraints. Hence, we propose Statement-Tuning, a technique that models discriminative tasks as a set of finite statements and trains an encoder model to discriminate between the potential statements to determine the label. We do Statement-Tuning on multiple tasks to enable cross-task generalization. Experimental results demonstrate that Statement-Tuning achieves competitive performance compared to state-of-the-art LLMs with significantly fewer parameters. Furthermore, we compare with previous encoder-based methodology and show that our method is more accurate and more robust to spurious patterns. Moreover, the study investigates the impact of several design choices on few-shot and zero-shot generalization, revealing that Statement-Tuning can achieve strong performance with modest training data and benefits from task and statement diversity for unseen task generalizability. We release all the code used to generate statement data, train and evaluate our Statement-Tuned models.
Faster Machine Translation Ensembling with Reinforcement Learning and Competitive Correction (2025)

Ensembling neural machine translation (NMT) models to produce higher-quality translations than the $L$ individual models has been extensively studied. Recent methods typically employ a candidate selection block (CSB) and an encoder-decoder fusion block (FB), requiring inference across \textit{all} candidate models, leading to significant computational overhead, generally $\Omega(L)$. This paper introduces \textbf{SmartGen}, a reinforcement learning (RL)-based strategy that improves the CSB by selecting a small, fixed number of candidates and identifying optimal groups to pass to the fusion block for each input sentence. Furthermore, previously, the CSB and FB were trained independently, leading to suboptimal NMT performance. Our DQN-based \textbf{SmartGen} addresses this by using feedback from the FB block as a reward during training. We also resolve a key issue in earlier methods, where candidates were passed to the FB without modification, by introducing a Competitive Correction Block (CCB). Finally, we validate our approach with extensive experiments on English-Hindi translation tasks in both directions as well as English to Chinese and English to German.
Evaluating Numeracy of Language Models as a Natural Language Inference Task (2025)

While recent advancements in large language models (LLMs) have enhanced their capabilities to solve mathematical problems, other aspects of numeracy remain underexplored. In this paper, we propose a benchmark to evaluate the ability of language models to perform basic numeracy tasks. We frame numeracy as a Natural Language Inference (NLI) task to assess the models' ability to understand both numbers and language contexts. We evaluate 49 language models (LMs), including fine-tuned LMs on NLI datasets, instruction-tuned LLMs, and specialized math-LLMs. Our findings reveal three main insights: (1) LLMs only clearly outperform smaller LMs in arithmetic tasks, indicating that mathematical reasoning cannot be generalized to other numeracy skills such as number comparison and normalization; (2) while most language models achieve fair to good accuracy for NLI entailment cases, they still struggle to predict contradiction and neutral cases; and (3) the robustness of language models' numeracy capabilities needs improvement, particularly in understanding the semantics and pragmatics of numbers in linguistic contexts.
Are Language Models Agnostic to Linguistically Grounded Perturbations? A Case Study of Indic Languages (2025)

Pre-trained language models (PLMs) are known to be susceptible to perturbations to the input text, but existing works do not explicitly focus on linguistically grounded attacks, which are subtle and more prevalent in nature. In this paper, we study whether PLMs are agnostic to linguistically grounded attacks or not. To this end, we offer the first study addressing this, investigating different Indic languages and various downstream tasks. Our findings reveal that although PLMs are susceptible to linguistic perturbations, when compared to non-linguistic attacks, PLMs exhibit a slightly lower susceptibility to linguistic attacks. This highlights that even constrained attacks are effective. Moreover, we investigate the implications of these outcomes across a range of languages, encompassing diverse language families and different scripts.
Do LLMs Have Distinct and Consistent Personality? TRAIT: Personality Testset designed for LLMs with Psychometrics (2025)

Recent advancements in Large Language Models (LLMs) have led to their adaptation in various domains as conversational agents. We wonder: can personality tests be applied to these agents to analyze their behavior, similar to humans? We introduce TRAIT, a new benchmark consisting of 8K multi-choice questions designed to assess the personality of LLMs. TRAIT is built on two psychometrically validated small human questionnaires, Big Five Inventory (BFI) and Short Dark Triad (SD-3), enhanced with the ATOMIC-10X knowledge graph to a variety of real-world scenarios. TRAIT also outperforms existing personality tests for LLMs in terms of reliability and validity, achieving the highest scores across four key metrics: Content Validity, Internal Validity, Refusal Rate, and Reliability. Using TRAIT, we reveal two notable insights into personalities of LLMs: 1) LLMs exhibit distinct and consistent personality, which is highly influenced by their training data (e.g., data used for alignment tuning), and 2) current prompting techniques have limited effectiveness in eliciting certain traits, such as high psychopathy or low conscientiousness, suggesting the need for further research in this direction.
Tell Me What You Know About Sexism: Expert-LLM Interaction Strategies and Co-Created Definitions for Zero-Shot Sexism Detection (2025)

This paper investigates hybrid intelligence and collaboration between researchers of sexism and Large Language Models (LLMs), with afour-component pipeline. First, nine sexism researchers answer questions about their knowledge of sexism and of LLMs. They then participate in two interactive experiments involving an LLM (GPT3.5). The first experiment has experts assessing the model`s knowledgeabout sexism and suitability for use in research. The second experiment tasks them with creating three different definitions of sexism: anexpert-written definition, an LLM-written one, and a co-created definition. Lastly, zero-shot classification experiments use the three definitions from each expert in a prompt template for sexism detection, evaluating GPT4o on 2.500 texts sampled from five sexism benchmarks. We then analyze the resulting 67.500 classification decisions. The LLM interactions lead to longer and more complex definitions of sexism. Expert-written definitions on average perform poorly compared to LLM-generated definitions. However, some experts do improve classification performance with their co-created definitions of sexism, also experts who are inexperienced in using LLMs.
The Role of Prosody in Spoken Question Answering (2025)

Spoken language understanding research to date has generally carried a heavy text perspective. Most datasets are derived from text, which is then subsequently synthesized into speech, and most models typically rely on automatic transcriptions of speech. This is to the detriment of prosody{--}additional information carried by the speech signal beyond the phonetics of the words themselves and difficult to recover from text alone. In this work, we investigate the role of prosody in Spoken Question Answering. By isolating prosodic and lexical information on the SLUE-SQA-5 dataset, which consists of natural speech, we demonstrate that models trained on prosodic information alone can perform reasonably well by utilizing prosodic cues. However, we find that when lexical information is available, models tend to predominantly rely on it. Our findings suggest that while prosodic cues provide valuable supplementary information, more effective integration methods are required to ensure prosody contributes more significantly alongside lexical features.
Target-Augmented Shared Fusion-based Multimodal Sarcasm Explanation Generation (2025)

Sarcasm is a linguistic phenomenon that intends to ridicule a target (e.g., entity, event, or person) in an inherent way. Multimodal Sarcasm Explanation (MuSE) aims at revealing the intended irony in a sarcastic post using a natural language explanation. Though important, existing systems overlooked the significance of the target of sarcasm in generating explanations. In this paper, we propose a \textbf{T}arget-a\textbf{U}gmented sha\textbf{R}ed fusion-\textbf{B}ased sarcasm explanati\textbf{O}n model, aka. . We design a novel shared-fusion mechanism to leverage the inter-modality relationships between an image and its caption. assumes the target of the sarcasm and guides the multimodal shared fusion mechanism in learning intricacies of the intended irony for explanations. We evaluate our proposed model on the dataset. Comparison against multiple baselines and state-of-the-art models signifies the performance improvement of by an average margin of $+3.3\%$. Moreover, we explore LLMs in zero and one-shot settings for our task and observe that LLM-generated explanation, though remarkable, often fails to capture the critical nuances of the sarcasm. Furthermore, we supplement our study with extensive human evaluation on {\textquoteleft}s generated explanations and find them out to be comparatively better than other systems.
Seeds of Discourse: A Multilingual Corpus of Direct Quotations from African Media on Agricultural Biotechnologies (2025)

Direct quotations play a crucial role in journalism by substantiating claims and enhancing persuasive communication. This makes news articles a rich resource for opinion mining, providing valuable insights into the topics they cover. This paper presents the first multilingual corpora (English and French) featuring both manually annotated (1,657) and automatically extracted (102,483) direct quotations related to agricultural biotechnologies from a curated list of Africa-based news sources. In addition, we provide 665 instances annotated for Aspect-Based Sentiment Analysis, enabling a fine-grained examination of sentiment toward key aspects of agricultural biotechnologies. These corpora are freely available to the research community for future work on media discourse surrounding agricultural biotechnologies.
Position Really Matters: Towards a Holistic Approach for Prompt Tuning (2025)

Prompt tuning is highly effective in efficiently extracting knowledge from foundation models, encompassing both language, vision, and vision-language models. However, the efficacy of employing fixed soft prompts with a \textit{predetermined position} for concatenation with inputs for all instances, irrespective of their inherent disparities, remains uncertain. Variables such as the position, length, and representations of prompts across diverse instances and tasks can substantially influence the performance of prompt tuning. We first provide a theoretical analysis, revealing that optimizing the position of the prompt to encompass the input can capture additional semantic information that traditional prefix or postfix prompt tuning methods fail to capture. Then, we present a holistic parametric prompt tuning strategy that dynamically determines different factors of prompts based on specific tasks or instances. Experimental results underscore the significant performance improvement achieved by dynamic prompt tuning across a wide range of tasks, including NLP, vision recognition, and vision-language tasks. Furthermore, we establish the universal applicability of our approach under full-data, few-shot, and multitask settings.
A Dataset for Programming-based Instructional Video Classification and Question Answering (2025)

This work aims to develop an understanding of the rapidly emerging field of VideoQA, particularly in the context of instructional programming videos. It also encourages designing of system that can produce visual answer to programming based natural language questions. We introduce two datasets: CodeVidQA, with 2,104 question-answer pair links with timestamps taken from programming videos of Stack Overflow for Programming Visual Answer Localization task, and CodeVidCL with 4,331 videos (1,751 programming ,2580 non-programming) for Programming Video Classification task. In addition, we proposed a framework that adapts BigBird and SVM for video classification techniques. The proposed approach achieves a significantly high accuracy of 99.61{\%} for video classification.
CVT5: Using Compressed Video Encoder and UMT5 for Dense Video Captioning (2025)

The dense video captioning task aims to detect all events occurring in a video and describe each event using natural language. Unlike most other video processing tasks, where it is typically assumed that videos contain only a single main event, this task deals with long, untrimmed videos. Consequently, the speed of processing videos in dense video captioning is a critical aspect of the system. To the best of our knowledge, all published work on this task uses RGB frames to encode input videos. In this work, we introduce the use of compressed videos for the first time in this task. Our experiments on the SoccerNet challenge demonstrate significant improvements in both processing speed and GPU memory footprint while achieving competitive results. Additionally, we leverage multilingual transcripts, which seems to be effective. The encoder in our proposed method achieves approximately 5.4{\texttimes} higher speed and 5.1{\texttimes} lower GPU memory usage during training, and 4.7{\texttimes} higher speed and 7.8{\texttimes} lower GPU memory usage during inference, compared to its RGB-based counterpart. The code is publicly available at https://github.com/mohammadjavadpirhadi/CVT5.
If I feel smart, I will do the right thing: Combining Complementary Multimodal Information in Visual Language Models (2025)

Generative visual language models (VLMs) have recently shown potential across various downstream language-and-vision tasks. At the same time, it is still an open question whether, and to what extent, these models can properly understand a multimodal context where language and vision provide complementary information{---}a mechanism routinely in place in human language communication. In this work, we test various VLMs on the task of generating action descriptions consistent with both an image`s visual content and an intention or attitude (not visually grounded) conveyed by a textual prompt. Our results show that BLIP-2 is not far from human performance when the task is framed as a generative multiple-choice problem, while other models struggle. Furthermore, the actions generated by BLIP-2 in an open-ended generative setting are better than those by the competitors; indeed, human annotators judge most of them as plausible continuations for the multimodal context. Our study reveals substantial variability among VLMs in integrating complementary multimodal information, yet BLIP-2 demonstrates promising trends across most evaluations, paving the way for seamless human-computer interaction.
LLaVA-RE: Binary Image-Text Relevancy Evaluation with Multimodal Large Language Model (2025)

Multimodal generative AI usually involves generating image or text responses given inputs in another modality. The evaluation of image-text relevancy is essential for measuring the response quality or ranking candidate responses. In particular, binary relevancy evaluation, i.e., {\textquotedblleft}Relevant{\textquotedblright} vs. {\textquotedblleft}Not Relevant{\textquotedblright}, is a fundamental problem. However, this is a challenging task considering that texts have diverse formats and the definition of relevancy varies in different scenarios. We find that Multimodal Large Language Models (MLLMs) are an ideal choice to build such evaluators, as they can flexibly handle complex text formats and take in additional task information. In this paper, we present LLaVA-RE, a first attempt for binary image-text relevancy evaluation with MLLM. It follows the LLaVA architecture and adopts detailed task instructions and multimodal in-context samples. Further, we propose a novel binary relevancy dataset covering diverse tasks. Experimental results validate the effectiveness of our framework.
Persian in a Court: Benchmarking VLMs In Persian Multi-Modal Tasks (2025)

This study introduces a novel framework for evaluating Large Language Models (LLMs) and Vision-Language Models (VLMs) in Persian, a low-resource language. We develop comprehensive datasets to assess reasoning, linguistic understanding, and multimodal capabilities. Our datasets include Persian-OCR-QA for optical character recognition, Persian-VQA for visual question answering, Persian world-image puzzle for multimodal integration, Visual-Abstraction-Reasoning for abstract reasoning, and Iran-places for visual knowledge of Iranian figures and locations. We evaluate models like GPT-4o, Claude 3.5 Sonnet, and Llama 3.2 90B Vision, revealing their strengths and weaknesses in processing Persian. This research contributes to inclusive language processing by addressing the unique challenges of low-resource language evaluation.
TaiwanVQA: A Benchmark for Visual Question Answering for Taiwanese Daily Life (2025)

We introduce TaiwanVQA, a novel visual question answering benchmark designed to evaluate vision language models' (VLMs) ability to recognize and reason about Taiwan-specific multimodal content.TaiwanVQA comprises 2,000 image-question pairs covering diverse topics relevant to Taiwanese culture and daily life. We categorize the questions into recognition and reasoning tasks, further sub-classifying reasoning questions based on the level of external knowledge required. We conduct extensive experiments on state-of-the-art VLMs, including GPT-4o, Llama-3.2, LLaVA, Qwen2-VL, and InternVL2 models. Our findings reveal significant limitations in current VLMs when handling culturally specific content. The performance gap widens between recognition tasks (top score 73.60{\%}) and reasoning tasks (top score 49.80{\%}), indicating challenges in cultural inference and contextual understanding.These results highlight the need for more culturally diverse training data and improved model architectures that can better integrate visual and textual information within specific cultural contexts. By providing TaiwanVQA, we aim to contribute to the development of more inclusive and culturally aware AI models, facilitating their deployment in diverse real-world settings. TaiwanVQA can be accessed on our GitHub page.
Guiding Vision-Language Model Selection for Visual Question-Answering Across Tasks, Domains, and Knowledge Types (2025)

Visual Question-Answering (VQA) has become key to user experience, particularly after improved generalization capabilities of Vision-Language Models (VLMs). But evaluating VLMs for an application requirement using a standardized framework in practical settings is still challenging. This paper aims to solve that using an end-to-end framework. We present VQA360 - a novel dataset derived from established VQA benchmarks, annotated with task types, application domains, and knowledge types, for a comprehensive evaluation. We also introduce GoEval, a multimodal evaluation metric developed using GPT-4o, achieving a correlation factor of 56.71{\%} with human judgments. Our experiments with state-of-the-art VLMs reveal that no single model excels universally, thus, making a right choice a key design decision. Proprietary models such as Gemini-1.5-Pro and GPT-4o-mini generally outperform others, but open-source models like InternVL-2-8B and CogVLM-2-Llama-3-19B also demonstrate competitive strengths, while providing additional advantages. Our framework can also be extended to other tasks.