ymoslem/acl-abstracts huggingface dataset:
SWITCH: Studying with Teacher for Knowledge Distillation of Large Language Models (2025)

Despite the success of Large Language Models (LLMs), they still face challenges related to high inference costs and memory requirements. To address these issues, Knowledge Distillation (KD) has emerged as a popular method for model compression, with the use of student-generated outputs (SGOs) as training data being particularly notable for reducing the mismatch between training and inference. However, SGOs often produce noisy and biased sequences, which can lead to misguidance from the teacher model, especially in long sequences. To mitigate these challenges, we propose SWITCH (Studying With Teacher for Knowledge Distillation), a novel approach that strategically incorporates the teacher model during the student`s sequence generation. SWITCH identifies discrepancies between the token probabilities of the teacher and student models, allowing the teacher to intervene selectively, particularly in long sequences that are more prone to teacher misguidance. Extensive experimental results across three model families and five instruction-following datasets show that SWITCH surpasses traditional KD methods, particularly excelling in the generation of long sequential data.
Thought2Text: Text Generation from EEG Signal using Large Language Models (LLMs) (2025)

Decoding and expressing brain activity in a comprehensible form is a challenging frontier in AI. This paper presents *Thought2Text*, which uses instruction-tuned Large Language Models (LLMs) fine-tuned with EEG data to achieve this goal. The approach involves three stages: (1) training an EEG encoder for visual feature extraction, (2) fine-tuning LLMs on image and text data, enabling multimodal description generation, and (3) further fine-tuning on EEG embeddings to generate text directly from EEG during inference. Experiments on a public EEG dataset collected for six subjects with image stimuli and text captions demonstrate the efficacy of multimodal LLMs (*LLaMA-v3*, *Mistral-v0.3*, *Qwen2.5*), validated using traditional language generation evaluation metrics, as well as *fluency* and *adequacy* measures. This approach marks a significant advancement towards portable, low-cost {\textquotedblleft}thoughts-to-text{\textquotedblright} technology with potential applications in both neuroscience and natural language processing.
A Comprehensive Survey of Contemporary Arabic Sentiment Analysis: Methods, Challenges, and Future Directions (2025)

Sentiment Analysis, a popular subtask of Natural Language Processing, employs computational methods to extract sentiment, opinions, and other subjective aspects from linguistic data. Given its crucial role in understanding human sentiment, research in sentiment analysis has witnessed significant growth in the recent years. However, the majority of approaches are aimed at the English language, and research towards Arabic sentiment analysis remains relatively unexplored. This paper presents a comprehensive and contemporary survey of Arabic Sentiment Analysis, identifies the challenges and limitations of existing literature in this field and presents avenues for future research. We present a systematic review of Arabic sentiment analysis methods, focusing specifically on research utilizing deep learning. We then situate Arabic Sentiment Analysis within the broader context, highlighting research gaps in Arabic sentiment analysis as compared to general sentiment analysis. Finally, we outline the main challenges and promising future directions for research in Arabic sentiment analysis.
Towards Cross-Lingual Explanation of Artwork in Large-scale Vision Language Models (2025)

As the performance of Large-scale Vision Language Models (LVLMs) improves, they are increasingly capable of responding in multiple languages, and there is an expectation that the demand for explanations generated by LVLMs will grow. However, pre-training of Vision Encoder and the integrated training of LLMs with Vision Encoder are mainly conducted using English training data, leaving it uncertain whether LVLMs can completely handle their potential when generating explanations in languages other than English. In addition, multilingual QA benchmarks that create datasets using machine translation have cultural differences and biases, remaining issues for use as evaluation tasks. To address these challenges, this study created an extended dataset in multiple languages without relying on machine translation. This dataset that takes into account nuances and country-specific phrases was then used to evaluate the generation explanation abilities of LVLMs. Furthermore, this study examined whether Instruction-Tuning in resource-rich English improves performance in other languages. Our findings indicate that LVLMs perform worse in languages other than English compared to English. In addition, it was observed that LVLMs struggle to effectively manage the knowledge learned from English data.
Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis (2025)

Language Confusion is a phenomenon where Large Language Models (LLMs) generate text that is neither in the desired language, nor in a contextually appropriate language. This phenomenon presents a critical challenge in text generation by LLMs, often appearing as erratic and unpredictable behavior. We hypothesize that there are linguistic regularities to this inherent vulnerability in LLMs and shed light on patterns of language confusion across LLMs. We introduce a novel metric, Language Confusion Entropy, designed to directly measure and quantify this confusion, based on language distributions informed by linguistic typology and lexical variation. Comprehensive comparisons with the Language Confusion Benchmark (Marchisio et al., 2024) confirm the effectiveness of our metric, revealing patterns of language confusion across LLMs. We further link language confusion to LLM security, and find patterns in the case of multilingual embedding inversion attacks. Our analysis demonstrates that linguistic typology offers theoretically grounded interpretation, and valuable insights into leveraging language similarities as a prior for LLM alignment and security.
Huatuo-26M, a Large-scale Chinese Medical QA Dataset (2025)

Large Language Models infuse newfound vigor into the advancement of the medical domain, yet the scarcity of data poses a significant bottleneck hindering community progress. In this paper, we release the largest ever medical Question Answering (QA) dataset with 26 Million QA pairs named Huatuo-26M. We benchmark many existing approaches in our dataset in terms of both retrieval and generation. We also experimentally show the benefit of the proposed dataset in many aspects: (i) it serves as a fine-tuning data for training medical Large Language Models (LLMs); (ii) it works as an external knowledge source for retrieval-augmented generation (RAG); (iii) it demonstrates transferability by enhancing zero-shot performance on other QA datasets; and (iv) it aids in training biomedical model as a pre-training corpus. Our empirical findings substantiate the dataset`s utility in these domains, thereby confirming its significance as a resource in the medical QA landscape.
SEP-MLDC: A Simple and Effective Paradigm for Multi-Label Document Classification (2025)

Multi-label document classification (MLDC) aims to allocate more than one label to each document and attracts increasing attention in many practical applications. However, previous studies have failed to pay sufficient attention to the lack of semantic information on labels and the long-tail problem prevalent in the datasets. Additionally, most existing methods focus on optimizing document features, overlooking the potential of high-quality label features to enhance classification performance. In this paper, we propose a simple and effective paradigm for MLDC. Regarding the problem of insufficient label information and imbalance in the sample size of categories, we utilize large language models (LLMs) to semantically expand the label content and generate pseudo-samples for the tail categories. To optimize the features of both documents and labels, we design the contrastive learning boosted feature optimization module facilitated by the similarity matrices. Finally, we construct a label-guided feature selection module to incorporate the optimized label features into the input features to provide richer semantic information for the classifier. Extensive experiments have demonstrated that our proposed method significantly outperforms state-of-the-art baselines.
Improving Pre-trained Language Models with Knowledge Enhancement and Filtering Framework (2025)

Pre-trained language models (PLMs) are widely used in NLP but struggle with capturing entity knowledge. To address this, knowledge enhancement techniques have been proposed. However, existing methods rely heavily on external knowledge bases embedding and often introduce noisy entity representations. In this work, we propose a novel **K**nowledge **E**nhancement **F**iltering **F**ramework named KEFF, which contains both knowledge enhancement and knowledge enhancement filtering modules for PLM. We find that there are certain redundant bits in the embedding space of PLMs. Building on this insight, we implement knowledge-enhanced mapping of redundant bit values in entity span tokens. In order to solve the knowledge enhancement problem of existing methods that introduce noisy entity representation knowledge, we further propose a novel knowledge enhancement filter based on our knowledge enhancement method. Finally, experiments on four knowledge-driven NLP tasks show that our method effectively improves the ability of PLMs on downstream tasks. Compared to state-of-the-art approachs, our method achieves the highest F1-score and accuracy, while reducing the computational cost by 1.7-2.5x.
Using Review Combination and Pseudo-Tokens for Aspect Sentiment Quad Prediction (2025)

Aspect Sentiment Quad Prediction (ASQP) aims to identify quadruples consisting of an aspect term, aspect category, opinion term, and sentiment polarity from a given sentence, which is the most representative and challenging task in aspect-based sentiment analysis. A major challenge arises when implicit sentiment is present, as existing models often confuse implicit and explicit sentiment, making it difficult to extract the quadruples effectively. To tackle this issue, we propose a framework that leverages distinct labeled features from diverse reviews and incorporates pseudo-token prompts to harness the semantic knowledge of pre-trained models, effectively capturing both implicit and explicit sentiment expressions. Our approach begins by categorizing reviews based on the presence of implicit sentiment elements. We then build new samples that combine those with implicit sentiment and those with explicit sentiment. Next, we employ prompts with pseudo-tokens to guide the model in distinguishing between implicit and explicit sentiment expressions. Extensive experimental results show that our proposed method enhances the model`s ability across four public datasets, averaging 1.99{\%} F1 improvement, particularly in instances involving implicit sentiment. We release our code at https://github.com/chienarmor/absa-implicit.
DDGIP: Radiology Report Generation Through Disease Description Graph and Informed Prompting (2025)

Automatic radiology report generation has attracted considerable attention with the rise of computer-aided diagnostic systems. Due to the inherent biases in medical imaging data, generating reports with precise clinical details is challenging yet crucial for accurate diagnosis. To this end, we design a disease description graph that encapsulates comprehensive and pertinent disease information. By aligning visual features with the graph, our model enhances the quality of the generated reports. Furthermore, we introduce a novel informed prompting method which increases the accuracy of short-gram predictions, acting as an implicit bag-of-words planning for surface realization. Notably, this informed prompt succeeds with a three-layer decoder, reducing the reliance on conventional prompting methods that require extensive model parameters. Extensive experiments on two widely-used datasets, IU-Xray and MIMIC-CXR, demonstrate that our method outperforms previous state-of-the-art models.
Lossless Acceleration of Large Language Models with Hierarchical Drafting based on Temporal Locality in Speculative Decoding (2025)

Accelerating inference in Large Language Models (LLMs) is critical for real-time interactions, as they have been widely incorporated into real-world services. Speculative decoding, a fully algorithmic solution, has gained attention for improving inference speed by drafting and verifying tokens, thereby generating multiple tokens in a single forward pass. However, current drafting strategies usually require significant fine-tuning or have inconsistent performance across tasks. To address these challenges, we propose Hierarchy Drafting (HD), a novel lossless drafting approach that organizes various token sources into multiple databases in a hierarchical framework based on temporal locality. In the drafting step, HD sequentially accesses multiple databases to obtain draft tokens from the highest to the lowest locality, ensuring consistent acceleration across diverse tasks and minimizing drafting latency. Our experiments on Spec-Bench using LLMs with 7B and 13B parameters demonstrate that HD outperforms existing database drafting methods, achieving robust inference speedups across model sizes, tasks, and temperatures.
Improve Decoding Factuality by Token-wise Cross Layer Entropy of Large Language Models (2025)

Despite their impressive capacities, Large language models (LLMs) often struggle with the hallucination issue of generating inaccurate or fabricated content even when they possess correct knowledge. In this paper, we extend the exploration of the correlation between hidden-state prediction changes and output factuality into a deeper, token-wise level. Based on the insights , we propose cross-layer Entropy eNhanced Decoding (END), a decoding method that mitigates hallucinations without requiring extra training. END leverages inner probability changes across layers to individually quantify the factual knowledge required for each candidate token, and adjusts the final predicting distribution to prioritize tokens with higher factuality. Experiments on both hallucination and QA benchmarks demonstrate that END significantly enhances the truthfulness and informativeness of generation while maintaining robust QA accuracy. Moreover, our work provides a deeper perspective of understanding the correlations between inherent knowledge and output factuality.
TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement (2025)

Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). However, human evaluations reveal that LLM-generated translations still contain various errors. Notably, feeding the error information back into the LLMs can facilitate self-refinement, leading to enhanced translation quality. Motivated by these findings, we introduce TEaR (Translate, Estimate, and Refine), a systematic LLM-based self-refinement framework aimed at bootstrapping translation performance. Our key results show that: 1) TEaR framework enables LLMs to improve their translation quality relying solely on self-feedback, measured by both automatic metrics and Multidimensional Quality Metrics (MQM) scores; 2) TEaR autonomously selects improvements, ensuring a robust translation quality baseline while outperforming both internal refinement and external feedback methods. Error analysis and iterative refinement experiments show its ability to continuously reduce translation errors and enhance overall translation quality. Our code and data are publicly available at https://github.com/fzp0424/self{\_}correct{\_}mt.
Vulnerability of Large Language Models to Output Prefix Jailbreaks: Impact of Positions on Safety (2025)

Previous research on jailbreak attacks has mainly focused on optimizing the adversarial snippet content injected into input prompts to expose LLM security vulnerabilities. A significant portion of this research focuses on developing more complex, less readable adversarial snippets that can achieve higher attack success rates. In contrast to this trend, our research investigates the impact of the adversarial snippet`s position on the effectiveness of jailbreak attacks. We find that placing a simple and readable adversarial snippet at the beginning of the output effectively exposes LLM safety vulnerabilities, leading to much higher attack success rates than the input suffix attack or prompt-based output jailbreaks. Precisely speaking, we discover that directly enforcing the user`s target embedded output prefix is an effective method to expose LLMs' safety vulnerabilities.
ImaRA: An Imaginative Frame Augmented Method for Low-Resource Multimodal Metaphor Detection and Explanation (2025)

Multimodal metaphor detection is an important and challenging task in multimedia computing, which aims to distinguish between metaphorical and literal multimodal expressions. Existing studies mainly utilize typical multimodal computing approaches for detection, neglecting the unique cross-domain and cross-modality characteristics underlying multimodal metaphor understanding. According to Conceptual Metaphor Theory (CMT), the inconsistency between source and target domains and their attribute similarity are essential to infer the intricate meanings implied in metaphors. In practice, the scarcity of the annotated multimodal metaphorical contents in the real world brings additional difficulty to the detection task and further complicates the understanding of multimodal metaphors. To address the above challenges, in this paper, we propose a novel Imaginative FRame Augmented (ImaRA) method for low-resource multimodal metaphor detection and explanation inspired by CMT. Specifically, we first identify imaginative frame as an associative structure to stimulate the imaginative thinking of multimodal metaphor detection and understanding. We then construct a cross-modal imagination dataset rich in multimodal metaphors and corresponding imaginative frames, and retrieve an augmented instance from this imagination dataset using imaginative frames mined from the input. This augmented instance serves as the demonstration exemplar to boost the metaphor reasoning ability of the multimodal large language model (MLLM) in low-resource multimodal scenarios. Experiments on two publicly available datasets show that our method consistently achieves robust results compared to MLLM-based methods for both multimodal metaphor detection and explanation in low-resource scenarios and meanwhile surpasses existing multimodal metaphor detection methods with full training data.
XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples (2025)

Recent studies indicate that leveraging off-the-shelf or fine-tuned retrievers, capable of retrieving relevant in-context examples tailored to the input query, enhances few-shot in-context learning of English. However, adapting these methods to other languages, especially low-resource ones, poses challenges due to the scarcity of cross-lingual retrievers and annotated data. Thus, we introduce XAMPLER: Cross-Lingual Example Retrieval, a method tailored to tackle the challenge of cross-lingual in-context learning using only annotated English data. XAMPLER first trains a retriever based on Glot500, a multilingual small language model, using positive and negative English examples constructed from the predictions of a multilingual large language model, i.e., MaLA500. Leveraging the cross-lingual capacity of the retriever, it can directly retrieve English examples as few-shot examples for in-context learning of target languages. Experiments on two multilingual text classification benchmarks, namely SIB200 with 176 languages and MasakhaNEWS with 16 languages, demonstrate that XAMPLER substantially improves the in-context learning performance across languages.
Evaluating Cultural and Social Awareness of LLM Web Agents (2025)

As large language models (LLMs) expand into performing as agents for real-world applications beyond traditional NLP tasks, evaluating their robustness becomes increasingly important. However, existing benchmarks often overlook critical dimensions like cultural and social awareness. To address these, we introduce CASA, a benchmark designed to assess LLM agents' sensitivity to cultural and social norms across two web-based tasks: online shopping and social discussion forums. Our approach evaluates LLM agents' ability to detect and appropriately respond to norm-violating user queries and observations. Furthermore, we propose a comprehensive evaluation framework that measures awareness coverage, helpfulness in managing user queries, and the violation rate when facing misleading web content. Experiments show that current LLMs perform significantly better in non-agent than in web-based agent environments, with agents achieving less than 10{\%} awareness coverage and over 40{\%} violation rates. To improve performance, we explore two methods: prompting and fine-tuning, and find that combining both methods can offer complementary advantages {--} fine-tuning on culture-specific datasets significantly enhances the agents' ability to generalize across different regions, while prompting boosts the agents' ability to navigate complex tasks. These findings highlight the importance of constantly benchmarking LLM agents' cultural and social awareness during the development cycle.
GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective Hallucination Mitigation (2025)

Refusal-Aware Instruction Tuning (RAIT) aims to enhance Large Language Models (LLMs) by improving their ability to refuse responses to questions beyond their knowledge, thereby reducing hallucinations and improving reliability. Effective RAIT must address two key challenges: firstly, effectively reject unknown questions to minimize hallucinations; secondly, avoid over-refusal to ensure questions that can be correctly answered are not rejected, thereby maintain the helpfulness of LLM outputs. In this paper, we address the two challenges by deriving insightful observations from the gradient-based perspective, and proposing the Gradient-driven Refusal Aware Instruction Tuning Framework GRAIT: (1) employs gradient-driven sample selection to effectively minimize hallucinations and (2) introduces an adaptive weighting mechanism during fine-tuning to reduce the risk of over-refusal, achieving the balance between accurate refusals and maintaining useful responses. Experimental evaluations on open-ended and multiple-choice question answering tasks demonstrate that GRAIT significantly outperforms existing RAIT methods in the overall performance. The source code and data will be available at https://github.com/opendatalab/GRAIT .