ymoslem/acl-abstracts huggingface dataset:
Semantic and Sentiment Dual-Enhanced Generative Model for Script Event Prediction (2025)

Script Event Prediction (SEP) aims to forecast the next event in a sequence from a list of candidates. Traditional methods often use pre-trained language models to model event associations but struggle with semantic ambiguity and embedding bias. Semantic ambiguity arises from the multiple meanings of identical words and insufficient consideration of event arguments, while embedding bias results from assigning similar word embeddings to event pairs with similar lexical features, despite their different meanings. To address above issues, we propose a the Semantic and Sentiment Dual-enhanced Generative Model (SSD-GM). SSD-GM leverages two types of script event information to enhance the generative model. Specifically, it employs a GNN-based semantic structure aggregator to integrate the event-centric structure information, thereby mitigating the impact of semantic ambiguity. Furthermore, we find that local sentiment variability effectively reduces biases in event embeddings, while maintaining global sentiment consistency enhances predictive accuracy. As a result, SSD-GM adeptly captures both global and local sentiment of events through its sentiment information awareness mechanism. Extensive experiments on the Multi-Choice Narrative Cloze (MCNC) task demonstrate that our approach achieves better results than other state-of-the-art baselines.
Generation-Based and Emotion-Reflected Memory Update: Creating the KEEM Dataset for Better Long-Term Conversation (2025)

In this work, we introduce the Keep Emotional and Essential Memory (KEEM) dataset, a novel generation-based dataset designed to enhance memory updates in long-term conversational systems. Unlike existing approaches that rely on simple accumulation or operation-based methods, which often result in information conflicts and difficulties in accurately tracking a user`s current state, KEEM dynamically generates integrative memories. This process not only preserves essential factual information but also incorporates emotional context and causal relationships, enabling a more nuanced understanding of user interactions. By seamlessly updating a system`s memory with both emotional and essential data, our approach promotes deeper empathy and enhances the system`s ability to respond meaningfully in open-domain conversations.
medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRs (2025)

Electronic Medical Records (EMRs), while integral to modern healthcare, present challenges for clinical reasoning and diagnosis due to their complexity and information redundancy. To address this, we proposed medIKAL (\textbf{I}ntegrating \textbf{K}nowledge Graphs as \textbf{A}ssistants of \textbf{L}LMs), a framework that combines Large Language Models (LLMs) with knowledge graphs (KGs) to enhance diagnostic capabilities. medIKAL assigns weighted importance to entities in medical records based on their type, enabling precise localization of candidate diseases within KGs. It innovatively employs a residual network-like approach, allowing initial diagnosis by the LLM to be merged into KG search results. Through a path-based reranking algorithm and a fill-in-the-blank style prompt template, it further refined the diagnostic process. We validated medIKAL`s effectiveness through extensive experiments on a newly introduced open-sourced Chinese EMR dataset, demonstrating its potential to improve clinical diagnosis in real-world settings.
AIDER: a Robust and Topic-Independent Framework for Detecting AI-Generated Text (2025)

The human-level fluency achieved by large language models in text generation has intensified the challenge of distinguishing between human-written and AI-generated texts. While current fine-tuned detectors exist, they often lack robustness against adversarial attacks and struggle with out-of-distribution topics, limiting their practical applicability. This study introduces AIDER, a robust and topic-independent AI-generated text detection framework. AIDER leverages the ALBERT model for topic content disentanglement, enhancing transferability to unseen topics. It incorporates an augmentor that generates robust adversarial data for training, coupled with contrastive learning techniques to boost resilience. Comprehensive experiments demonstrate AIDER`s significant superiority over state-of-the-art methods, exhibiting exceptional robustness against adversarial attacks with minimal performance degradation. AIDER consistently achieves high accuracy in non-augmented scenarios and demonstrates remarkable generalizability to unseen topics. These attributes establish AIDER as a powerful and versatile tool for LLM-generated text detection across diverse real-world applications, addressing critical challenges in the evolving landscape of AI-generated content.
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information (2025)

The colossal parameters and computational overhead of Large Language Models (LLMs) challenge their real-world applications. Network pruning, which targets unstructured or structured sparsity by removing redundant parameters, has recently been explored for LLM acceleration. Existing LLM pruning works focus on unstructured pruning, which typically requires special hardware support for a practical speed-up. In contrast, structured pruning can reduce latency on general devices. However, it remains a challenge to perform structured pruning efficiently and maintain performance, especially at high sparsity ratios. To this end, we introduce an efficient structured pruning framework named CFSP, which leverages both Coarse (interblock) and Fine-grained (intrablock) activation information as an importance criterion to guide pruning. The pruning is highly efficient, as it only requires one forward pass to compute feature activations. Specifically, we first allocate the sparsity budget across blocks based on their importance and then retain important weights within each block. In addition, we introduce a recovery fine-tuning strategy that adaptively allocates training overhead based on coarse-grained importance to further improve performance. Experimental results demonstrate that CFSP outperforms existing methods on diverse models across various sparsity budgets. Our code will be available at https://github.com/wyxscir/CFSP.
Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models (2025)

Abstention Ability (AA) is a critical aspect of Large Language Model (LLM) reliability, referring to an LLM`s capability to withhold responses when uncertain or lacking a definitive answer, without compromising performance. Although previous studies have attempted to improve AA, they lack a standardized evaluation method and remain unsuitable for black-box models where token prediction probabilities are inaccessible. This makes comparative analysis challenging, especially for state-of-the-art closed-source commercial LLMs. This paper bridges this gap by introducing a black-box evaluation approach and a new dataset, Abstain-QA, crafted to rigorously assess AA across varied question types (answerable and unanswerable), domains (well-represented and under-represented), and task types (fact-centric and reasoning). We also propose a new confusion matrix, the {\textquotedblright}Answerable-Unanswerable Confusion Matrix{\textquotedblright} (AUCM) which serves as the basis for evaluating AA, by offering a structured and precise approach for assessment. Finally, we explore the impact of three prompting strategies {---} Strict Prompting, Verbal Confidence Thresholding, and Chain-of-Thought (CoT) {---} on improving AA. Our results indicate that even powerful models like GPT-4, Mixtral 8x22b encounter difficulties with abstention; however, strategic approaches such as Strict prompting and CoT can enhance this capability.
Dr.ECI: Infusing Large Language Models with Causal Knowledge for Decomposed Reasoning in Event Causality Identification (2025)

Despite the demonstrated potential of Large Language Models (LLMs) in diverse NLP tasks, their causal reasoning capability appears inadequate when evaluated within the context of the event causality identification (ECI) task. The ECI tasks pose significant complexity for LLMs and necessitate comprehensive causal priors for accurate identification. To improve the performance of LLMs for causal reasoning, we propose a multi-agent Decomposed reasoning framework for Event Causality Identification, designated as \textit{Dr.ECI}. In the discovery stage, \textit{Dr.ECI} incorporates specialized agents such as \textit{Causal Explorer} and \textit{Mediator Detector}, which capture implicit causality and indirect causality more effectively. In the reasoning stage, \textit{Dr.ECI} introduces the agents \textit{Direct Reasoner} and \textit{Indirect Reasoner}, which leverage the knowledge of the generalized causal structure specific to the ECI. Extensive evaluations demonstrate the state-of-the-art performance of \textit{Dr.ECI} comparing with baselines based on LLMs and supervised training. Our implementation will be open-sourced at \url{https://github.com/DMIRLAB-Group/Dr.ECI}.
InternLM-Law: An Open-Sourced Chinese Legal Large Language Model (2025)

We introduce InternLM-Law, a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws. These tasks range from responding to standard legal questions (e.g., legal exercises in textbooks) to analyzing complex real-world legal situations. Our work contributes to Chinese Legal NLP research by (1) conducting one of the most extensive evaluations of state-of-the-art general-purpose and legal-specific LLMs to date that involves an automatic evaluation on the 20 legal NLP tasks in LawBench, a human evaluation on a challenging version of the Legal Consultation task, and an automatic evaluation of a model`s ability to handle very long legal texts; (2) presenting a methodology for training a Chinese legal LLM that offers superior performance to all of its counterparts in our extensive evaluation; and (3) facilitating future research in this area by making all of our code and model publicly available at https://github.com/InternLM/InternLM-Law.
Let`s Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model (2025)

Large Language Models (LLMs) are composed of neurons that exhibit various behaviors and roles, which become increasingly diversified as models scale. Recent studies have revealed that not all neurons are active across different datasets, and this sparsity correlates positively with the task-specific ability, leading to advancements in model pruning and training efficiency. Traditional fine-tuning methods engage all parameters of LLMs, which is computationally expensive and may not be necessary. In contrast, Parameter-Efficient Fine-Tuning (PEFT) approaches aim to minimize the number of trainable parameters, yet they still operate at a relatively macro scale (e.g., layer-level). We introduce Neuron-Level Fine-Tuning (NeFT), a novel approach that refines the granularity of parameter training down to the individual neuron, enabling a more parameter-efficient fine-tuning model. The experimental results show that NeFT not only exceeded the performance of full-parameter fine-tuning and PEFT but also provided insights into the analysis of neurons. Our code and data are available at: https://github.com/NLP2CT/NeFT.
Cross-Domain Fake News Detection based on Dual-Granularity Adversarial Training (2025)

Cross-domain fake news detection, aiming to detect fake news in unseen domains, has achieved promising results with the help of pre-trained language models. Existing approaches mainly relied on extracting domain-independent representations or modeling domain discrepancies to achieve domain adaptation. However, we found that the relationship between entities in a piece of news and its corresponding label (fake or real) fluctuates among different domains. Such discrepancy is ignored by existing methods, leading to model entity bias. Therefore, in this paper, we propose a novel cross-domain fake news detection method based on dual-granularity adversarial training from the perspective of document-level and entity-level. Specifically, both the news pieces and their entities are modeled individually to construct an encoder that can generate domain-independent representations using adversarial training. Moreover, the dual-granularity soft prompt, consisting of two independent learnable segments trained on the source domains, is employed to make the model easily adapt to the unseen target domains. In addition, MultiFC, a released dataset for cross domain fake news detection, is not suitable for the evaluation due to its unreasonable domain construction rules. We artificially reconstructed the dataset and named it New-MultiFC, which is a more domain-discriminative dataset. Experimental results on both the newly constructed New-MultiFC and FND3 show the effectiveness of the proposed approach, achieving the state-of-the-art results in unseen domains.
Position Information Emerges in Causal Transformers Without Positional Encodings via Similarity of Nearby Embeddings (2025)

Transformers with causal attention can solve tasks that require positional information without using positional encodings. In this work, we propose and investigate a new hypothesis about how positional information can be stored without using explicit positional encoding. We observe that nearby embeddings are more similar to each other than faraway embeddings, allowing the transformer to potentially reconstruct the positions of tokens. We show that this pattern can occur in both the trained and the randomly initialized Transformer models with causal attention and no positional encodings over a common range of hyperparameters.
RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentation (2025)

Riddle-solving requires advanced reasoning skills, pushing Large Language Models (LLMs) to engage in abstract thinking and creative problem-solving, often revealing limitations in their cognitive abilities. In this paper, we examine the riddle-solving capabilities of LLMs using a multiple-choice format, exploring how different prompting techniques impact performance on riddles that demand diverse reasoning skills. To enhance results, we introduce RISCORE (RIddle Solving with COntext REcontruciton) a novel fully automated prompting method that generates and utilizes contextually reconstructed sentence-based puzzles in conjunction with the original examples to create few-shot exemplars. Our experiments demonstrate that RISCORE significantly improves the performance of language models in both vertical and lateral thinking tasks, surpassing traditional exemplar selection strategies across a variety of few-shot settings.
Ranking Over Scoring: Towards Reliable and Robust Automated Evaluation of LLM-Generated Medical Explanatory Arguments (2025)

Evaluating LLM-generated text has become a key challenge, especially in domain-specific contexts like the medical field. This work introduces a novel evaluation methodology for LLM-generated medical explanatory arguments, relying on Proxy Tasks and rankings to closely align results with human evaluation criteria, overcoming the biases typically seen in LLMs used as judges. We demonstrate that the proposed evaluators are robust against adversarial attacks, including the assessment of non-argumentative text. Additionally, the human-crafted arguments needed to train the evaluators are minimized to just one example per Proxy Task. By examining multiple LLM-generated arguments, we establish a methodology for determining whether a Proxy Task is suitable for evaluating LLM-generated medical explanatory arguments, requiring only five examples and two human experts.
CACA: Context-Aware Cross-Attention Network for Extractive Aspect Sentiment Quad Prediction (2025)

Aspect Sentiment Quad Prediction(ASQP) enhances the scope of aspect-based sentiment analysis by introducing the necessity to predict both explicit and implicit aspect and opinion terms. Existing leading generative ASQP approaches do not modeling the contextual relationship of the review sentence to predict implicit terms. However, introducing the contextual information into the pre-trained language models framework is non-trivial due to the inflexibility of the generative encoder-decoder architecture. To well utilize the contextual information, we propose an extractive ASQP framework, CACA, which features with \textit{Context-Aware Cross-Attention Network}. When implicit terms are present, the \textit{Context-Aware Cross-Attention Network} enhances the alignment of aspects and opinions, through alternating updates of explicit and implicit representations. Additionally, contrastive learning is introduced in the implicit representation learning process. Experimental results on three benchmarks demonstrate the effectiveness of CACA. Our implementation will be open-sourced at \url{https://github.com/DMIRLAB-Group/CACA}.
Improved Sparse Upcycling for Instruction Tuning (2025)

The Mixture-of-Experts (MoE) architecture has demonstrated significant potential in both large-scale pre-training and instruction tuning by offering increased parameter capacity without additional inference costs. However, developing MoE models faces challenges including training instability and the need for substantial high-quality training data. While efficient methodologies like sparse upcycling exist, they often lead to performance degradation in instruction tuning scenarios. We introduce representation-based sparse upcycling, a straightforward yet effective technique for converting dense language models into sparsely activated ones while maintaining similar computational costs. Unlike conventional sparse upcycling, our approach leverages intermediate representations from language models to initialize router weights. This strategy addresses the mismatch between randomly initialized and well-trained parameters while providing prior knowledge to guide expert specialization during training. Extensive experiments across diverse benchmarks demonstrate significant improvements in both model capabilities and routing consistency compared to existing approaches.
SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment (2025)

Despite the significant improvements achieved by large language models (LLMs) in English reasoning tasks, these models continue to struggle with multilingual reasoning. Recent studies leverage a full-parameter and two-stage training paradigm to teach models to first understand non-English questions and then reason. However, this method suffers from both substantial computational resource computing and catastrophic forgetting. The fundamental cause is that, with the primary goal of enhancing multilingual comprehension, an excessive number of irrelevant layers and parameters are tuned during the first stage. Given our findings that the representation learning of languages is merely conducted in lower-level layers, we propose an efficient multilingual reasoning alignment approach that precisely identifies and fine-tunes the layers responsible for handling multilingualism. Experimental results show that our method, SLAM, only tunes 6 layers' feed-forward sub-layers including 6.5-8{\%} of all parameters within 7B and 13B LLMs, achieving superior average performance than all strong baselines across 10 languages. Meanwhile, SLAM only involves one training stage, reducing training time by 4.1-11.9{\texttimes} compared to the two-stage method.
ME2-BERT: Are Events and Emotions what you need for Moral Foundation Prediction? (2025)

Moralities, emotions, and events are complex aspects of human cognition, which are often treated separately since capturing their combined effects is challenging, especially due to the lack of annotated data. Leveraging their interrelations hence becomes crucial for advancing the understanding of human moral behaviors. In this work, we propose ME2-BERT, the first holistic framework for fine-tuning a pre-trained language model like BERT to the task of moral foundation prediction. ME2-BERT integrates events and emotions for learning domain-invariant morality-relevant text representations. Our extensive experiments show that ME2-BERT outperforms existing state-of-the-art methods for moral foundation prediction, with an average increase up to 35{\%} in the out-of-domain scenario.
SCCD: A Session-based Dataset for Chinese Cyberbullying Detection (2025)

The rampant spread of cyberbullying content poses a growing threat to societal well-being. However, research on cyberbullying detection in Chinese remains underdeveloped, primarily due to the lack of comprehensive and reliable datasets. Notably, no existing Chinese dataset is specifically tailored for cyberbullying detection. Moreover, while comments play a crucial role within sessions, current session-based datasets often lack detailed, fine-grained annotations at the comment level. To address these limitations, we present a novel Chinese cyberbullying dataset, termed SCCD, which consists of 677 session-level samples sourced from a major social media platform Weibo. Moreover, each comment within the sessions is annotated with fine-grained labels rather than conventional binary class labels. Empirically, we evaluate the performance of various baseline methods on SCCD, highlighting the challenges for effective Chinese cyberbullying detection.
Hands-off Image Editing: Language-guided Editing without any Task-specific Labeling, Masking or even Training (2025)

Instruction-guided image editing consists in taking an image and an instruction and delivering that image altered according to that instruction. State-of-the-art approaches to this task suffer from the typical scaling up and domain adaptation hindrances related to supervision as they eventually resort to some kind of task-specific labelling, masking or training. We propose a novel approach that does without any such task-specific supervision and offers thus a better potential for improvement. Its assessment demonstrates that it is highly effective, achieving very competitive performance.
Beyond Film Subtitles: Is YouTube the Best Approximation of Spoken Vocabulary? (2025)

Word frequency is a key variable in psycholinguistics, useful for modeling human familiarity with words even in the era of large language models (LLMs). Frequency in film subtitles has proved to be a particularly good approximation of everyday language exposure. For many languages, however, film subtitles are not easily available, or are overwhelmingly translated from English. We demonstrate that frequencies extracted from carefully processed YouTube subtitles provide an approximation comparable to, and often better than, the best currently available resources. Moreover, they are available for languages for which a high-quality subtitle or speech corpus does not exist. We use YouTube subtitles to construct frequency norms for five diverse languages, Chinese, English, Indonesian, Japanese, and Spanish, and evaluate their correlation with lexical decision time, word familiarity, and lexical complexity. In addition to being strongly correlated with two psycholinguistic variables, a simple linear regression on the new frequencies achieves a new high score on a lexical complexity prediction task in English and Japanese, surpassing both models trained on film subtitle frequencies and the LLM GPT-4. We publicly release our code, the frequency lists, fastText word embeddings, and statistical language models.