ymoslem/acl-abstracts huggingface dataset:
Gen-SQL: Efficient Text-to-SQL By Bridging Natural Language Question And Database Schema With Pseudo-Schema (2025)

With the prevalence of Large Language Models (LLMs), recent studies have shifted paradigms and leveraged LLMs to tackle the challenging task of Text-to-SQL. Because of the complexity of real world databases, previous works adopt the retrieve-then-generate framework to retrieve relevant database schema and then to generate the SQL query. However, efficient embedding-based retriever suffers from lower retrieval accuracy, and more accurate LLM-based retriever is far more expensive to use, which hinders their applicability for broader applications. To overcome this issue, this paper proposes Gen-SQL, a novel generate-ground-regenerate framework, where we exploit prior knowledge from the LLM to enhance embedding-based retriever and reduce cost. Experiments on several datasets are conducted to demonstrate the effectiveness and scalability of our proposed method. We release our code and data at https://github.com/jieshi10/gensql.
Language Models at the Syntax-Semantics Interface: A Case Study of the Long-Distance Binding of Chinese Reflexive Ziji (2025)

This paper explores whether language models can effectively resolve the complex binding patterns of the Mandarin Chinese reflexive \textit{ziji}, which are constrained by both syntactic and semantic factors. We construct a dataset of 320 synthetic sentences using templates and examples from syntactic literature, along with 360 natural sentences from the BCC corpus. Evaluating 21 language models against this dataset and comparing their performance to judgments from native Mandarin speakers, we find that none of the models consistently replicates human-like judgments. The results indicate that existing language models tend to rely heavily on sequential cues, though not always favoring the closest strings, and often overlooking subtle semantic and syntactic constraints. They tend to be more sensitive to noun-related than verb-related semantics.
HyperHatePrompt: A Hypergraph-based Prompting Fusion Model for Multimodal Hate Detection (2025)

Multimodal hate detection aims to identify hate content across multiple modalities for promoting a harmonious online environment. Despite promising progress, three critical challenges, \textit{the absence of implicit hateful cues}, \textit{the cross-modal-induced hate}, and \textit{the diversity of hate target groups}, inherent in the multimodal hate detection task, have been overlooked. To address these challenges, we propose a hypergraph-based prompting fusion model. Our model first uses tailored prompts to infer implicit hateful cues. It then introduces hyperedges to capture cross-modal-induced hate and applies a diversity-oriented hyperedge expansion strategy to account for different hate target groups. Finally, hypergraph convolution fuses diverse hateful cues, enhancing the exploration of cross-modal hate and targeting specific groups. Experimental results on two benchmark datasets show that our model achieves state-of-the-art performance in multimodal hate detection.
GenWebNovel: A Genre-oriented Corpus of Entities in Chinese Web Novels (2025)

Entities are important to understanding literary works, which emphasize characters, plots and environment. The research on entity recognition, especially nested entity recognition in the literary domain is still insufficient partly due to insufficient annotated data. To address this issue, we construct the first Genre-oriented Corpus for Entity Recognition in Chinese Web Novels, namely GenWebNovel, comprising 400 chapters totaling 1,214,283 tokens under two genres, XuanHuan (Eastern Fantasy) and History. Based on the corpus, we analyze the distribution of different types of entities, including person, location, and organization. We also compare the nesting patterns of nested entities between GenWebNovel and the English corpus LitBank. Even though both belong to the literary domain, entities in different genres share few overlaps, making genre adaptation of NER (Named Entity Recognition) a hard problem. We propose a novel method that utilizes a pre-trained language model as an In-context learning example retriever to boost the performance of large language models. Our experiments show that this approach significantly enhances entity recognition, matching state-of-the-art (SOTA) models without requiring additional training data. Our code, dataset, and model are available at https://github.com/hjzhao73/GenWebNovel.
Automated Progressive Red Teaming (2025)

Ensuring the safety of large language models (LLMs) is paramount, yet identifying potential vulnerabilities is challenging. While manual red teaming is effective, it is time-consuming, costly and lacks scalability. Automated red teaming (ART) offers a more cost-effective alternative, automatically generating adversarial prompts to expose LLM vulnerabilities. However, in current ART efforts, a robust framework is absent, which explicitly frames red teaming as an effectively learnable task. To address this gap, we propose Automated Progressive Red Teaming (APRT) as an effectively learnable framework. APRT leverages three core modules: an Intention Expanding LLM that generates diverse initial attack samples, an Intention Hiding LLM that crafts deceptive prompts, and an Evil Maker to manage prompt diversity and filter ineffective samples. The three modules collectively and progressively explore and exploit LLM vulnerabilities through multi-round interactions. In addition to the framework, we further propose a novel indicator, Attack Effectiveness Rate (AER) to mitigate the limitations of existing evaluation metrics. By measuring the likelihood of eliciting unsafe but seemingly helpful responses, AER aligns closely with human evaluations. Extensive experiments with both automatic and human evaluations, demonstrate the effectiveness of ARPT across both open- and closed-source LLMs. Specifically, APRT effectively elicits 54{\%} unsafe yet useful responses from Meta`s Llama-3-8B-Instruct, 50{\%} from GPT-4o (API access), and 39{\%} from Claude-3.5 (API access), showcasing its robust attack capability and transferability across LLMs (especially from open-source LLMs to closed-source LLMs).
Rumor Detection on Social Media with Temporal Propagation Structure Optimization (2025)

Traditional methods for detecting rumors on social media primarily focus on analyzing textual content, often struggling to capture the complexity of online interactions. Recent research has shifted towards leveraging graph neural networks to model the hierarchical conversation structure that emerges during rumor propagation. However, these methods tend to overlook the temporal aspect of rumor propagation and may disregard potential noise within the propagation structure. In this paper, we propose a novel approach that incorporates temporal information by constructing a weighted propagation tree, where the weight of each edge represents the time interval between connected posts. Drawing upon the theory of structural entropy, we transform this tree into a coding tree. This transformation aims to preserve the essential structure of rumor propagation while reducing noise. Finally, we introduce a recursive neural network to learn from the coding tree for rumor veracity prediction. Experimental results on two common datasets demonstrate the superiority of our approach.
Revisiting Implicitly Abusive Language Detection: Evaluating LLMs in Zero-Shot and Few-Shot Settings (2025)

Implicitly abusive language (IAL), unlike its explicit counterpart, lacks overt slurs or unambiguously offensive keywords, such as {\textquotedblleft}bimbo{\textquotedblright} or {\textquotedblleft}scum{\textquotedblright}, making it challenging to detect and mitigate. While current research predominantly focuses on explicitly abusive language, the subtler and more covert forms of IAL remain insufficiently studied. The rapid advancement and widespread adoption of large language models (LLMs) have opened new possibilities for various NLP tasks, but their application to IAL detection has been limited. We revisit three very recent challenging datasets of IAL and investigate the potential of LLMs to enhance the detection of IAL in English through zero-shot and few-shot prompting approaches. We evaluate the models' capabilities in classifying sentences directly as either IAL or benign, and in extracting linguistic features associated with IAL. Our results indicate that classifiers trained on features extracted by advanced LLMs outperform the best previously reported results, achieving near-human performance.
Grading Massive Open Online Courses Using Large Language Models (2025)

Massive open online courses (MOOCs) offer free education globally. Despite this democratization of learning, the massive enrollment in these courses makes it impractical for an instructor to assess every student`s writing assignment. As a result, peer grading, often guided by a straightforward rubric, is the method of choice. While convenient, peer grading often falls short in terms of reliability and validity. In this study, we explore the feasibility of using large language models (LLMs) to replace peer grading in MOOCs. To this end, we adapt the zero-shot chain-of-thought (ZCoT) prompting technique to automate the feedback process once the LLM assigns a score to an assignment. Specifically, to instruct LLMs for grading, we use three distinct prompts based on ZCoT: (1) ZCoT with instructor-provided correct answers, (2) ZCoT with both instructor-provided correct answers and rubrics, and (3) ZCoT with instructor-provided correct answers and LLM-generated rubrics. We tested these prompts in 18 different scenarios using two LLMs{---}GPT-4 and GPT-3.5{---}across three MOOCs: Introductory Astronomy, Astrobiology, and the History and Philosophy of Astronomy. Our results show that ZCoT, when augmented with instructor-provided correct answers and rubrics, produces grades that are more aligned with those assigned by instructors compared to peer grading. Finally, our findings indicate a promising potential for automated grading systems in MOOCs, especially in subjects with well-defined rubrics, to improve the learning experience for millions of online learners worldwide.
Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks (2025)

The impact of social media on critical issues such as echo chambers, needs to be addressed, as these phenomena can have disruptive consequences for our society. Traditional research often oversimplifies emotional tendencies and opinion evolution into numbers and formulas, neglecting that news and communication are conveyed through text, which limits these approaches. Hence, in this work, we propose an LLM-based simulation for the social opinion network to evaluate and counter polarization phenomena. We first construct three typical network structures to simulate different characteristics of social interactions. Then, agents interact based on recommendation algorithms and update their strategies through reasoning and analysis. By comparing these interactions with the classic Bounded Confidence Model (BCM), the Friedkin-Johnsen (FJ) model, and using echo chamber-related indices, we demonstrate the effectiveness of our framework in simulating opinion dynamics and reproducing phenomena such as opinion polarization and echo chambers. We propose two mitigation methods{---}active and passive nudges{---}that can help reduce echo chambers, specifically within language-based simulations. We hope our work will offer valuable insights and guidance for social polarization mitigation.
Parameter-Efficient Fine-Tuning of Large Language Models via Deconvolution in Subspace (2025)

This paper proposes a novel parameter-efficient fine-tuning method that combines the knowledge completion capability of deconvolution with the subspace learning ability, reducing the number of parameters required for fine-tuning by 8 times . Experimental results demonstrate that our method achieves superior training efficiency and performance compared to existing models.
StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal Large Language Models (2025)

The rapid development of multimodal large language models (MLLMs) has positioned visual storytelling as a crucial area in content creation. However, existing models often struggle to maintain temporal, spatial, and narrative coherence across image sequences, and they frequently lack the depth and engagement of human-authored stories. To address these challenges, we propose Story with Large Language-and-Vision Alignment (StoryLLaVA), a novel framework for enhancing visual storytelling. Our approach introduces a topic-driven narrative optimizer that improves both the training data and MLLM models by integrating image descriptions, topic generation, and GPT-4-based refinements. Furthermore, we employ a preference-based ranked story sampling method that aligns model outputs with human storytelling preferences through positive-negative pairing. These two phases of the framework differ in their training methods: the former uses supervised fine-tuning, while the latter incorporates reinforcement learning with positive and negative sample pairs. Experimental results demonstrate that StoryLLaVA outperforms current models in visual relevance, coherence, and fluency, with LLM-based evaluations confirming the generation of richer and more engaging narratives. The enhanced dataset and model will be made publicly available soon.
Aligning Complex Knowledge Graph Question Answering as Knowledge-Aware Constrained Code Generation (2025)

Generating executable logical forms (LF) using Large Language Models (LLMs) in a few-shot setting for Knowledge Graph Question Answering (KGQA) is becoming popular. However, their performance is still limited due to very little exposure to the LF during pre-training of LLMs, resulting in many syntactically incorrect LF generation. If the LF generation task can be transformed to a more familiar task for the LLM, it can potentially reduce the syntax errors and elevate the generation quality. On the other hand, there exist specialized LLMs trained/fine-tuned on code in many programming languages. They can be leveraged to generate the LF as step-wise constrained code expression generation using modular functions in the LF. Based on this insight, we propose CodeAlignKGQA: a framework that aligns the LF generation as code generation that incorporates LF-specific constraints. We extract the question-specific subgraph information to enable Knowledge-Aware code generation. We additionally introduce a dynamic self-code-correction mechanism, to be applied as required. Our extensive experiments on Complex KGQA benchmarks such as KQA Pro demonstrate the effectiveness of our approach. CodeAlignKGQA surpasses all few-shot baselines on KQA Pro by 21{\%}, achieving a new state-of-the-art.
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting (2025)

Making analogies is fundamental to cognition. Proportional analogies, which consist of four terms, are often used to assess linguistic and cognitive abilities. For instance, completing analogies like {\textquotedblleft}Oxygen is to Gas as {\ensuremath{<}} blank {\ensuremath{>}} is to {\ensuremath{<}} blank {\ensuremath{>}}'' requires identifying the semantic relationship (e.g., {\textquotedblleft}type of{\textquotedblright}) between the first pair of terms ({\textquotedblleft}Oxygen{\textquotedblright} and {\textquotedblleft}Gas{\textquotedblright}) and finding a second pair that shares the same relationship (e.g., {\textquotedblleft}Aluminum{\textquotedblright} and {\textquotedblleft}Metal{\textquotedblright}). In this work, we introduce a 15K Multiple-Choice Question Answering (MCQA) dataset for proportional analogy completion and evaluate the performance of contemporary Large Language Models (LLMs) in various knowledge-enhanced prompt settings. Specifically, we augment prompts with three types of knowledge: exemplar, structured, and targeted. Our results show that despite extensive training data, solving proportional analogies remains challenging for current LLMs, with the best model achieving an accuracy of 55{\%}. Notably, we find that providing targeted knowledge can better assist models in completing proportional analogies compared to providing exemplars or collections of structured knowledge. Our code and data are available at: \url{https://github.com/Thiliniiw/KnowledgePrompts/}
Unified Grid Tagging Scheme for Aspect Sentiment Quad Prediction (2025)

Aspect Sentiment Quad Prediction (ASQP) aims to extract all sentiment elements in quads for a given review to explain the reason for the sentiment. Previous table-filling based methods have achieved promising results by modeling word-pair relations. However, these methods decompose the ASQP task into several subtasks without considering the association between sentiment elements. Most importantly, they fail to tackle the situation where a sentence contains multiple implicit expressions. To address these limitations, we propose a simple yet effective Unified Grid Tagging Scheme (UGTS) to extract sentiment quadruplets in one shot, with two additional special tokens from pre-trained models to represent potential implicit aspect and opinion terms. Based on this, we first introduce the adaptive graph diffusion convolution network to construct the direct connection between explicit and implicit sentiment elements from syntactic and semantic views. Next, we utilize conditional layer normalization to refine the mutual indication effect between words for matching valid aspect-opinion pairs. Finally, we employ the triaffine mechanism to integrate heterogeneous word-pair relations to capture higher-order interactions between sentiment elements. Experimental results on four benchmark datasets show the effectiveness and robustness of our model, which achieves state-of-the-art performance.
Claim veracity assessment for explainable fake news detection (2025)

With the rapid growth of social network services, misinformation has spread uncontrollably. Most recent approaches to fake news detection use neural network models to predict whether the input text is fake or real. Some of them even provide explanations, in addition to veracity, generated by Large Language Models (LLMs). However, they do not utilize factual evidence, nor do they allude to it or provide evidence/justification, thereby making their predictions less credible. This paper proposes a new fake news detection method that predicts the truth or false-hood of a claim based on relevant factual evidence (if exists) or LLM`s inference mechanisms (such as common-sense reasoning) otherwise. Our method produces the final synthesized prediction, along with well-founded facts or reasoning. Experimental results on several large COVID-19 fake news datasets show that our method achieves state-of-the-art (SOTA) detection and evidence explanation performance. Our source codes are available online.
ACE-$M^3$: Automatic Capability Evaluator for Multimodal Medical Models (2025)

As multimodal large language models (MLLMs) gain prominence in the medical field, the need for precise evaluation methods to assess their effectiveness has become critical. While benchmarks provide a reliable means to evaluate the capabilities of MLLMs, traditional metrics like ROUGE and BLEU employed for open domain evaluation only focus on token overlap and may not align with human judgment. While human evaluation is more reliable, it is labor-intensive, costly, and not scalable. LLM-based evaluation methods have proven promising, but to date, there is still an urgent need for open-source multimodal LLM-based evaluators in the medical field. To address this issue, we introduce ACE-$M^3$, an open-sourced \textbf{A}utomatic \textbf{C}apability \textbf{E}valuator for \textbf{M}ultimodal \textbf{M}edical \textbf{M}odels that specifically designed to assess the question answering abilities of medical MLLMs. It first utilizes a branch-merge architecture to provide both detailed analysis and a concise final score based on standard medical evaluation criteria. Subsequently, a reward token-based direct preference optimization (RTDPO) strategy is incorporated to save training time without compromising performance of our model. Extensive experiments have demonstrated the effectiveness of our ACE-$M^3$ model in evaluating the capabilities of medical MLLMs.
A Dual Contrastive Learning Framework for Enhanced Multimodal Conversational Emotion Recognition (2025)

Multimodal Emotion Recognition in Conversations (MERC) identifies utterance emotions by integrating both contextual and multimodal information from dialogue videos. Existing methods struggle to capture emotion shifts due to label replication and fail to preserve positive independent modality contributions during fusion. To address these issues, we propose a Dual Contrastive Learning Framework (DCLF) that enhances current MERC models without additional data. Specifically, to mitigate label replication effects, we construct context-aware contrastive pairs. Additionally, we assign pseudo-labels to distinguish modality-specific contributions. DCLF works alongside basic models to introduce semantic constraints at the utterance, context, and modality levels. Our experiments on two MERC benchmark datasets demonstrate performance gains of 4.67{\%}-4.98{\%} on IEMOCAP and 5.52{\%}-5.89{\%} on MELD, outperforming state-of-the-art approaches. Perturbation tests further validate DCLF`s ability to reduce label dependence. Additionally, DCLF incorporates emotion-sensitive independent modality features and multimodal fusion representations into final decisions, unlocking the potential contributions of individual modalities.
Can LLMs Clarify? Investigation and Enhancement of Large Language Models on Argument Claim Optimization (2025)

In argumentation, the claim is the foundational proposition that underpins the argument, serving as the central pillar upon which the argument is constructed. It guides the subsequent presentation of evidence, reasoning, and analysis, thereby facilitating the audience`s understanding of the core issue. Therefore, ensuring that the claim is precise and unambiguous is crucial for constructing a coherent and persuasive argument. While Large Language Models (LLMs) have demonstrated proficiency in text rewriting tasks such as style transfer and query rewriting, their application to claim optimization remains unexplored. Unlike other rewriting tasks, claim clarification requires the model to rewrite ambiguous or unclear segments of the claim, enhance the content by adding omitted key details, and eliminate redundant or verbose elements. Addressing this gap, this paper evaluates the performance of LLMs on the claim clarification task across various settings. While popular rewriting evaluation methods such as BLEU and Rouge rely on exact word matching, this paper introduces a novel semantic evaluation approach based on a sliding window mechanism. Three distinct LLMs, including LLama2, Mistral, and Qwen2, are assessed for their ability to clarify arguments through zero-shot or few-shot prompting, and supervised fine-tuning (SFT). Additionally, we propose a reinforcement learning-based clarification approach that optimally balances content preservation with claim clarity, thereby augmenting the performance of LLMs on the claim clarification task.