GenAI4ELab/papercli-papers huggingface dataset:
O_O-VC: Synthetic Data-Driven One-to-One Alignment for Any-to-Any Voice Conversion
Authors: Huu Tuong Tu; Huan Vu; Cuong Tien Nguyen; Dien Hy Ngo; Nguyen Thi Thu Trang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Traditional voice conversion (VC) methods typically attempt to separate speaker identity and linguistic information into distinct representations, which are then combined to reconstruct the audio. However, effectively disentangling these factors remains challenging, often leading to information loss during training. In this paper, we propose a new approach that leverages synthetic speech data generated by a high-quality, pretrained multispeaker text-to-speech (TTS) model. Specifically, synthetic data pairs that share the same linguistic content but differ in speaker identity are used as input-output pairs to train the voice conversion model. This enables the model to learn a direct mapping between source and target voices, effectively capturing speaker-specific characteristics while preserving linguistic content. Additionally, we introduce a flexible training strategy for any-to-any voice conversion that generalizes well to unseen speakers and new languages, enhancing adaptability and performance in zero-shot scenarios. Our experiments show that our proposed method achieves a 16.35% relative reduction in word error rate and a 5.91% improvement in speaker cosine similarity, outperforming several state-of-the-art methods. Voice conversion samples can be accessed at: https://oovc-emnlp-2025.github.io/
Simple Factuality Probes Detect Hallucinations in Long-Form Natural Language Generation
Authors: Jiatong Han; Neil Band; Muhammed Razzak; Jannik Kossen; Tim G. J. Rudner; Yarin Gal
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large language models (LLMs) often mislead users with confident hallucinations. Current approaches to detect hallucination require many samples from the LLM generator, which is computationally infeasible as frontier model sizes and generation lengths continue to grow. We present a remarkably simple baseline for detecting hallucinations in long-form LLM generations, with performance comparable to expensive multi-sample approaches while drawing only a single sample from the LLM generator. Our key finding is that LLM hidden states are highly predictive of factuality in long-form natural language generation and that this information can be efficiently extracted at inference time using a lightweight probe. We benchmark a variety of long-form hallucination detection methods across open-weight models up to 405B parameters and demonstrate that our approach achieves competitive performance with up to 100x fewer FLOPs. Furthermore, our probes generalize to out-of-distribution model outputs, evaluated using hidden states of smaller open-source models. Our results demonstrate the promise of hidden state probes in detecting long-form LLM hallucinations.
CESRec: Constructing Pseudo Interactions for Sequential Recommendation via Conversational Feedback
Authors: Yifan Wang; Shen Gao; Jiabao Fang; Rui Yan; Billy Chiu; Shuo Shang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Sequential Recommendation Systems (SRS) have become essential in many real-world applications. However, existing SRS methods often rely on collaborative filtering signals and fail to capture real-time user preferences, while Conversational Recommendation Systems (CRS) excel at eliciting immediate interests through natural language interactions but neglect historical behavior. To bridge this gap, we propose CESRec, a novel framework that integrates the long-term preference modeling of SRS with the real-time preference elicitation of CRS. We introduce semantic-based pseudo interaction construction, which dynamically updates users’ historical interaction sequences by analyzing conversational feedback, generating a pseudo-interaction sequence that seamlessly combines long-term and real-time preferences. Additionally, we reduce the impact of outliers in historical items that deviate from users’ core preferences by proposing dual alignment outlier items masking, which identifies and masks such items using semantic-collaborative aligned representations. Extensive experiments demonstrate that CESRec achieves state-of-the-art performance by boosting strong SRS models, validating its effectiveness in integrating conversational feedback into SRS.
TTPA: Token-level Tool-use Preference Alignment Training Framework with Fine-grained Evaluation
Authors: Chengrui Huang; Shen Gao; Zhengliang Shi; Dongsheng Wang; Shuo Shang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Existing tool-learning methods usually rely on supervised fine-tuning, they often overlook fine-grained optimization of internal tool call details, leading to limitations in preference alignment and error discrimination. To overcome these challenges, we propose **T**oken-level **T**ool-use **P**reference **A**lignment Training Framework (TTPA), a training paradigm for constructing token-level tool-use preference datasets that align LLMs with fine-grained preferences using a novel error-oriented scoring mechanism. TTPA first introduces reversed dataset construction, a method for creating high-quality, multi-turn tool-use datasets by reversing the generation flow. Additionally, we propose _Preference Oriented Tool-use Dataset Construction_ to capture fine-grained preferences by modeling token-level differences during generation. To address biases in scoring, we introduce the _Error-oriented Scoring Mechanism_, which quantifies tool-call errors and can be used as a training signal. Extensive experiments on three diverse benchmark datasets demonstrate that TTPA significantly improves tool-using performance while showing strong generalization ability across models and datasets.
Avoiding Knowledge Edit Skipping in Multi-hop Question Answering with Guided Decomposition
Authors: Yi Liu; Xiangrong Zhu; Xiangyu Liu; Wei Wei; Wei Hu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: In a rapidly evolving world where information updates swiftly, knowledge in large language models (LLMs) becomes outdated quickly. Retraining LLMs is not a cost-effective option, making knowledge editing (KE) without modifying parameters particularly necessary. We find that although existing retrieval-augmented generation (RAG)-based KE methods excel at editing simple knowledge, they struggle with KE in multi-hop question answering due to the issue of ”edit skipping”, which refers to skipping the relevant edited fact in inference. In addition to the diversity of natural language expressions of knowledge, edit skipping also arises from the mismatch between the granularity of LLMs in problem-solving and the facts in the edited memory. To address this issue, we propose a novel Iterative Retrieval-Augmented Knowledge Editing method with guided decomposition (IRAKE) through the guidance from single edited facts and entire edited cases. Experimental results demonstrate that IRAKE mitigates the failure of editing caused by edit skipping and outperforms state-of-the-art methods for KE in multi-hop question answering.
Bridging the Creativity Understanding Gap: Small-Scale Human Alignment Enables Expert-Level Humor Ranking in LLMs
Authors: Kuan Lok Zhou; Jiayi Chen; Siddharth Suresh; Reuben Narad; Timothy T. Rogers; Lalit K Jain; Robert D Nowak; Bob Mankoff; Jifan Zhang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large Language Models (LLMs) have shown significant limitations in understanding creative content, as demonstrated by Hessel et al. (2023)’s influential work on the New Yorker Cartoon Caption Contest (NYCCC). Their study exposed a substantial gap between LLMs and humans in humor comprehension, establishing that understanding and evaluating creative content is key challenge in AI development. We revisit this challenge by decomposing humor understanding into three components and systematically improve each: enhancing visual understanding through improved annotation, utilizing LLM-generated humor reasoning and explanations, and implementing targeted alignment with human preference data. Our refined approach achieves 82.4% accuracy in caption ranking, significantly improving upon the previous 67% benchmark and matching the performance of world-renowned human experts in this domain. Notably, while attempts to mimic subgroup preferences through various persona prompts showed minimal impact, model finetuning with crowd preferences proved remarkably effective. These findings reveal that LLM limitations in creative judgment can be effectively addressed through focused alignment to specific subgroups and individuals. Lastly, we propose the position that achieving artificial general intelligence necessitates systematic collection of human preference data across creative domains. We advocate that just as human creativity is deeply influenced by individual and cultural preferences, training LLMs with diverse human preference data may be essential for developing true creative understanding.
SMARTMiner: Extracting and Evaluating SMART Goals from Low-Resource Health Coaching Notes
Authors: Iva Bojic; Qi Chwen Ong; Stephanie Hilary Xinyi Ma; Lin Ai; Zheng Liu; Ziwei Gong; Julia Hirschberg; Andy Hau Yan Ho; Andy W. H. Khong
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: We present SMARTMiner, a framework for extracting and evaluating specific, measurable, attainable, relevant, time-bound (SMART) goals from unstructured health coaching (HC) notes. Developed in response to challenges observed during a clinical trial, the SMARTMiner achieves two tasks: (i) extracting behavior change goal spans and (ii) categorizing their SMARTness. We also introduce SMARTSpan, the first publicly available dataset of 173 HC notes annotated with 266 goals and SMART attributes. SMARTMiner incorporates an extractive goal retriever with a component-wise SMARTness classifier. Experiment results show that extractive models significantly outperformed their generative counterparts in low-resource settings, and that two-stage fine-tuning substantially boosted performance. The SMARTness classifier achieved up to 0.91 SMART F1 score, while the full SMARTMiner maintained high end-to-end accuracy. This work bridges healthcare, behavioral science, and natural language processing to support health coaches and clients with structured goal tracking - paving way for automated weekly goal reviews between human-led HC sessions. Both the code and the dataset are available at: https://github.com/IvaBojic/SMARTMiner.
GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models
Authors: Jialin Chen; Houyu Zhang; Seongjun Yun; Alejandro Mottini; Rex Ying; Xiang Song; Vassilis N. Ioannidis; Zheng Li; Qingjun Cui
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Retrieval-Augmented Generation (RAG) has significantly mitigated the hallucinations of Large Language Models (LLMs) by grounding the generation with external knowledge. Recent extensions of RAG to graph-based retrieval offer a promising direction, leveraging the structural knowledge for multi-hop reasoning. However, existing graph RAG typically decouples retrieval and reasoning processes, which prevents the retriever from adapting to the reasoning needs of the LLM. They also struggle with scalability when performing multi-hop expansion over large-scale graphs, or depend heavily on annotated ground-truth entities, which are often unavailable in open-domain settings. To address these challenges, we propose a novel graph retriever trained end-to-end with LLM, which features an attention-based growing and pruning mechanism, adaptively navigating multi-hop relevant entities while filtering out noise. Within the extracted subgraph, structural knowledge and semantic features are encoded via soft tokens and the verbalized graph, respectively, which are infused into the LLM together, thereby enhancing its reasoning capability and facilitating interactive joint training of the graph retriever and the LLM reasoner. Experimental results across three QA benchmarks show that our approach consistently achieves state-of-the-art performance, validating the strength of joint graph–LLM optimization for complex reasoning tasks. Notably, our framework eliminates the need for predefined ground-truth entities by directly optimizing the retriever using LLM logits as implicit feedback, making it especially effective in open-domain settings.
Exploring Deductive and Inductive Reasoning Capabilities of Large Language Models in Procedural Planning
Authors: Jiabao Kang; Xinye Li; Liyan Xu; Qingbin Liu; Xi Chen; Zhiying Tu; Dianhui Chu; Dianbo Sui
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Deductive and inductive reasoning are fundamental components of human cognition, and in daily life, people often apply these types of reasoning unconsciously. While previous studies have extensively examined the deductive and inductive reasoning abilities of Large Language Models (LLMs) in rule-based and math-related tasks, little attention has been given to their role in procedural planning——an area that holds considerable relevance for real-world applications. To fill this gap, we present DIRPP (Deductive and Inductive Reasoning in Procedural Planning) in this paper, a benchmark designed to assess the deductive and inductive reasoning abilities of various LLMs within the context of procedural planning. Based on the benchmark, we initially observe that LLMs demonstrate excellent deductive reasoning capabilities in procedural planning but show suboptimal performance in inductive reasoning. To enhance their inductive reasoning abilities, we further propose a novel and effective method called IMSE (Induction through Multiple Similar Examples), which enables LLMs to generate multiple similar procedural plans and then perform inductive reasoning based on these examples. Through various experiments, we find that the proposed method can significantly improve the inductive reasoning capabilities of LLMs.
KELE: A Multi-Agent Framework for Structured Socratic Teaching with Large Language Models
Authors: Xian Peng; Pan Yuan; Dong Li; Junlong Cheng; Qin Fang; Zhi Liu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Socratic teaching, known for its emphasis on heuristic questioning and deep thinking, has demonstrated significant advantages in promoting students’ cognitive development. However, traditional Socratic teaching places high demands on teachers’ expertise and real-time feedback capabilities, making it difficult to scale in large educational settings. Recent breakthroughs in large language models (LLMs) in natural language generation and dialogue comprehension offer the potential for automated Socratic teaching. In this paper, we propose Knowledge-Enlightened Learning Enhanced by LLMs (KELE), a novel multi-agent framework for structured Socratic teaching with LLMs. KELE constructs a structured Socratic teaching rule system (SocRule) and a “consultant–teacher” multi-agent collaborative teaching mechanism, in which two LLMs respectively take charge of teaching planning and execution, ensuring a logically coherent and hierarchically structured Socratic teaching process. We also construct SocratDataset, a structured Socratic teaching dataset covering 34 teaching strategies and over 42,000 dialogue turns, and train SocratTeachLLM, a specialized LLM for Socratic teaching tasks. Additionally, we build a comprehensive Socratic teaching quality evaluation system for LLMs, covering 9 dimensions from single-turn dialogue to multi-turn teaching processes. Experimental results show that SocratTeachLLM significantly outperforms GPT-4o, which has a much larger parameter size, across all Socratic teaching capabilities.
VisualEDU: A Benchmark for Assessing Coding and Visual Comprehension through Educational Problem-Solving Video Generation
Authors: Hao Chen; Tianyu Shi; Pengran Huang; Zeyuan Li; Jiahui Pan; Qianglong Chen; Lewei He
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Generating logically coherent video from text (T2V) for reasoning-intensive tasks like mathematical problem-solving presents a significant challenge for Vision-Language Models (VLMs). Therefore, we introduce VisualEDU, a benchmark based on Manim package to rigorously evaluate VLM capabilities in producing coherent, step-by-step video solutions for educational purposes, with a framework that integrates meta-prompt learning, visual and code feedback, and a modular drawing toolkit to enhance output quality. Novel metrics for temporal consistency, logical correctness, and visual clarity are proposed, and extensive experiments across nine VLMs reveal that while advanced proprietary models show promise, all struggle significantly with increasing task complexity (e.g., the performances of Claude-3.7-Sonnet and GPT-4o are below 56% on difficult tasks ), highlighting limitations in code generation, visual feedback correction and precise tool invocation. VisualEDU offers a robust platform for systematic T2V assessment in reasoning-intensive domains and guides future VLM improvements in this area.
OkraLong: A Flexible Retrieval-Augmented Framework for Long-Text Question Answering
Authors: Yulong Hui; Yihao Liu; Yao Lu; Huanchen Zhang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large Language Models (LLMs) encounter challenges in efficiently answering long-text questions, as seen in applications like enterprise document analysis and financial report comprehension. While conventional solutions employ long-context processing or Retrieval-Augmented Generation (RAG), they suffer from prohibitive input expenses or incomplete information. Recent advancements adopt context compression and dynamic retrieval loops, but still sacrifice critical details or incur iterative costs. To address these limitations, we propose OkraLong, a novel framework that flexibly optimizes the entire processing workflow. Unlike prior static or coarse-grained adaptive strategies, OkraLong adopts fine-grained orchestration through three synergistic components: analyzer, organizer and executor. The analyzer characterizes the task states, which guide the organizer in dynamically scheduling the workflow. The executor carries out the execution and generates the final answer. Experimental results demonstrate that OkraLong not only enhances answer accuracy by 5.7%-41.2%, but also achieves cost savings of 1.3x-4.7x.
VerifiAgent: a Unified Verification Agent in Language Model Reasoning
Authors: Jiuzhou Han; Wray Buntine; Ehsan Shareghi
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large language models demonstrate remarkable reasoning capabilities but often produce unreliable or incorrect responses. Existing verification methods are typically model-specific or domain-restricted, requiring significant computational resources and lacking scalability across diverse reasoning tasks. To address these limitations, we propose VerifiAgent, a unified verification agent that integrates two levels of verification: meta-verification, which assesses completeness and consistency in model responses, and tool-based adaptive verification, where VerifiAgent autonomously selects appropriate verification tools based on the reasoning type, including mathematical, logical, or commonsense reasoning. This adaptive approach ensures both efficiency and robustness across different verification scenarios. Experimental results show that VerifiAgent outperforms baseline verification methods (e.g., deductive verifier, backward verifier) among all reasoning tasks. Additionally, it can further enhance reasoning accuracy by leveraging feedback from verification results. VerifiAgent can also be effectively applied to inference scaling, achieving better results with fewer generated samples and costs compared to existing process reward models in the mathematical reasoning domain.
DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains
Authors: Yongkang Xiao; Sinian Zhang; Yi Dai; Huixue Zhou; Jue Hou; Jie Ding; Rui Zhang
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs (KGs) by leveraging existing triples and textual information. Recently, generative large language models (LLMs) have been increasingly employed for graph tasks. However, current approaches typically encode graph context in textual form, which fails to fully exploit the potential of LLMs for perceiving and reasoning about graph structures. To address this limitation, we propose DrKGC (Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion). DrKGC employs a flexible lightweight model training strategy to learn structural embeddings and logical rules within the KG. It then leverages a novel bottom-up graph retrieval method to extract a subgraph for each query guided by the learned rules. Finally, a graph convolutional network (GCN) adapter uses the retrieved subgraph to enhance the structural embeddings, which are then integrated into the prompt for effective LLM fine-tuning. Experimental results on two general domain benchmark datasets and two biomedical datasets demonstrate the superior performance of DrKGC. Furthermore, a realistic case study in the biomedical domain highlights its interpretability and practical utility.
Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism
Authors: Zhiwei Wang; Yunji Wang; Zhongwang Zhang; Zhangchen Zhou; Hui Jin; Tianyang Hu; Jiacheng Sun; Zhenguo Li; Yaoyu Zhang; Zhi-Qin John Xu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large language models have consistently struggled with complex reasoning tasks, such as mathematical problem-solving. Investigating the internal reasoning mechanisms of these models can help us design better model architectures and training strategies, ultimately enhancing their reasoning capability. In this study, we constructed a symbolic multi-step reasoning task to investigate the information propagation mechanisms in Transformer models when solving the task through direct answering and Chain-of-Thought (CoT) reasoning. We introduced the concept of buffer mechanism: the model stores various information in distinct buffers and selectively extracts it through the query-key matrix. We proposed a random matrix-based algorithm to enhance the model’s reasoning ability. This algorithm introduces only 132 trainable parameters, yet leads to significant performance improvements on 7 multi-step reasoning datasets, including PrOntoQA, LogicAsker, and LogicInference. These findings provide new insights into understanding the large language models.