GenAI4ELab/papercli-papers huggingface dataset:
UniversalCEFR: Enabling Open Multilingual Research on Language Proficiency Assessment
Authors: Joseph Marvin Imperial; Abdullah Barayan; Regina Stodden; Rodrigo Wilkens; Ricardo Muñoz Sánchez; Lingyun Gao; Melissa Torgbi; Dawn Knight; Gail Forey; Reka R. Jablonkai; Ekaterina Kochmar; Robert Joshua Reynolds; Eugénio Ribeiro; Horacio Saggion; Elena Volodina; Sowmya Vajjala; Thomas François; Fernando Alva-Manchego; Harish Tayyar Madabushi
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: We introduce UniversalCEFR, a large-scale multilingual multidimensional dataset of texts annotated according to the CEFR (Common European Framework of Reference) scale in 13 languages. To enable open research in both automated readability and language proficiency assessment, UniversalCEFR comprises 505,807 CEFR-labeled texts curated from educational and learner-oriented resources, standardized into a unified data format to support consistent processing, analysis, and modeling across tasks and languages. To demonstrate its utility, we conduct benchmark experiments using three modelling paradigms: a) linguistic feature-based classification, b) fine-tuning pre-trained LLMs, and c) descriptor-based prompting of instruction-tuned LLMs. Our results further support using linguistic features and fine-tuning pretrained models in multilingual CEFR level assessment. Overall, UniversalCEFR aims to establish best practices in data distribution in language proficiency research by standardising dataset formats and promoting their accessibility to the global research community.
CROP: Contextual Region-Oriented Visual Token Pruning
Authors: Jiawei Guo; Feifei Zhai; Pu Jian; Qianrun Wei; Yu Zhou
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Current VLM-based VQA methods often process entire images, leading to excessive visual tokens that include redundant information irrelevant to the posed question. This abundance of unnecessary image details creates numerous visual tokens, drastically increasing memory and computational requirements in VLMs. To address this, we propose Contextual Region-Oriented Visual Token Pruning (CROP), a novel framework to compress visual tokens through a two-step process: Localization and Pruning. Specifically, CROP first employs an efficient model to identify the contextual region relevant to the input query. Subsequently, two distinct strategies are introduced for pruning: (1) Pre-LLM Compression (PLC), which adaptively compresses different image regions with varying ratios, and (2) Inner-LLM Pruning (ILP), a training-free method that prunes tokens within early LLM layers guided by the identified contextual region. Extensive experiments on a wide range of VQA tasks demonstrate that CROP significantly outperforms existing visual token pruning methods and achieves state-of-the-art performance.
CR4-NarrEmote: An Open Vocabulary Dataset of Narrative Emotions Derived Using Citizen Science
Authors: Andrew Piper; Robert Budac
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: We introduce “Citizen Readers for Narrative Emotions” (CR4-NarrEmote), a large-scale, open-vocabulary dataset of narrative emotions derived through a citizen science initiative. Over a four-month period, 3,738 volunteers contributed more than 200,000 emotion annotations across 43,000 passages from long-form fiction and non-fiction, spanning 150 years, twelve genres, and multiple Anglophone cultural contexts. To facilitate model training and comparability, we provide mappings to both dimensional (Valence-Arousal-Dominance) and categorical (NRC Emotion) frameworks. We evaluate annotation reliability using lexical, categorical, and semantic agreement measures, and find substantial alignment between citizen science annotations and expert-generated labels. As the first open-vocabulary resource focused on narrative emotions at scale, CR4-NarrEmote provides an important foundation for affective computing and narrative understanding.
XQuant: Achieving Ultra-Low Bit KV Cache Quantization with Cross-Layer Compression
Authors: Haoqi Yang; Yao Yao; Zuchao Li; Baoyuan Qi; Liu Guoming; Hai Zhao
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks. However, their extensive memory requirements, particularly due to KV cache growth during long-text understanding and generation, present significant challenges for deployment in resource-constrained environments. Quantization has emerged as a promising solution to reduce memory consumption while preserving historical information. We propose XQuant, a training-free and plug-and-play framework that achieves ultra-low equivalent bit-width KV cache quantization. XQuant introduces two key innovations: a computationally negligible data-free calibration method and cross-layer KV cache compression, enabling quantization to sub-1.4 bits. Extensive experiments on TruthfulQA and LongBench demonstrate that XQuant outperforms state-of-the-art methods (e.g., KIVI-2bit and AsymKV-1.5bit) by achieving lower bit-width while maintaining superior performance, establishing a better trade-off between memory efficiency and model accuracy. The source code is available at https://github.com/brinenick511/XQuant.
DINT Transformer
Authors: Yueyang Cang; Yuhang Liu; Xiaoteng Zhang; Erlu Zhao; Li Shi
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: The DIFF Transformer mitigates interference from irrelevant contexts by introducing a differential attention mechanism, thereby enhancing focus on critical tokens. However, this architecture suffers from two major limitations: first, its use of two independent attention matrices leads to numerical instability, and second, it lacks global context modeling, which is essential for identifying globally significant tokens. To address these challenges, we propose the DINT Transformer, which extends the DIFF Transformer by incorporating an integral mechanism. By computing global importance scores and integrating them into the attention matrix, the DINT Transformer not only improves overall numerical stability but also significantly enhances its ability to capture global dependencies. Experimental results demonstrate that the DINT Transformer achieves superior accuracy and robustness across various practical applications, including long-context language modeling and key information retrieval. These advancements establish the DINT Transformer as a highly effective and promising architecture.
ICR: Iterative Clarification and Rewriting for Conversational Search
Authors: Zhiyu Cao; Peifeng Li; Qiaoming Zhu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Most previous work on Conversational Query Rewriting employs an end-to-end rewriting paradigm. However, this approach is hindered by the issue of multiple fuzzy expressions within the query, which complicates the simultaneous identification and rewriting of multiple positions. To address this issue, we propose a novel framework ICR (Iterative Clarification and Rewriting), an iterative rewriting scheme that pivots on clarification questions. Within this framework, the model alternates between generating clarification questions and rewritten queries. The experimental results show that our ICR can continuously improve retrieval performance in the clarification-rewriting iterative process, thereby achieving state-of-the-art performance on two popular datasets.
Pre-training CLIP against Data Poisoning with Optimal Transport-based Matching and Alignment
Authors: Tong Zhang; Kuofeng Gao; Jiawang Bai; Leo Yu Zhang; Xin Yin; Zonghui Wang; Shouling Ji; Wenzhi Chen
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Recent studies have shown that Contrastive Language-Image Pre-training (CLIP) models are threatened by targeted data poisoning and backdoor attacks due to massive training image-caption pairs crawled from the Internet. Previous defense methods correct poisoned image-caption pairs by matching a new caption for each image. However, the matching process solely relies on the global representations of images and captions, overlooking fine-grained features of visual and textual features. It may introduce incorrect image-caption pairs and detriment the CLIP pre-training. To address their limitations, we propose an Optimal Transport-based framework to reconstruct the image-caption pairs, named OTCCLIP. We involve a new optimal transport-based distance measure between fine-grained visual and textual feature sets and re-assign new captions based on the proposed optimal transport distance. Additionally, to further reduce the negative impact of mismatched pairs, we encourage the inter- and intra-modality fine-grained alignment by employing optimal transport-based objective functions. Our experiments demonstrate that OTCCLIP can successfully decrease the attack success rates of poisoning attacks to 0% in most cases. Also, compared to previous methods, OTCCLIPsignificantly improves CLIP’s zero-shot and linear probing performance trained on poisoned datasets.
Similarity = Value? Consultation Value-Assessment and Alignment for Personalized Search
Authors: Weicong Qin; Yi Xu; Weijie Yu; Teng Shi; Chenglei Shen; Ming He; Jianping Fan; Xiao Zhang; Jun Xu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Personalized search systems in e-commerce platforms increasingly involve user interactions with AI assistants, where users consult about products, usage scenarios, and more. Leveraging consultation to personalize search services is trending. Existing methods typically rely on semantic similarity to align historical consultations with current queries due to the absence of ‘value’ labels, but we observe that semantic similarity alone often fails to capture the true value of consultation for personalization. To address this, we propose a consultation value assessment framework that evaluates historical consultations from three novel perspectives: (1) Scenario Scope Value, (2) Posterior Action Value, and (3) Time Decay Value. Based on this, we introduce VAPS, a value-aware personalized search model that selectively incorporates high-value consultations through a consultation–user action interaction module and an explicit objective that aligns consultations with user actions. Experiments on both public and commercial datasets show that VAPS consistently outperforms baselines in both retrieval and ranking tasks. Codes are available at https://github.com/E-qin/VAPS.
RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models
Authors: Zhaoyan Gong; Juan Li; Zhiqiang Liu; Lei Liang; Huajun Chen; Wen Zhang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Current temporal knowledge graph question answering (TKGQA) methods primarily focus on implicit temporal constraints, lacking the capability to handle more complex temporal queries, and struggle with limited reasoning abilities and error propagation in decomposition frameworks. We propose RTQA, a novel framework to address these challenges by enhancing reasoning over TKGs without requiring training. Following recursive thinking, RTQA recursively decomposes questions into sub-problems, solves them bottom-up using LLMs and TKG knowledge, and employs multi-path answer aggregation to improve fault tolerance. RTQA consists of three core components: the Temporal Question Decomposer, the Recursive Solver, and the Answer Aggregator. Experiments on MultiTQ and TimelineKGQA benchmarks demonstrate significant Hits@1 improvements in “Multiple” and “Complex” categories, outperforming state-of-the-art methods. Our code and data are available at https://github.com/zjukg/RTQA.
Not All Parameters Are Created Equal: Smart Isolation Boosts Fine-Tuning Performance
Authors: Yao Wang; Di Liang; Minlong Peng
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Supervised fine-tuning (SFT) is a pivotal approach to adapting large language models (LLMs) for downstream tasks; however, performance often suffers from the “seesaw phenomenon”, where indiscriminate parameter updates yield progress on certain tasks at the expense of others. To address this challenge, we propose a novel Core Parameter Isolation Fine-Tuning (CPI-FT) framework. Specifically, we first independently fine-tune the LLM on each task to identify its core parameter regions by quantifying parameter update magnitudes. Tasks with similar core regions are then grouped based on region overlap, forming clusters for joint modeling. We further introduce a parameter fusion technique: for each task, core parameters from its individually fine-tuned model are directly transplanted into a unified backbone, while non-core parameters from different tasks are smoothly integrated via Spherical Linear Interpolation (SLERP), mitigating destructive interference. A lightweight, pipelined SFT training phase using mixed-task data is subsequently employed, while freezing core regions from prior tasks to prevent catastrophic forgetting. Extensive experiments on multiple public benchmarks demonstrate that our approach significantly alleviates task interference and forgetting, consistently outperforming vanilla multi-task and multi-stage fine-tuning baselines.
AI Knows Where You Are: Exposure, Bias, and Inference in Multimodal Geolocation with KoreaGEO
Authors: Xiaonan Wang; Bo Shao; Hansaem Kim
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Recent advances in vision-language models (VLMs) have enabled accurate image-based geolocation, raising serious concerns about location privacy risks in everyday social media posts. Yet, a systematic evaluation of such risks is still lacking: existing benchmarks show coarse granularity, linguistic bias, and a neglect of multimodal privacy risks. To address these gaps, we introduce KoreaGEO, the first fine-grained, multimodal, and privacy-aware benchmark for geolocation, built on Korean street views. The benchmark covers four socio-spatial clusters and nine place types with rich contextual annotations and two captioning styles that simulate real-world privacy exposure. To evaluate mainstream VLMs, we design a three-path protocol spanning image-only, functional-caption, and high-risk-caption inputs, enabling systematic analysis of localization accuracy, spatial bias, and reasoning behavior. Results show that input modality exerts a stronger influence on localization precision and privacy exposure than model scale or architecture, with high-risk captions substantially boosting accuracy. Moreover, they highlight structural prediction biases toward core cities.
CAT: Causal Attention Tuning For Injecting Fine-grained Causal Knowledge into Large Language Models
Authors: Kairong Han; Wenshuo Zhao; Ziyu Zhao; Ye Jun Jian; Lujia Pan; Kun Kuang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large Language Models (LLMs) have achieved remarkable success across various domains. However, a fundamental question remains: Can LLMs effectively utilize causal knowledge for prediction and generation? Through empirical studies, we find that LLMs trained directly on large-scale data often capture spurious correlations rather than true causal relationships, leading to suboptimal performance, especially in out-of-distribution (OOD) scenarios. To address this challenge, we propose Causal Attention Tuning (CAT), a novel approach that injects fine-grained causal knowledge into the attention mechanism. We propose an automated pipeline that leverages human priors to automatically generate token-level causal signals and introduce the Re-Attention mechanism to guide training, helping the model focus on causal structures while mitigating noise and biases in attention scores. Experimental results on our proposed Spurious Token Game (STG) benchmark and multiple downstream tasks demonstrate that our approach effectively leverages causal knowledge for prediction and remains robust in OOD scenarios. The CAT achieves an average improvement of 5.76% on the STG dataset and 1.56% on downstream tasks. Notably, the OOD performance of the Llama-3.1-8B model on STG_M increased from 64.5% to 90.5%, and Qwen’s OOD performance on the STG_H dataset improved from 25.4% to 55.9%. Implementation details can be found at https://github.com/Kairong-Han/CAT.
Enhancing LLM Text Detection with Retrieved Contexts and Logits Distribution Consistency
Authors: Zhaoheng Huang; Yutao Zhu; Ji-Rong Wen; Zhicheng Dou
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models (LLMs) can generate fluent text, raising concerns about misuse in online comments and academic writing, leading to issues like corpus pollution and copyright infringement. Existing LLM text detection methods often rely on features from the logit distribution of the input text. However, the distinction between the LLM-generated and human-written texts may rely on only a few tokens due to the short length or insufficient information in some texts, leading to minimal and hard-to-detect differences in logit distributions. To address this, we propose HALO, an LLM-based detection method that leverages external text corpora to evaluate the difference in the logit distribution of input text under retrieved human-written and LLM-rewritten contexts. HALO also complements basic detection features and can serve as a plug-and-play module to enhance existing detection methods. Extensive experiments on five public datasets with three widely-used source LLMs show that our proposed detection method achieves state-of-the-art performance in AUROC, both in cross-domain and domain-specific scenarios.
Measuring Chain of Thought Faithfulness by Unlearning Reasoning Steps
Authors: Martin Tutek; Fateme Hashemi Chaleshtori; Ana Marasovic; Yonatan Belinkov
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: When prompted to think step-by-step, language models (LMs) produce a chain of thought (CoT), a sequence of reasoning steps that the model supposedly used to produce its prediction. Despite much work on CoT prompting, it is unclear if reasoning verbalized in a CoT is faithful to the models’ parametric beliefs. We introduce a framework for measuring parametric faithfulness of generated reasoning and propose Faithfulness by Unlearning Reasoning steps (FUR), an instance of this framework. FUR erases information contained in reasoning steps from model parameters and measures faithfulness as the resulting effect on the model’s prediction. Our experiments with four LMs and five multi-choice question answering (MCQA) datasets show that FUR is frequently able to precisely change the underlying models’ prediction for a given instance by unlearning key steps, indicating when a CoT is parametrically faithful. Further analysis shows that CoTs generated by models post-unlearning support different answers, hinting at a deeper effect of unlearning.
Stop Looking for “Important Tokens” in Multimodal Language Models: Duplication Matters More
Authors: Zichen Wen; Yifeng Gao; Shaobo Wang; Junyuan Zhang; Qintong Zhang; Weijia Li; Conghui He; Linfeng Zhang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Vision tokens in multimodal large language models often dominate huge computational overhead due to their excessive length compared to linguistic modality. Abundant recent methods aim to solve this problem with token pruning, which first defines an importance criterion for tokens and then prunes the unimportant vision tokens during inference. However, in this paper, we show that the importance is not an ideal indicator to decide whether a token should be pruned. Surprisingly, it usually results in inferior performance than random token pruning and leading to incompatibility to efficient attention computation operators. Instead, we propose DART (Duplication-Aware Reduction of Tokens), which prunes tokens based on its duplication with other tokens, leading to significant and training-free acceleration. Concretely, DART selects a small subset of pivot tokens and then retains the tokens with low duplication to the pivots, ensuring minimal information loss during token pruning. Experiments demonstrate that DART can prune 88.9% vision tokens while maintaining comparable performance, leading to a 1.99× and 2.99× speed-up in total time and prefilling stage, respectively, with good compatibility to efficient attention operators.
AgentPro: Enhancing LLM Agents with Automated Process Supervision
Authors: Yuchen Deng; Shichen Fan; Naibo Wang; Xinkui Zhao; See-Kiong Ng
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language model (LLM) agents have demonstrated significant potential for addressing complex tasks through mechanisms such as chain-of-thought reasoning and tool invocation. However, current frameworks lack explicit supervision during the reasoning process, which may lead to error propagation across reasoning chains and hinder the optimization of intermediate decision-making stages. This paper introduces a novel framework, AgentPro, which enhances LLM agent performance by automated process supervision. AgentPro employs Monte Carlo Tree Search to automatically generate step-level annotations, and develops a process reward model based on these annotations to facilitate fine-grained quality assessment of reasoning. By employing a rejection sampling strategy, the LLM agent dynamically adjusts generation probability distributions to prevent the continuation of erroneous paths, thereby improving reasoning capabilities. Extensive experiments on four datasets indicate that our method significantly outperforms existing agent-based LLM methods (e.g., achieving a 6.32% increase in accuracy on the HotpotQA dataset), underscoring its proficiency in managing intricate reasoning chains.
PORTS: Preference-Optimized Retrievers for Tool Selection with Large Language Models
Authors: Lorenzo Molfetta; Giacomo Frisoni; Nicolò Monaldini; Gianluca Moro
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Integrating external tools with Large Language Models (LLMs) has emerged as a promising paradigm for accomplishing complex tasks. Since LLMs still struggle to effectively manage large tool collections, researchers have begun exploring retrieval-based methods to pre-select the most relevant options, addressing input length and latency constraints. However, existing retrievers are often misaligned with tool-calling LLMs due to their separate training processes. This paper presents PORTS, a novel odds ratio preference optimization method for training retrievers aimed at tool selection. Using a perplexity-inspired preference signal from a frozen LLM, our approach fine-tunes a retriever to find helpful tools by optimizing the correlation between the selection probabilities and the downstream performances while jointly enforcing a contrastive semantic loss between documentation strings. The versatility of PORTS and its ability to significantly improve tool selection accuracy are demonstrated through extensive experiments on six datasets, two encoder models, and three LLMs with diverse prior knowledge. With low computational demands, our alignment process facilitates generalization to new queries and tools, proving valuable for practical applications with evolving toolsets.