GenAI4ELab/papercli-papers huggingface dataset:
LCES: Zero-shot Automated Essay Scoring via Pairwise Comparisons Using Large Language Models
Authors: Takumi Shibata; Yuichi Miyamura
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Recent advances in large language models (LLMs) have enabled zero-shot automated essay scoring (AES), providing a promising way to reduce the cost and effort of essay scoring in comparison with manual grading. However, most existing zero-shot approaches rely on LLMs to directly generate absolute scores, which often diverge from human evaluations owing to model biases and inconsistent scoring. To address these limitations, we propose LLM-based Comparative Essay Scoring (LCES), a method that formulates AES as a pairwise comparison task. Specifically, we instruct LLMs to judge which of two essays is better, collect many such comparisons, and convert them into continuous scores. Considering that the number of possible comparisons grows quadratically with the number of essays, we improve scalability by employing RankNet to efficiently transform LLM preferences into scalar scores. Experiments using AES benchmark datasets show that LCES outperforms conventional zero-shot methods in accuracy while maintaining computational efficiency. Moreover, LCES is robust across different LLM backbones, highlighting its applicability to real-world zero-shot AES.
The Arabic Generality Score: Another Dimension of Modeling Arabic Dialectness
Authors: Sanad Sha’ban; Nizar Habash
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Arabic dialects form a diverse continuum, yet NLP models often treat them as discrete categories. Recent work addresses this issue by modeling dialectness as a continuous variable, notably through the Arabic Level of Dialectness (ALDi). However, ALDi reduces complex variation to a single dimension. We propose a complementary measure: the Arabic Generality Score (AGS), which quantifies how widely a word is used across dialects. We introduce a pipeline that combines word alignment, etymology-aware edit distance, and smoothing to annotate a parallel corpus with word-level AGS. A regression model is then trained to predict AGS in context. Our approach outperforms strong baselines, including state-of-the-art dialect ID systems, on a multi-dialect benchmark. AGS offers a scalable, linguistically grounded way to model lexical generality, enriching representations of Arabic dialectness. Code is publicly available at https://github.com/CAMeL-Lab/arabic-generality-score.
Lemmatization as a Classification Task: Results from Arabic across Multiple Genres
Authors: Mostafa Saeed; Nizar Habash
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Lemmatization is crucial for NLP tasks in morphologically rich languages with ambiguous orthography like Arabic, but existing tools face challenges due to inconsistent standards and limited genre coverage. This paper introduces two novel approaches that frame lemmatization as classification into a Lemma-POS-Gloss (LPG) tagset, leveraging machine translation and semantic clustering. We also present a new Arabic lemmatization test set covering diverse genres, standardized alongside existing datasets. We evaluate character-level sequence-to-sequence models, which perform competitively and offer complementary value, but are limited to lemma prediction (not LPG) and prone to hallucinating implausible forms. Our results show that classification and clustering yield more robust, interpretable outputs, setting new benchmarks for Arabic lemmatization.
A Comprehensive Framework to Operationalize Social Stereotypes for Responsible AI Evaluations
Authors: Aida Mostafazadeh Davani; Sunipa Dev; Héctor Pérez-Urbina; Vinodkumar Prabhakaran
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Societal stereotypes are at the center of a myriad of responsible AI interventions targeted at reducing the generation and propagation of potentially harmful outcomes. While these efforts are much needed, they tend to be fragmented and often address different parts of the issue without adopting a unified or holistic approach to social stereotypes and how they impact various parts of the machine learning pipeline. As a result, current interventions fail to capitalize on the underlying mechanisms that are common across different types of stereotypes, and to anchor on particular aspects that are relevant in certain cases. In this paper, we draw on social psychological research and build on NLP data and methods, to propose a unified framework to operationalize stereotypes in generative AI evaluations. Our framework identifies key components of stereotypes that are crucial in AI evaluation, including the target group, associated attribute, relationship characteristics, perceiving group, and context. We also provide considerations and recommendations for its responsible use.
Correct-Detect: Balancing Performance and Ambiguity Through the Lens of Coreference Resolution in LLMs
Authors: Amber Shore; Russell Scheinberg; Ameeta Agrawal; So Young Lee
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large Language Models (LLMs) are intended to reflect human linguistic competencies. But humans have access to a broad and embodied context, which is key in detecting and resolving linguistic ambiguities, even in isolated text spans. A foundational case of semantic ambiguity is found in the task of coreference resolution: how is a pronoun related to an earlier person mention? This capability is implicit in nearly every downstream task, and the presence of ambiguity at this level can alter performance significantly. We show that LLMs can achieve good performance with minimal prompting in both coreference disambiguation and the detection of ambiguity in coreference, however, they cannot do both at the same time. We present the CORRECT-DETECT trade-off: though models have both capabilities and deploy them implicitly, successful performance balancing these two abilities remains elusive.
GRAID: Synthetic Data Generation with Geometric Constraints and Multi-Agentic Reflection for Harmful Content Detection
Authors: Melissa Kazemi Rad; Alberto Purpura; Himanshu Kumar; Emily Chen; Mohammad Shahed Sorower
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: We address the problem of data scarcity in harmful text classification for guardrailing applications and introduce GRAID (Geometric and Reflective AI-Driven Data Augmentation), a novel pipeline that leverages Large Language Models (LLMs) for dataset augmentation. GRAID consists of two stages: (i) generation of geometrically controlled examples using a constrained LLM, and (ii) augmentation through a multi-agentic reflective process that promotes stylistic diversity and uncovers edge cases. This combination enables both reliable coverage of the input space and nuanced exploration of harmful content. Using two benchmark data sets, we demonstrate that augmenting a harmful text classification dataset with GRAID leads to significant improvements in downstream guardrail model performance.
LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents
Authors: Taro Yano; Yoichi Ishibashi; Masafumi Oyamada
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large Language Models (LLMs) excel across diverse tasks, with post-training methods like Supervised Fine-Tuning (SFT), Preference Learning, and Model Merging enabling effective domain and task adaptation. While outcomes can vary with data orderings or component combinations, yet manual pipeline optimization is costly and labor-intensive. Existing approaches typically rely on manual design or focus narrowly on optimizing individual components, such as data ordering or merging parameters. We propose LaMDAgent, an LLM Agent-driven framework that autonomously constructs and optimizes end-to-end post-training pipelines by exploring various model improving methods, objects, and their applied orderings based on task-based feedback. LaMDAgent achieves a 9.0-point gain in tool-use accuracy without degrading instruction-following, and identifies high-performing strategies overlooked by manual design.We further analyze the impact of data and model scaling to reduce computational costs on the exploration, finding that model size scalings introduces new challenges, whereas scaling data size enables cost-effective pipeline discovery.
Finetuning LLMs for Human Behavior Prediction in Social Science Experiments
Authors: Akaash Kolluri; Shengguang Wu; Joon Sung Park; Michael S. Bernstein
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Large language models (LLMs) offer a powerful opportunity to simulate the results of social science experiments. In this work, we demonstrate that finetuning LLMs directly on individual-level responses from past experiments meaningfully improves the accuracy of such simulations. We construct SocSci210 via an automatic pipeline, a dataset comprising 2.9 million responses from 400,491 participants in 210 open-source social science experiments. Through finetuning, we achieve multiple levels of generalization. In completely unseen studies, our strongest model, Socrates-Qwen-14B, produces predictions that are 36% more aligned with distributions of human responses to diverse outcome questions under varying conditions relative to its base model (Qwen2.5-14B), outperforming GPT-4o by 15%. By finetuning on a subset of conditions in a study, generalization to new unseen conditions is particularly robust, improving by 71%. Since SocSci210 contains rich demographic information, we reduce demographic parity difference, a measure of bias, by 10.6% through finetuning. Because social sciences routinely generate rich, topic-specific datasets, our findings indicate that finetuning on such data could enable more accurate simulations for experimental hypothesis screening. We release our data, models and finetuning code.
How Private are Language Models in Abstractive Summarization?
Authors: Anthony Hughes; Nikolaos Aletras; Ning Ma
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: In sensitive domains such as medical and legal, protecting sensitive information is critical, with protective laws strictly prohibiting the disclosure of personal data. This poses challenges for sharing valuable data such as medical reports and legal cases summaries. While language models (LMs) have shown strong performance in text summarization, it is still an open question to what extent they can provide privacy-preserving summaries from non-private source documents. In this paper, we perform a comprehensive study of privacy risks in LM-based summarization across two closed- and four open-weight models of different sizes and families. We experiment with both prompting and fine-tuning strategies for privacy-preservation across a range of summarization datasets including medical and legal domains. Our quantitative and qualitative analysis, including human evaluation, shows that LMs frequently leak personally identifiable information in their summaries, in contrast to human-generated privacy-preserving summaries, which demonstrate significantly higher privacy protection levels. These findings highlight a substantial gap between current LM capabilities and expert human expert performance in privacy-sensitive summarization tasks.
Expectation Preference Optimization: Reliable Preference Estimation for Improving the Reasoning Capability of Large Language Models
Authors: Zelin Li; Dawei Song
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Pairwise preference optimization, such as Direct Preference Optimization (DPO), was originally designed to align large language models (LLMs) with human values. It has recently been used to improve the supervised fine-tuning (SFT) performance of LLMs. Using pairs of single samples, DPO estimates the probability distribution of the preferences of picking one response over another. However, in tasks that involve more complicated preferences (e.g., reasoning tasks) than those in the human value alignment task, this sampling method is likely to bring deviations from the ground-truth distribution. To solve the problem, extra efforts (e.g., external annotations or amendment of the loss function) are often required. In this paper, we hypothesise that the preferences can be better estimated through a multi-sampling process. Accordingly, we propose an Expectation Preference Optimization (EPO) algorithm that takes pairs of sample groups, instead of pairs of single samples as in DPO, for preference learning. Compared to pairwise DPO, the proposed EPO tends to produce more reliable preference estimations. Applying different preference optimization methods in a self-training paradigm, we have conducted extensive experiments on various reasoning benchmarks. The results show that our EPO approach outperforms a range of baseline approaches in terms of zero-shot accuracy on all benchmarks.
Split-Merge: Scalable and Memory-Efficient Merging of Expert LLMs
Authors: Sruthi Gorantla; Aditya Rawal; Devamanyu Hazarika; Kaixiang Lin; Mingyi Hong; Mahdi Namazifar
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: We introduce a zero-shot merging framework for large language models (LLMs) that consolidates specialized domain experts into a single model without any further training. Our core contribution lies in leveraging relative task vectors—difference representations encoding each expert’s unique traits with respect to a shared base model—to guide a principled and efficient merging process. By dissecting parameters into common dimensions (averaged across experts) and complementary dimensions (unique to each expert), we strike an optimal balance between generalization and specialization. We further devise a compression mechanism for the complementary parameters, retaining only principal components and scalar multipliers per expert, thereby minimizing overhead. A dynamic router then selects the most relevant domain at inference, ensuring that domain-specific precision is preserved. Experiments on code generation, mathematical reasoning, medical question answering, and instruction-following benchmarks confirm the versatility and effectiveness of our approach. Altogether, this framework enables truly adaptive and scalable LLMs that seamlessly integrate specialized knowledge for improved zero-shot performance.
Model Consistency as a Cheap yet Predictive Proxy for LLM Elo Scores
Authors: Ashwin Ramaswamy; Nestor Demeure; Ermal Rrapaj
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: New large language models (LLMs) are being released every day. Some perform significantly better or worse than expected given their parameter count. Therefore, there is a need for a method to independently evaluate models. The current best way to evaluate a model is to measure its Elo score by comparing it to other models in a series of contests—an expensive operation since humans are ideally required to compare LLM outputs. We observe that when an LLM is asked to judge such contests, the consistency with which it selects a model as the best in a matchup produces a metric that is 91% correlated with its own human-produced Elo score. This provides a simple proxy for Elo scores that can be computed cheaply, without any human data or prior knowledge.
Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance
Authors: Xueqing Peng; Triantafillos Papadopoulos; Efstathia Soufleri; Polydoros Giannouris; Ruoyu Xiang; Yan Wang; Lingfei Qian; Jimin Huang; Qianqian Xie; Sophia Ananiadou
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Despite Greece’s pivotal role in the global economy, large language models (LLMs) remain underexplored for Greek financial context due to the linguistic complexity of Greek and the scarcity of domain-specific datasets. While multilingual financial NLP has revealed large performance gaps across languages, no benchmarks or LLMs have been tailored for Greek financial tasks until now. To bridge this gap, we introduce Plutus-ben, the first Greek Financial Evaluation Benchmark, and Plutus-8B, the first financial LLM fine-tuned on Greek-specific financial data. Plutus-ben addresses six core tasks: numeric/textual named entity recognition, question answering, extractive summarization, abstractive summarization, and topic classification. To support these tasks, we release four new expert-annotated Greek financial datasets and incorporate two existing resources. Our comprehensive evaluation of 24 LLMs reveals persistent challenges in Greek financial NLP, driven by linguistic complexity, domain terminology, and financial reasoning gaps. Experiment results underscore the limitations of cross-lingual transfer and the need for Greek-specific financial modeling. We publicly release Plutus-ben, Plutus-8B, and all associated datasets to promote reproducible research and advance multilingual financial NLP.
TaxoAlign: Scholarly Taxonomy Generation Using Language Models
Authors: Avishek Lahiri; Yufang Hou; Debarshi Kumar Sanyal
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Taxonomies play a crucial role in helping researchers structure and navigate knowledge in a hierarchical manner. They also form an important part in the creation of comprehensive literature surveys. The existing approaches to automatic survey generation do not compare the structure of the generated surveys with those written by human experts. To address this gap, we present our own method for automated taxonomy creation that can bridge the gap between human-generated and automatically-created taxonomies. For this purpose, we create the CS-TaxoBench benchmark which consists of 460 taxonomies that have been extracted from human-written survey papers. We also include an additional test set of 80 taxonomies curated from conference survey papers. We propose TaxoAlign, a three-phase topic-based instruction-guided method for scholarly taxonomy generation. Additionally, we propose a stringent automated evaluation framework that measures the structural alignment and semantic coherence of automatically generated taxonomies in comparison to those created by human experts. We evaluate our method and various baselines on CS-TaxoBench, using both automated evaluation metrics and human evaluation studies. The results show that TaxoAlign consistently surpasses the baselines on nearly all metrics. The code and data can be found at https://github.com/AvishekLahiri/TaxoAlign.
DiNaM: Disinformation Narrative Mining with Large Language Models
Authors: Witold Sosnowski; Arkadiusz Modzelewski; Kinga Skorupska; Adam Wierzbicki
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Disinformation poses a significant threat to democratic societies, public health, and national security. To address this challenge, fact-checking experts analyze and track disinformation narratives. However, the process of manually identifying these narratives is highly time-consuming and resource-intensive. In this article, we introduce DiNaM, the first algorithm and structured framework specifically designed for mining disinformation narratives. DiNaM uses a multi-step approach to uncover disinformation narratives. It first leverages Large Language Models (LLMs) to detect false information, then applies clustering techniques to identify underlying disinformation narratives. We evaluated DiNaM’s performance using ground-truth disinformation narratives from the EUDisinfoTest dataset. The evaluation employed the Weighted Chamfer Distance (WCD), which measures the similarity between two sets of embeddings: the ground truth and the predicted disinformation narratives. DiNaM achieved a state-of-the-art WCD score of 0.73, outperforming general-purpose narrative mining methods by a notable margin of 16.4–24.7%. We are releasing DiNaM’s codebase and the dataset to the public.
VeriLocc: End-to-End Cross-Architecture Register Allocation via LLM
Authors: Lesheng Jin; Zhenyuan Ruan; Haohui Mai; Jingbo Shang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: Modern GPUs evolve rapidly, yet production compilers still rely on hand-crafted register allocation heuristics that require substantial re-tuning for each hardware generation. We introduce VeriLocc, a framework that combines large language models (LLMs) with formal compiler techniques to enable generalizable and verifiable register allocation across GPU architectures. VeriLocc fine-tunes an LLM to translate intermediate representations (MIRs) into target-specific register assignments, aided by static analysis for cross-architecture normalization and generalization and a verifier-guided regeneration loop to ensure correctness. Evaluated on matrix multiplication (GEMM) and multi-head attention (MHA), VeriLocc achieves 85–99% single-shot accuracy and near-100% pass@100. Case study shows that VeriLocc discovers more performant assignments than expert-tuned libraries, outperforming rocBLAS by over 10% in runtime.
MemeIntel: Explainable Detection of Propagandistic and Hateful Memes
Authors: Mohamed Bayan Kmainasi; Abul Hasnat; Md Arid Hasan; Ali Ezzat Shahroor; Firoj Alam
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing)

Abstract: The proliferation of multimodal content on social media presents significant challenges in understanding and moderating complex, context-dependent issues such as misinformation, hate speech, and propaganda. While efforts have been made to develop resources and propose new methods for automatic detection, limited attention has been given to label detection and the generation of explanation-based rationales for predicted labels. To address this challenge, we introduce MemeXplain, an explanation-enhanced dataset for propaganda memes in Arabic and hateful memes in English, making it the first large-scale resource for these tasks. To solve these tasks, we propose a novel multi-stage optimization approach and train Vision-Language Models (VLMs). Our results demonstrate that this approach significantly improves performance over the base model for both label detection and explanation generation, outperforming the current state-of-the-art with an absolute improvement of approximately 3% on ArMeme and 7% on Hateful Memes. For reproducibility and future research, we aim to make the MemeXplain dataset and scripts publicly available.