GenAI4ELab/papercli-papers huggingface dataset:
GraDeT-HTR: A Resource-Efficient Bengali Handwritten Text Recognition System utilizing Grapheme-based Tokenizer and Decoder-only Transformer
Authors: Md. Mahmudul Hasan; Ahmed Nesar Tahsin Choudhury; Mahmudul Hasan; Md. Mosaddek Khan
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: Despite Bengali being the sixth most spoken language in the world, handwritten text recognition (HTR) systems for Bengali remain severely underdeveloped. The complexity of Bengali script—featuring conjuncts, diacritics, and highly variable handwriting styles—combined with a scarcity of annotated datasets makes this task particularly challenging. We present GraDeT-HTR, a resource-efficient Bengali handwritten text recognition system based on a Grapheme-aware Decoder-only Transformer architecture. To address the unique challenges of Bengali script, we augment the performance of a decoder-only transformer by integrating a grapheme-based tokenizer and demonstrate that it significantly improves recognition accuracy compared to conventional subword tokenizers. Our model is pretrained on large-scale synthetic data and fine-tuned on real human-annotated samples, achieving state-of-the-art performance on multiple benchmark datasets.
AutoIntent: AutoML for Text Classification
Authors: Ilya Alekseev; Roman Solomatin; Darina Rustamova; Denis Kuznetsov
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: AutoIntent is an automated machine learning tool for text classification tasks. Unlike existing solutions, AutoIntent offers end-to-end automation with embedding model selection, classifier optimization, and decision threshold tuning, all within a modular, sklearn-like interface. The framework is designed to support multi-label classification and out-of-scope detection. AutoIntent demonstrates superior performance compared to existing AutoML tools on standard intent classification datasets and enables users to balance effectiveness and resource consumption.
TruthTorchLM: A Comprehensive Library for Predicting Truthfulness in LLM Outputs
Authors: Duygu Nur Yaldiz; Yavuz Faruk Bakman; Sungmin Kang; Alperen Öziş; Hayrettin Eren Yildiz; Mitash Ashish Shah; Zhiqi Huang; Anoop Kumar; Alfy Samuel; Daben Liu; Sai Praneeth Karimireddy; Salman Avestimehr
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: Generative Large Language Models (LLMs) inevitably produce untruthful responses. Accurately predicting the truthfulness of these outputs is critical, especially in high-stakes settings. To accelerate research in this domain and make truthfulness prediction methods more accessible, we introduce TruthTorchLM an open-source, comprehensive Python library featuring over 30 truthfulness prediction methods, which we refer to as Truth Methods. Unlike existing toolkits such as Guardrails, which focus solely on document-grounded verification, or LM-Polygraph, which is limited to uncertainty-based methods, TruthTorchLM offers a broad and extensible collection of techniques. These methods span diverse trade-offs in computational cost, access level (e.g., black-box vs. white-box), grounding document requirements, and supervision type (self-supervised or supervised). TruthTorchLM is seamlessly compatible with both HuggingFace and LiteLLM, enabling support for locally hosted and API-based models. It also provides a unified interface for generation, evaluation, calibration, and long-form truthfulness prediction, along with a flexible framework for extending the library with new methods. We conduct an evaluation of representative truth methods on three datasets, TriviaQA, GSM8K, and FactScore-Bio.
The Dangers of Indirect Prompt Injection Attacks on LLM-based Autonomous Web Navigation Agents: A Demonstration
Authors: Sam Johnson; Viet Pham; Thai Le
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: This work demonstrates that LLM-based web browsing AI agents offer powerful automation capabilities but are vulnerable to Indirect Prompt Injection (IPI) attacks. We show that adversaries can embed universal adversarial triggers in webpage HTML to hijack agents that utilize the parsed-HTML accessibility tree, causing unintended or malicious actions. Using the Greedy Coordinate Gradient (GCG) algorithm and a Browser Gym agent powered by Llama-3.1, this work demonstrates high success rates across real websites in both targeted and general attacks, including login credential exfiltration and forced advertisement clicks. Our empirical results highlight critical security risks and the need for stronger defenses as LLM-driven autonomous web agents become more widely adopted. The system software is released under the MIT License at https://github.com/sej2020/manipulating-web-agents, with an accompanying publicly available demo website and video.
LaTeXMT: Machine Translation for LaTeX Documents
Authors: Calvin Hoy; Samuel Frontull; Georg Moser
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: While machine translation has taken great strides in recent years, thanks in large part to transformer language models, machine translation tools are designed primarily for plain text, and thus not equipped to deal with complex markup documents. Not even Large Language Models can reliably handle LaTeX source files, as non-standard structures are not captured by any available training data. Previous attempts to create translation engines for LaTeX either work on compiled documents, rely on document pre-processors which may lose critical semantic elements, or cannot distinguish between text and non-text content. In this paper we present LaTeXMT, a software solution for structure-preserving, source-to-source translation of LaTeX documents. All of the source code to LaTeXMT is provided under the LGPL-3.0 open-source licence and a web version is publicly available.
LangVAE and LangSpace: Building and Probing for Language Model VAEs
Authors: Danilo Carvalho; Yingji Zhang; Harriet Unsworth; Andre Freitas
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: We present LangVAE, a novel framework for modular construction of variational autoencoders (VAEs) on top of pre-trained large language models (LLMs). Such language model VAEs can encode the knowledge of their pre-trained components into more compact and semantically disentangled representations. The representations obtained in this way can be analysed with the LangVAE companion framework: LangSpace, which implements a collection of probing methods, such as vector traversal and interpolation, disentanglement measures, and cluster visualisations. LangVAE and LangSpace offer a flexible, efficient and scalable way of building and analysing textual representations, with simple integration for models available on the HuggingFace Hub. Additionally, we conducted a set of experiments with different encoder and decoder combinations, as well as annotated inputs, revealing a wide range of interactions across architectural families and sizes w.r.t.generalisation and disentanglement. Our findings demonstrate a promising framework for systematising the experimentation and understanding of textual representations.
PresentAgent: Multimodal Agent for Presentation Video Generation
Authors: Jingwei Shi; Zeyu Zhang; Biao Wu; Yanjie Liang; Meng Fang; Ling Chen; Yang Zhao
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: We present PresentAgent, a multimodal agent that transforms long-form documents into narrated presentation videos. While existing approaches are limited to generating static slides or text summaries, our method advances beyond these limitations by producing fully synchronized visual and spoken content that closely mimics human-style presentations. To achieve this integration, PresentAgent employs a modular pipeline that systematically segments the input document, plans and renders slide-style visual frames, generates contextual spoken narration with large language models and Text-to-Speech models, and seamlessly composes the final video with precise audio-visual alignment. Given the complexity of evaluating such multimodal outputs, we introduce PresentEval, a unified assessment framework powered by Vision-Language Models that comprehensively scores videos across three critical dimensions: content fidelity, visual clarity, and audience comprehension through prompt-based evaluation. Our experimental validation on a curated dataset of 30 document–presentation pairs demonstrates that PresentAgent approaches human-level quality across all evaluation metrics. These results highlight the significant potential of controllable multimodal agents in transforming static textual materials into dynamic, effective, and accessible presentation formats.
PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization
Authors: Dawei Xiang; Wenyan Xu; Kexin Chu; Tianqi Ding; Zixu Shen; Yiming Zeng; Jianchang Su; Wei Zhang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: The rapid advancement of generative AI has democratized access to powerful tools such as Text-to-Image (T2I) models. However, to generate high-quality images, users must still craft detailed prompts specifying scene, style, and context—often through multiple rounds of refinement. We propose PromptSculptor, a novel multi-agent framework that automates this iterative prompt optimization process. Our system decomposes the task into four specialized agents that work collaboratively to transform a short, vague user prompt into a comprehensive, refined prompt. By leveraging Chain-of-Thought (CoT) reasoning, our framework effectively infers hidden context and enriches scene and background details. To iteratively refine the prompt, a self-evaluation agent aligns the modified prompt with the original input, while a feedback-tuning agent incorporates user feedback for further refinement. Experimental results demonstrate that PromptSculptor significantly enhances output quality and reduces the number of iterations needed for user satisfaction. Moreover, its model-agnostic design allows seamless integration with various T2I models, paving the way for industrial applications.
EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models
Authors: Chengyu Wang; Junbing Yan; Wenrui Cai; Yuanhao Yue; Jun Huang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: In this paper, we present EasyDistill, a comprehensive toolkit designed for effective black-box and white-box knowledge distillation (KD) of large language models (LLMs). Our framework offers versatile functionalities, including data synthesis, supervised fine-tuning, ranking optimization, and reinforcement learning techniques specifically tailored for KD scenarios. The toolkit accommodates KD functionalities for both System 1 (fast, intuitive) and System 2 (slow, analytical) models. With its modular design and user-friendly interface, EasyDistill empowers researchers and industry practitioners to seamlessly experiment with and implement state-of-the-art KD strategies for LLMs. In addition, EasyDistill provides a series of robust distilled models and KD-based industrial solutions developed by us, along with the corresponding open-sourced datasets, catering to a variety of use cases. Furthermore, we describe the seamless integration of EasyDistill into Alibaba Cloud’s Platform for AI (PAI). Overall, the EasyDistill toolkit makes advanced KD techniques for LLMs more accessible and impactful within the NLP community. The toolkit, together with source codes, all model checkpoints and datasets, is released at: https://github.com/modelscope/easydistill.
AM4DSP: Argumentation Mining in Structured Decentralized Discussion Platforms for Deliberative Democracy
Authors: Sofiane Elguendouze; Lucas Anastasiou; Erwan Hain; Elena Cabrio; Anna De Liddo; Serena Villata
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: Argument(ation) mining (AM) is the automated process of identification and extraction of argumentative structures in natural language. This field has seen rapid advancements, offering powerful tools to analyze and interpret complex and large discourse in diverse domains (political debates, medical reports, etc.). In this paper we introduce an AM-boosted version of BCause, a large-scale deliberation platform.The system enables the extraction and analysis of arguments from online discussions in the context of deliberative democracy, which aims to enhance the understanding and accessibility of structured argumentation in large-scale deliberation processes.
TRACE: Training and Inference-Time Interpretability Analysis for Language Models
Authors: Nura Aljaafari; Danilo Carvalho; Andre Freitas
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: Understanding when and how linguistic knowledge emerges during language model training remains a central challenge for interpretability. Most existing tools are post hoc, rely on scalar metrics, or require nontrivial integration effort, making comprehensive interpretability analysis difficult to deploy and maintain. We introduce TRACE, a modular toolkit for training and inference-time interpretability analysis of transformer models. It enables lightweight, in-training analysis of linguistic and representational signals, including features probing, intrinsic dimensionality, Hessian curvature, and output diagnostics. It integrates with ABSynth, a controllable synthetic corpus generator that provides structured annotations for precise evaluation of linguistic feature acquisition. Experiments with autoregressive transformers demonstrate that TRACE reveals developmental phenomena such as early syntactic emergence, delayed semantic acquisition, and representational compression, signals overlooked by traditional scalar metrics such as loss or accuracy. With minimal integration effort, the tool enables layer-wise diagnostics, convergence-based early stopping, and detection of structural errors, making transformer analysis interpretable, actionable, and reproducible.
MathBuddy: A Multimodal System for Affective Math Tutoring
Authors: Debanjana Kar; Leopold Böss; Dacia Braca; Sebastian Maximilian Dennerlein; Nina Christine Hubig; Philipp Wintersberger; Yufang Hou
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: The rapid adoption of LLM-based conversational systems is already transforming the landscape of educational technology. However, the current state-of-the-art learning models do not take into account the student’s affective states. Multiple studies in educational psychology support the claim that positive or negative emotional states can impact a student’s learning capabilities. To bridge this gap, we present MathBuddy, an emotionally aware LLM-powered Math Tutor, which dynamically models the student’s emotions and maps them to relevant pedagogical strategies, making the tutor-student conversation a more empathetic one. The student’s emotions are captured from the conversational text as well as from their facial expressions. The student’s emotions are aggregate from both modalities to confidently prompt our LLM Tutor for an emotionally-aware response. We have evaluated our model using automatic evaluation metrics across eight pedagogical dimensions and user studies. We report a massive 23 point performance gain using the win rate and a 3 point gain at an overall level using DAMR scores which strongly supports our hypothesis of improving LLM-based tutor’s pedagogical abilities by modeling students’ emotions. Our dataset and code are available at: https://github.com/ITU-NLP/MathBuddy.
PledgeTracker: A System for Monitoring the Fulfilment of Pledges
Authors: Yulong Chen; Michael Sejr Schlichtkrull; Zhenyun Deng; David Corney; Nasim Asl; Joshua Salisbury; Andrew Dudfield; Andreas Vlachos
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: Political pledges reflect candidates’ policy commitments, but tracking their fulfilment requires reasoning over incremental evidence distributed across multiple, dynamically updated sources. Existing methods simplify this task into a document classification task, overlooking its dynamic temporal and multi-document nature. To address this issue, we introduce PledgeTracker, a system that reformulates pledge verification into structured event timeline construction. PledgeTracker consists of three core components: (1) a multi-step evidence retrieval module; (2) a timeline construction module and; (3) a fulfilment filtering module, allowing the capture of the evolving nature of pledge fulfilment and producing interpretable and structured timelines. We evaluate PledgeTracker in collaboration with professional fact-checkers in real-world workflows, demonstrating its effectiveness in retrieving relevant evidence and reducing human verification effort.
Interactive Training: Feedback-Driven Neural Network Optimization
Authors: Wentao Zhang; Yang Young Lu; Yuntian Deng
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: Traditional neural network training typically follows fixed, predefined optimization recipes, lacking the flexibility to dynamically respond to instabilities or emerging training issues. In this paper, we introduce Interactive Training, an open-source framework that enables real-time, feedback-driven intervention during neural network training by human experts or automated AI agents. At its core, Interactive Training uses a control server to mediate communication between users or agents and the ongoing training process, allowing users to dynamically adjust optimizer hyperparameters, training data, and model checkpoints. Through three case studies, we demonstrate that Interactive Training achieves superior training stability, reduced sensitivity to initial hyperparameters, and improved adaptability to evolving user needs, paving the way toward a future training paradigm where AI agents autonomously monitor training logs, proactively resolves instabilities, and optimizes training dynamics.
Metamo: Empowering Large Language Models with Psychological Distortion Detection for Cognition-aware Coaching
Authors: Hajime Hotta; Huu-Loi Le; Manh-Cuong Phan; Minh-Tien Nguyen
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: We demonstrate Metamo, a browser-based dialogue system that transforms an off-the-shelf large language model into an empathetic coach for everyday workplace concerns. Metamo introduces a light, single-pass wrapper that first identifies the cognitive distortion behind an emotion, then recognizes the user’s emotion, and finally produces a question-centered reply that invites reflection, all within one model call. The wrapper keeps the response time below two seconds in the API, yet enriches the feedback with cognitively grounded insight. A front-end web interface renders the detected emotion as an animated avatar and shows distortion badges in real time, whereas a safety layer blocks medical advice and redirects crisis language to human hotlines. Empirical tests on public corpora confirmed that the proposed design improved emotion‐recognition quality and response diversity without sacrificing latency. A small user study with company staff reported higher perceived empathy and usability than a latency‐matched baseline. Metamo is model-agnostic, illustrating a practical path toward cognition-aware coaching tools.
InTriage: Intelligent Telephone Triage in Pre-Hospital Emergency Care
Authors: Kai He; Qika Lin; Hao Fei; Eng Siong Chng; Dehan Hong; Marcus Eng Hock Ong; Mengling Feng
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: Pre-hospital Emergency Care (PEC) systems are critical for managing life-threatening emergencies where rapid intervention can significantly impact patient outcomes. The rising global demand for PEC services, coupled with increased emergency calls and strained emergency departments, necessitates efficient resource utilization through Telephone Triage (TT) systems. However, existing TT processes face challenges such as incomplete data collection, communication barriers, and manual errors, leading to high over-triage and under-triage rates. This study proposes InTriage, an AI-driven multilingual TT system to provide decision support for triage. InTriage enhances accuracy by transcribing emergency calls, extracting critical patient information, prompting supplementary, and providing real-time triage decisions support. We conducted an evaluation on a real-world corpus of approximately 40 hours of telephone data, achieving a word error rate of 14.57% for speech recognition and an F1 score of 73.34% for key information extraction.By improving communication efficiency and reducing triage errors, InTriage offers a scalable solution to potentially help address the growing demands on PEC systems globally.
From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with VLM-Lens
Authors: Hala Sheta; Eric Haoran Huang; Shuyu Wu; Ilia Alenabi; Jiajun Hong; Ryker Lin; Ruoxi Ning; Daniel Wei; Jialin Yang; Jiawei Zhou; Ziqiao Ma; Freda Shi
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations)

Abstract: We introduce VLM-Lens, a toolkit designed to enable systematic benchmarking, analysis, and interpretation of vision-language models (VLMs) by supporting the extraction of intermediate outputs from any layer during the forward pass of open-source VLMs. VLM-Lens provides a unified, YAML-configurable interface that abstracts away model-specific complexities and supports user-friendly operation across diverse VLMs. It currently supports 16 state-of-the-art base VLMs and their over 30 variants, and is extensible to accommodate new models without changing the core logic.The toolkit integrates easily with various interpretability and analysis methods. We demonstrate its usage with two simple analytical experiments, revealing systematic differences in the hidden representations of VLMs across layers and target concepts. VLM-Lens is released as an open-sourced project to accelerate community efforts in understanding and improving VLMs.