GenAI4ELab/papercli-papers huggingface dataset:
CAPSTONE: Composable Attribute‐Prompted Scene Translation for Zero‐Shot Vision–Language Reasoning
Authors: Md. Ismail Hossain; Shahriyar Zaman Ridoy; Moshiur Farazi; Nabeel Mohammed; Shafin Rahman
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Interpreting visual scenes with high-level reasoning is essential for many real-world applications, such as autonomous systems andcontent moderation, but training and maintaining Vision–Language Models (VLMs) remains resource-intensive and opaque. In this work, we present CAPSTONE, a lightweight, modular framework designed for industrial settings. Instead of relying on multimodal training or fine-tuning large models, CAPSTONE transforms outputs from off-the-shelf vision models into structured text prompts that can be interpreted by a frozen Large Language Model (LLM). This plug-and-play architecture enables reasoning over visual input without access to raw pixels, dramatically reducing computational cost and complexity. On the POPE dataset, our system, using a 7B LLM, outperforms several fully trained VLMs in zero-shot evaluations, while on the VSR benchmark, the 4B model achieves competitive results, together demonstrating strong generalization without retraining. CAPSTONE offers a scalable and interpretable alternative for companies looking to integrate visual reasoning capabilities without the burden of full-scale VLM pipelines.
Building Resource-Constrained Language Agents: A Korean Case Study on Chemical Toxicity Information
Authors: Hojun Cho; Donghu Kim; Soyoung Yang; Chan Lee; Hunjoo Lee; Jaegul Choo
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Language agents powered by large language models (LLMs) face significant deployment challenges in resource-constrained environments, particularly for specialized domains and less-common languages. This paper presents Tox-chat, a Korean chemical toxicity information agent devised within these limitations. We propose two key innovations: a context-efficient architecture that reduces token consumption through hierarchical section search, and a scenario-based dialogue generation methodology that effectively distills tool-using capabilities from larger models. Experimental evaluations demonstrate that our fine-tuned 8B parameter model substantially outperforms both untuned models and baseline approaches, in terms of DB faithfulness and preference. Our work offers valuable insights for researchers developing domain-specific language agents under practical constraints.
AutoDSPy: Automating Modular Prompt Design with Reinforcement Learning for Small and Large Language Models
Authors: Nafew Azim; Abrar Ur Alam; Hasan Bin Omar; Abdullah Mohammad Muntasir Adnan Jami; Jawad Ibn Ahad; Muhammad Rafsan Kabir; Md. Ismail Hossain; Fuad Rahman; Mohammad Ruhul Amin; Shafin Rahman; Nabeel Mohammed
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Large Language Models (LLMs) excel at complexreasoning tasks, yet their performance hinges on the quality of their prompts and pipeline structures. Manual promptdesign, as used in frameworks like DSPy, poses significantlimitations: it is time-intensive, demands substantial expertise,and lacks scalability, restricting the widespread use of LLMsacross diverse applications. To overcome these challenges, weintroduce AutoDSPy, the first framework to fully automateDSPy pipeline construction using reinforcement learning (RL).AutoDSPy leverages an RL-tuned policy network to dynamicallyselect optimal reasoning modules—such as Chain-of-Thought forlogical tasks or ReAct for tool integration—along with inputoutput signatures and execution strategies, entirely eliminatingthe need for manual configuration. Experimental results on theGSM8K and HotPotQA benchmarks demonstrate that AutoDSPyoutperforms traditional DSPy baselines, achieving accuracy gainsof up to 4.3% while reducing inference time, even with smallermodels like GPT-2 (127M). By integrating RL-based automation,AutoDSPy enhances both efficiency and accessibility, simplifyingthe development of structured, high-performing LLM solutionsand enabling scalability across a wide range of tasks
A Proactive Reliability Metric for Detecting Failures in Language Model Training
Authors: Maryam Fatima
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Training large language models (LLMs) at scale is fraught with instabilities that can lead to catastrophic failures, wasting millions of dollars in compute resources. Current approaches rely on reactive interventions like checkpointing, which only mitigate failures after detection. We introduce the R-Metric, a proactive reliability metric that combines signals from hardware monitoring (𝜆), training dynamics (𝜎2), and model performance (𝛥 L) to predict failures before they occur. Through extensive experiments across 720 simulated runs and real-world validation on diverse hardware (NVIDIA T4/L4 GPUs) and model architectures (Llama 3.2-1B, GPT-2 Large, Qwen3-0.6B, Liquid AI LFM2-700M), we demonstrate that the R-Metric achieves 0.973 F1-Score in simulation and perfect 1.00 F1-Score in real-world deployment with an average lead time of 255 steps (12.8 minutes for small models, scaling to 2-8 minutes at production training speeds), enabling preemptive intervention. Importantly, our optimized weights (𝜆=0.10, 𝜎2=0.45, 𝛥 L=0.70) transfer across architectures with less than 3% performance degradation, eliminating expensive retuning. The metric’s lightweight computational overhead (1.8% training time increase) makes it immediately deployable for resource-constrained organizations—academic labs, startups, and open-source communities—democratizing access to enterprise-grade reliability monitoring.
Efficient Inference for Large Language Models –Algorithm, Model, and System
Authors: Xuefei Ning; Guohao Dai; Haoli Bai; Lu Hou; Yu Wang; Qun Liu
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts)

Abstract: The inference of LLMs incurs high computational costs, memory access overhead, and memory usage, leading to inefficiencies in terms of latency, throughput, power consumption, and storage. To this end, this tutorial focuses on the increasingly important topic of Efficient Inference for LLMs and aims to provide a systematic understanding of key facts and methodologies from a designer’s perspective. We start by introducing the basic concepts of modern LLMs, software and hardware. Following this, we define the efficiency optimization problem. To equip the audience with a designer’s mindset, we briefly explain how to diagnose efficiency bottlenecks for a given workload on specific hardware. After introducing the basics, we will introduce our full-stack taxonomy of efficient inference methods for LLMs. We will walk through each category of methodology, using one to three representative methods as examples for each leaf subcategory, elaborating on the design logic behind each method and which inefficiency factors they primarily address. Finally, we will wrap up with a takeaway summary, and future research directions. The tutorial website is https://haolibai.github.io/emnlp-2025-tutorial-efficiency/.
Advancing Language Models through Instruction Tuning: Recent Progress and Challenges
Authors: Zhihan Zhang; Renze Lou; Fangkai Jiao; Wenpeng Yin; Meng Jiang
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts)

Abstract: The capability of following instructions is a key dimension for AI systems. Therefore, in NLP, instruction tuning – the process of training language models to follow natural language instructions – has become a fundamental component of the model development pipeline. This tutorial addresses three critical questions within the field: (1) What are the current focal points in instruction tuning research? (2) What are the best practices in training an instruction-following model? (3) What new challenges have emerged? To answer these questions, the tutorial presents a systematic overview of recent advances in instruction tuning. It covers different stages in model training: supervised fine-tuning, preference optimization, and reinforcement learning. It introduces scalable strategies for building high-quality instruction data, explores approaches for training autonomous AI agents that handle complex real-world tasks, and discusses common criteria for evaluating instruction-following models. The audience will gain a comprehensive understanding of cutting-edge trends in instruction tuning and insights into promising directions for future research.
Spoken Conversational Agents with Large Language Models
Authors: Huck Yang; Andreas Stolcke; Larry P. Heck
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts)

Abstract: Spoken conversational agents are converging toward voice-native LLMs. This tutorial distills the path from cascaded ASR/NLU to end-to-end, retrieval-and vision-grounded systems. We frame adaptation of text LLMs to audio, cross-modal alignment, and joint speech–text training; review datasets, metrics, and robustness across accents; and compare design choices (cascaded vs. E2E, post-ASR correction, streaming). We link industrial assistants to current open-domain and task-oriented agents, highlight reproducible baselines, and outline open problems in privacy, safety, and evaluation. Attendees leave with practical recipes and a clear systems-level roadmap.
NLP+Code: Code Intelligence in Language Models
Authors: Terry Yue Zhuo; Qian Liu; Zijian Wang; Wasi Uddin Ahmad; Binyuan Hui; Loubna Ben Allal
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts)

Abstract: Language models (LMs) like GPT and Claude have shown impressive abilities in a range of natural language processing (NLP) tasks. Among these tasks, code understanding and generation have quickly become one of the most popular applications of LMs, given its nature of executable logic forms. However, there is a practical understanding of how programming knowledge can be combined with natural language to automate software development. Moreover, recent studies also empirically demonstrate that code can be a better form for complex reasoning and agentic task automation, but they do not indicate their significance. In this tutorial, we deem such superior capabilities brought by code modeling as Code Intelligence, and aim to provide a coherent overview of recent advances in this topic. We will start by first providing preliminaries of training foundation models on code and their common practices. We will then focus on downstream tasks in the domain of code and their evaluations. Then, we will cover how code can contribute to advancements in general tasks, and the opportunities of future research on Code Intelligence.
Data and Model Centric Approaches for Expansion of Large Language Models to New languages
Authors: Anoop Kunchukuttan; Raj Dabre; Rudra Murthy; Mohammed Safi Ur Rahman Khan; Thanmay Jayakumar
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts)

Abstract: Despite the increasing pace of Large Language Model (LLM) research, a vast majority of existing LLMs mainly support English alongside a handful of high resource languages, leaving a major gap for most low-resource languages. In this tutorial, we focus on approaches to expand the language coverage of LLMs. This provides an efficient and viable path to bring LLM technologies to low-resource languages, instead of training from scratch. We look at approaches at various stages of the LLM training pipeline, like tokenizer training, pre-training, instruction tuning, alignment, evaluation, etc., where adaptations are made to support new languages. We look at data-oriented approaches as well as model-oriented approaches. We hope that our tutorial enables researchers and practitioners to work on incorporating additional languages and tasks into existing LLMs to enhance inclusivity and coverage.
Neuro-Symbolic Natural Language Processing
Authors: André Freitas; Marco Valentino; Danilo Silva de Carvalho
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts)

Abstract: Despite the performance leaps delivered by Large Language Models (LLMs), NLP systems based only on deep learning architectures still have limiting capabilities in terms of delivering safe and controlled reasoning, interpretability, and adaptability within complex and specialised domains, restricting their use in areas where reliability and trustworthiness are crucial. Neur-symbolic NLP methods seek to overcome these limitations by integrating the flexibility of contemporary language models with the control/interpretability of symbolic methods. This hybrid approach brings the promise to both enhance inference capabilities and to deepen the theoretical understanding of LLMs. This tutorial aims to bridge the gap between the practical performance of LLMs and the principled modelling of language and inference of formal methods. We provide an overview of formal foundations in linguistics and reasoning, followed by contemporary architectural mechanisms to interpret, control, and extend NLP models. Balancing theoretical and practical activities, the tutorial is suitable for PhD students, experienced researchers, and industry practitioners.
Continual Learning of Large Language Models
Authors: Tongtong Wu; Trang Vu; Linhao Luo; Gholamreza Haffari
Venue: EMNLP 2025 (Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts)

Abstract: As large language models (LLMs) continue to expand in size and utility, keeping them current with evolving knowledge and shifting user preferences becomes an increasingly urgent yet challenging task. This tutorial offers a comprehensive exploration of continual learning (CL) in the context of LLMs, presenting a structured framework that spans continual pre-training, instruction tuning, and alignment. Grounded in recent survey work and empirical studies, we discuss emerging trends, key methods, and practical insights from both academic research and industry deployments. In addition, we highlight the new frontier of lifelong LLM agents, i.e., systems capable of autonomous, self-reflective, and tool-augmented adaptation. Participants will gain a deep understanding of the computational, algorithmic, and ethical challenges inherent to CL in LLMs, and learn about strategies to mitigate forgetting, manage data and evaluation pipelines, and design systems that can adapt responsibly and reliably over time. This tutorial will benefit researchers and practitioners interested in advancing the long-term effectiveness, adaptability, and safety of foundation models.
Automating Alternative Generation in Decision-Making
Authors: Yevhen Kostiuk; Clara Seyfried; Chris Reed
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: In decision making, generating alternative solutions is crucial for solving a problem. However, cognitive biases can impede this process by constraining individual decision makers’ creativity. To address this issue, we introduce a new task for automatically generating alternatives, inspired by the process of human “brainstorming”. We define alternative options based on atomic action components and present a dataset of 106 annotated Reddit r/Advice posts containing unique alternative options extracted from users’ replies. We also introduce new metrics to assess the quality of generated components, including distinctiveness, creativity, upvote-weighted, crowd intersection, and final commit intersection scores. As a baseline, we evaluated the large language models (LLMs) LLaMa3:8b, LLaMa3.1:8b, and Gemma 2:9b on the alternative component generation task. On the one hand, models demonstrated high creativity (ability to generate options beyond what Reddit users suggested) and performed well at proposing distinct alternatives. A subset of generated components was manually evaluated and found overall useful. This indicates that LLMs might be used to extend lists of alternative options, helping decision makers consider a problem from different perspectives. On the other hand, LLMs’ outputs often failed to align with human suggestions, implying that they still tend to miss important components.
Bias Analysis and Mitigation through Protected Attribute Detection and Regard Classification
Authors: Takuma Udagawa; Yang Zhao; Hiroshi Kanayama; Bishwaranjan Bhattacharjee
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Large language models (LLMs) acquire general linguistic knowledge from massive-scale pretraining. However, pretraining data mainly comprised of web-crawled texts contain undesirable social biases which can be perpetuated or even amplified by LLMs. In this study, we propose an efficient yet effective annotation pipeline to investigate social biases in the pretraining corpora. Our pipeline consists of protected attribute detection to identify diverse demographics, followed by regard classification to analyze the language polarity towards each attribute. Through our experiments, we demonstrate the effect of our bias analysis and mitigation measures, focusing on Common Crawl as the most representative pretraining corpus.
Large Language Models Might Not Care What You Are Saying: Prompt Format Beats Descriptions
Authors: Chenming Tang; Zhixiang Wang; Hao Sun; Yunfang Wu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: With the help of in-context learning (ICL), large language models (LLMs) have achieved impressive performance across various tasks. However, the function of descriptive instructions during ICL remains under-explored. In this work, we propose an ensemble prompt framework to describe the selection criteria of multiple in-context examples, and preliminary experiments on machine translation (MT) across six translation directions confirm that this framework boosts ICL performance. But to our surprise, LLMs might not care what the descriptions actually say, and the performance gain is primarily caused by the ensemble format, since it could lead to improvement even with random descriptive nouns. We further apply this new ensemble framework on a range of commonsense, math, logical reasoning and hallucination tasks with three LLMs and achieve promising results, suggesting again that designing a proper prompt format would be much more effective and efficient than paying effort into specific descriptions.
Boundary Matters: Leveraging Structured Text Plots for Long Text Outline Generation
Authors: Yuanchi Ma; Jiamou Liu; Hui He; Libo Zhang; Haoyuan Li; Zhendong Niu
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Outline generation aims to uncover the internal content structure of a document by identifying potential chapter connections and generating corresponding summaries. A robust outline generation model strives for coherence between and within plots. However, existing methods perform well on short- and medium-length texts and struggle with generating readable outlines for very long texts (e.g., fictional literary works). The primary challenge lies in their inability to accurately segment plots within long texts. To address this issue, we propose a novel unsupervised guidance framework, LeStrTP, to guide large language model (LLM) outline generation. This framework ensures that each structured plot encapsulates complete causality by accurately identifying plot boundaries. Specifically, the LeStrTP framework constructs chapter-level graph from long texts and learns their embeddings. Subsequently, through Markov chain modeling chapter dependence, a unique search operator is designed to achieve plot segmentation. To facilitate research on this task, we introduce a new annotated benchmark dataset, NovOutlineSet. Experimental results demonstrate that structured plots not only enhance the coherence and integrity of generated outlines but also significantly improve their quality.
Can Large Language Models Personalize Dialogues to Generational Styles?
Authors: Pier Felice Balestrucci; Ondrej Dusek; Luca Anselma; Alessandro Mazzei
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: We investigate how large language models (LLMs) can produce personalized dialogue responses, specifically focusing on whether they reflect linguistic styles pertaining to different generations: Baby Boomers, Generation X, Generation Y, and Generation Z. We create P-MultiWoZ, a personalized, generation-specific version of MultiWOZ 2.2, by prompting LLMs, and validate its alignment with the original dataset through automatic and human evaluations. To validate the appropriateness of generational linguistic traits, we introduce GeMoSC, a corpus of generation-annotated movie dialogues. Linguistic analysis and perplexity test suggest that P-MultiWoZ reflects patterns consistent with GeMoSC. Finally, a human evaluation reveals that annotators were able to mostly correctly identify the generation behind P-MultiWoZ dialogues, based only on a single query-reply pair.
Toward Optimal LLM Alignments Using Two-Player Games
Authors: Rui Zheng; Hongyi Guo; Zhihan Liu; Xiaoying Zhang; Yuanshun Yao; Xiaojun Xu; Zhaoran Wang; Zhiheng Xi; Tao Gui; Qi Zhang; Xuanjing Huang; Yang Liu; Hang Li
Venue: EMNLP 2025 (Findings of the Association for Computational Linguistics: EMNLP 2025)

Abstract: Alignment of large language models (LLM) is a process that ensures the model’s responses to user prompts align with human intentions and social values. This optimization typically relies on pre-collected prompts. The collection of these prompts often either requires careful human interventions or proves to be difficult to have a good coverage over all scenarios an LLM can improve over . To address this issue, we propose an alignment method based on a two-agent game, consisting of an adversarial agent and a defensive agent. The adversarial agent’s task is to generate prompts that expose the deficiencies of the defensive agent. At the same time, the defensive agent improves its performance on the prompts generated by the adversary based on feedback from the reward model. This iterative process is repeated to enhance the model’s performance. We theoretically demonstrate that, under mild assumptions, this iterative alignment process converges to a Nash equilibrium by both agents. Learning in this competitive environment results in policies with better generalization capabilities. We demonstrate the advantage of our framework using extensive experiments.