GenAI4ELab/papercli-papers huggingface dataset:
The Role of Prosody in Spoken Question Answering
Authors: Jie Chi; Maureen de Seyssel; Natalie Schluter
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Spoken language understanding research to date has generally carried a heavy text perspective. Most datasets are derived from text, which is then subsequently synthesized into speech, and most models typically rely on automatic transcriptions of speech. This is to the detriment of prosody–additional information carried by the speech signal beyond the phonetics of the words themselves and difficult to recover from text alone. In this work, we investigate the role of prosody in Spoken Question Answering. By isolating prosodic and lexical information on the SLUE-SQA-5 dataset, which consists of natural speech, we demonstrate that models trained on prosodic information alone can perform reasonably well by utilizing prosodic cues. However, we find that when lexical information is available, models tend to predominantly rely on it. Our findings suggest that while prosodic cues provide valuable supplementary information, more effective integration methods are required to ensure prosody contributes more significantly alongside lexical features.
Target-Augmented Shared Fusion-based Multimodal Sarcasm Explanation Generation
Authors: Palaash Goel; Dushyant Singh Chauhan; Md Shad Akhtar
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Sarcasm is a linguistic phenomenon that intends to ridicule a target (e.g., entity, event, or person) in an inherent way. Multimodal Sarcasm Explanation (MuSE) aims at revealing the intended irony in a sarcastic post using a natural language explanation. Though important, existing systems overlooked the significance of the target of sarcasm in generating explanations. In this paper, we propose a Target-aUgmented shaRed fusion-Based sarcasm explanatiOn model, aka. TURBO. We design a novel shared-fusion mechanism to leverage the inter-modality relationships between an image and its caption. TURBO assumes the target of the sarcasm and guides the multimodal shared fusion mechanism in learning intricacies of the intended irony for explanations. We evaluate our proposed TURBO model on the MORE+ dataset. Comparison against multiple baselines and state-of-the-art models signifies the performance improvement of TURBO by an average margin of +3.3%. Moreover, we explore LLMs in zero and one-shot settings for our task and observe that LLM-generated explanation, though remarkable, often fails to capture the critical nuances of the sarcasm. Furthermore, we supplement our study with extensive human mevaluation on TURBO’s generated explanations and find them out to be comparatively better than other systems.
Seeds of Discourse: A Multilingual Corpus of Direct Quotations from African Media on Agricultural Biotechnologies
Authors: Patricia Chiril; Trevor Spreadbury; Joeva Sean Rock; Brian Dowd-Uribe; David Uminsky
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Direct quotations play a crucial role in journalism by substantiating claims and enhancing persuasive communication. This makes news articles a rich resource for opinion mining, providing valuable insights into the topics they cover. This paper presents the first multilingual corpora (English and French) featuring both manually annotated (1,657) and automatically extracted (102,483) direct quotations related to agricultural biotechnologies from a curated list of Africa-based news sources. In addition, we provide 665 instances annotated for Aspect-Based Sentiment Analysis, enabling a fine-grained examination of sentiment toward key aspects of agricultural biotechnologies. These corpora are freely available to the research community for future work on media discourse surrounding agricultural biotechnologies.
Position Really Matters: Towards a Holistic Approach for Prompt Tuning
Authors: Xianjun Yang; Wei Cheng; Xujiang Zhao; Wenchao Yu; Linda Ruth Petzold; Haifeng Chen
Venue: NAACL 2025 (Findings of the Association for Computational Linguistics: NAACL 2025)

Abstract: Prompt tuning is highly effective in efficiently extracting knowledge from foundation models, encompassing both language, vision, and vision-language models. However, the efficacy of employing fixed soft prompts with a predetermined position for concatenation with inputs for all instances, irrespective of their inherent disparities, remains uncertain. Variables such as the position, length, and representations of prompts across diverse instances and tasks can substantially influence the performance of prompt tuning. We first provide a theoretical analysis, revealing that optimizing the position of the prompt to encompass the input can capture additional semantic information that traditional prefix or postfix prompt tuning methods fail to capture. Then, we present a holistic parametric prompt tuning strategy that dynamically determines different factors of prompts based on specific tasks or instances. Experimental results underscore the significant performance improvement achieved by dynamic prompt tuning across a wide range of tasks, including NLP, vision recognition, and vision-language tasks. Furthermore, we establish the universal applicability of our approach under full-data, few-shot, and multitask settings.
Variable Extraction for Model Recovery in Scientific Literature
Authors: Chunwei Liu; Enrique Noriega-Atala; Adarsh Pyarelal; Clayton T Morrison; Mike Cafarella
Venue: NAACL 2025 (Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities)

Abstract: Due to the increasing productivity in the scientific community, it is difficult to keep up with the literature without the assistance of AI methods. This paper evaluates various methods for extracting mathematical model variables from epidemiological studies, such as ‘infection rate (𝛼),” ‘recovery rate (𝛾),” and ‘mortality rate (𝜇).” Variable extraction appears to be a basic task, but plays a pivotal role in recovering models from scientific literature. Once extracted, we can use these variables for automatic mathematical modeling, simulation, and replication of published results. We also introduce a benchmark dataset comprising manually-annotated variable descriptions and variable values extracted from scientific papers. Our analysis shows that LLM-based solutions perform the best. Despite the incremental benefits of combining rule-based extraction outputs with LLMs, the leap in performance attributed to the transfer-learning and instruction-tuning capabilities of LLMs themselves is far more significant. This investigation demonstrates the potential of LLMs to enhance automatic comprehension of scientific artifacts and for automatic model recovery and simulation.
How Well Do Large Language Models Extract Keywords? A Systematic Evaluation on Scientific Corpora
Authors: Nacef Ben Mansour; Hamed Rahimi; Motasem Alrahabi
Venue: NAACL 2025 (Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities)

Abstract: Automatic keyword extraction from scientific articles is pivotal for organizing scholarly archives, powering semantic search engines, and mapping interdisciplinary research trends. However, existing methods—including statistical and graph-based approaches—struggle to handle domain-specific challenges such as technical terminology, cross-disciplinary ambiguity, and dynamic scientific jargon. This paper presents an empirical comparison of traditional keyword extraction methods (e.g. TextRank and YAKE) with approaches based on Large Language Model. We introduce a novel evaluation framework that combines fuzzy semantic matching based on Levenshtein Distance with exact-match metrics (F1, precision, recall) to address inconsistencies in keyword normalization across scientific corpora. Through an extensive ablation study across nine different LLMs, we analyze their performance and associated costs. Our findings reveal that LLM-based methods consistently achieve superior precision and relevance compared to traditional approaches. This performance advantage suggests significant potential for improving scientific search systems and information retrieval in academic contexts.
A Human-LLM Note-Taking System with Case-Based Reasoning as Framework for Scientific Discovery
Authors: Douglas B Craig
Venue: NAACL 2025 (Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities)

Abstract: Scientific discovery is an iterative process that requires transparent reasoning, empirical validation, and structured problem-solving. This work presents a novel human-in-the-loop AI system that leverages case-based reasoning to facilitate structured scientific inquiry. The system is designed to be note-centric, using the Obsidian note-taking application as the primary interface where all components, including user inputs, system cases, and tool specifications, are represented as plain-text notes. This approach ensures that every step of the research process is visible, editable, and revisable by both the user and the AI. The system dynamically retrieves relevant cases from past experience, refines hypotheses, and structures research workflows in a transparent and iterative manner. The methodology is demonstrated through a case study investigating the role of TLR4 in sepsis, illustrating how the system supports problem framing, literature review, hypothesis formulation, and empirical validation. The results highlight the potential of AI-assisted scientific workflows to enhance research efficiency while preserving human oversight and interpretability.
Towards AI-assisted Academic Writing
Authors: Daniel J. Liebling; Malcolm Kane; Madeleine Grunde-McLaughlin; Ian Lang; Subhashini Venugopalan; Michael Brenner
Venue: NAACL 2025 (Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities)

Abstract: We present components of an AI-assisted academic writing system including citation recommendation and introduction writing. The system recommends citations by considering the user’s current document context to provide relevant suggestions. It generates introductions in a structured fashion, situating the contributions of the research relative to prior work. We demonstrate the effectiveness of the components through quantitative evaluations. Finally, the paper presents qualitative research exploring how researchers incorporate citations into their writing workflows. Our findings indicate that there is demand for precise AI-assisted writing systems and simple, effective methods for meeting those needs.
Evaluating and Enhancing Large Language Models for Novelty Assessment in Scholarly Publications
Authors: Ethan Lin; Zhiyuan Peng; Yi Fang
Venue: NAACL 2025 (Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities)

Abstract: Recent studies have evaluated creativity, where novelty is an important aspect, of large language models (LLMs) primarily from a semantic perspective, using benchmarks from cognitive science. However, assessing the novelty in scholarly publications, a critical facet of evaluating LLMs as scientific discovery assistants, remains underexplored, despite its potential to accelerate research cycles and prioritize high-impact contributions in scientific workflows. We introduce SchNovel, a benchmark to evaluate LLMs’ ability to assess novelty in scholarly papers, a task central to streamlining discovery pipeline. SchNovel consists of 15000 pairs of papers across six fields sampled from the arXiv dataset with publication dates spanning 2 to 10 years apart. In each pair, the more recently published paper is assumed to be more novel. Additionally, we propose RAG-Novelty, a retrieval-augmented method that mirrors human peer review by grounding novelty assessment in retrieved context. Extensive experiments provide insights into the capabilities of different LLMs to assess novelty and demonstrate that RAG-Novelty outperforms recent baseline models highlight LLMs’ promise as tools for automating novelty detection in scientific workflows.
LLM-Assisted Translation of Legacy FORTRAN Codes to C++: A Cross-Platform Study
Authors: Nishath Rajiv Ranasinghe; Shawn M. Jones; Michal Kucer; Ayan Biswas; Daniel O’Malley; Alexander Most; Selma Liliane Wanna; Ajay Sreekumar
Venue: NAACL 2025 (Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities)

Abstract: Large Language Models (LLMs) are increasinglybeing leveraged for generating andtranslating scientific computer codes by bothdomain-experts and non-domain experts. Fortranhas served as one of the go to programminglanguages in legacy high-performance computing(HPC) for scientific discoveries. Despitegrowing adoption, LLM-based code translationof legacy code-bases has not been thoroughlyassessed or quantified for its usability.Here, we studied the applicability of LLMbasedtranslation of Fortran to C++ as a step towardsbuilding an agentic-workflow using openweightLLMs on two different computationalplatforms. We statistically quantified the compilationaccuracy of the translated C++ codes,measured the similarity of the LLM translatedcode to the human translated C++ code, andstatistically quantified the output similarity ofthe Fortran to C++ translation.
FlavorDiffusion: Modeling Food-Chemical Interactions with Diffusion
Authors: Junpyo Seo; Dongwan Kim; Jaewook Jeong; Inkyu Park; Junho Min
Venue: NAACL 2025 (Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities)

Abstract: The study of food pairing has evolved beyond subjective expertise with the advent of machine learning. This paper presents FlavorDiffusion, a novel framework leveraging diffusion models to predict food-chemical interactions and ingredient pairings without relying on chromatography. By integrating graph-based embeddings, diffusion processes, and chemical property encoding, FlavorDiffusion addresses data imbalances and enhances clustering quality. Using a heterogeneous graph derived from datasets like Recipe1M and FlavorDB, our model demonstrates superior performance in reconstructing ingredient-ingredient relationships. The addition of a Chemical Structure Prediction (CSP) layer further refines the embedding space, achieving state-of-the-art NMI scores and enabling meaningful discovery of novel ingredient combinations. The proposed framework represents a significant step forward in computational gastronomy, offering scalable, interpretable, and chemically informed solutions for food science.
Automatic Text Segmentation of Ancient and Historic Hebrew
Authors: Elisha Rosensweig; Benjamin Resnick; Hillel Gershuni; Joshua Guedalia; Nachum Dershowitz; Avi Shmidman
Venue: NAACL 2025 (Proceedings of the Second Workshop on Ancient Language Processing)

Abstract: Ancient texts often lack punctuation marks, making it challenging to determine sentence boundaries and clause boundaries. Texts may contain sequences of hundreds of words without any period or indication of a full stop. Determining such boundaries is a crucial step in various NLP pipelines, especially regarding language models such as BERT that have context window constraints and regarding machine translation models which may become far less accurate when fed too much text at a time. In this paper, we consider several novel approaches to automatic segmentation of unpunctuated ancient texts into grammatically complete or semi-complete units. Our work here focuses on ancient and historical Hebrew and Aramaic texts, but the tools developed can be applied equally to similar languages. We explore several approaches to addressing this task: masked language models (MLM) to predict the next token; fewshot completions via an open-source foundational LLM; and the “Segment-Any-Text” (SaT) tool by Frohmann et al. (Frohmann et al., 2024). These are then compared to instructbased flows using commercial (closed, managed) LLMs, to be used as a benchmark. To evaluate these approaches, we also introduce a new ground truth (GT) dataset of manually segmented texts. We explore the performance of our different approaches on this dataset. We release both our segmentation tools and the dataset to support further research into computational processing and analysis of ancient texts, which can be found here ‘https://github.com/ERC-Midrash/rabbinic_chunker’.
Integrating Semantic and Statistical Features for Authorial Clustering of Qumran Scrolls
Authors: Yonatan Lourie; Jonathan Ben-Dov; Roded Sharan
Venue: NAACL 2025 (Proceedings of the Second Workshop on Ancient Language Processing)

Abstract: We present a novel framework for authorial classification and clustering of the Qumran Dead Sea Scrolls (DSS). Our approach com-bines modern Hebrew BERT embeddings with traditional natural language processing features in a graph neural network (GNN) architecture. Our results outperform baseline methods on both the Dead Sea Scrolls and a validation dataset of the Hebrew Bible. In particular, we leverage our model to provide significant insights into long-standing debates, including the classification of sectarian and non-sectarian texts and the division of the Hodayot collection of hymns.
Assignment of account type to proto-cuneiform economic texts with Multi-Class Support Vector Machines
Authors: Piotr Zadworny; Shai Gordin
Venue: NAACL 2025 (Proceedings of the Second Workshop on Ancient Language Processing)

Abstract: We investigate the use of machine learning for classifying proto-cuneiform economic texts (3,500-3,000 BCE), leveraging Multi-Class Support Vector Machines (MSVM) to assign text type based on content. Proto-cuneiform presents unique challenges, as it does not en-code spoken language, yet is transcribed into linear formats that obscure original structural elements. We address this by reformatting tran-scriptions, experimenting with different tok-enization strategies, and optimizing feature ex-traction. Our workflow achieves high label-ing reliability and enables significant metadata enrichment. In addition to improving digital corpus organization, our approach opens the chance to identify economic institutions in an-cient Mesopotamian archives, providing a new tool for Assyriological research.
Using Cross-Linguistic Data Formats to Enhance the Annotation of Ancient Chinese Documents Written on Bamboo Slips
Authors: Michele Pulini; Johann-Mattis List
Venue: NAACL 2025 (Proceedings of the Second Workshop on Ancient Language Processing)

Abstract: Ancient Chinese documents written on bam-boo slips more than 2000 years ago offer a rich resource for research in linguistics, paleogra-phy, and historiography. However, since most documents are only available in the form of scans, additional steps of analysis are needed to turn them into interactive digital editions, amenable both for manual and computational exploration. Here, we present a first attempt to establish a workflow for the annotation of an-cient bamboo slips. Based on a recently redis-covered dialogue on warfare, we illustrate how a digital edition amenable for manual and com-putational exploration can be created by inte-grating standards originally designed for cross-linguistic data collections.
Accessible Sanskrit: A Cascading System for Text Analysis and Dictionary Access
Authors: Giacomo De Luca
Venue: NAACL 2025 (Proceedings of the Second Workshop on Ancient Language Processing)

Abstract: Sanskrit text processing presents unique com-putational challenges due to its complex mor-phology, frequent compound formation, and the phenomenon of Sandhi. While several ap-proaches to Sanskrit word segmentation ex-ist, the field lacks integrated tools that make texts accessible while maintaining high accu-racy. We present a hybrid approach combining rule-based and statistical methods that achieves reliable Sanskrit text analysis through a cascade mechanism in which a deterministic matching using inflection tables is used for simple cases and statistical approaches are used for the more complex ones. The goal of the system is to provide automatic text annotation and inflected dictionary search, returning for each word root forms, comprehensive grammatical analysis, inflection tables, and dictionary entries from multiple sources. The system is evaluated on 300 randomly selected compounds from the GRETIL corpus across different length cate-gories and maintains 90% accuracy regardless of compound length, with 91% accuracy on the 40+ characters long compounds. The approach is also tested on the complete text of the Yoga Sutra, demonstrating 96% accuracy in the prac-tical use case. This approach is implemented both as an open-source Python library and a web application, making Sanskrit text analysis accessible to scholars and interested readers while retaining state-of-the-art accuracy.
Towards an Integrated Methodology of Dating Biblical Texts: The Case of the Book of Jeremiah
Authors: Martijn Naaijer; Aren Wilson-Wright
Venue: NAACL 2025 (Proceedings of the Second Workshop on Ancient Language Processing)

Abstract: In this paper we describe our research project on dating the language of the Book of Jeremiah using a combination of traditional biblical scholarship and machine learning. Jeremiah is a book with a long history of composing and editing, and the historical background of many of the sections in the book are unclear. Moreover, redaction criticism and historical linguistics are mostly separate fields within the discipline of Biblical Studies. With our approach we want to integrate these areas of research and make new strides in uncovering the compositional history of Book of Jeremiah.
The Development of Hebrew in Antiquity – A Computational Linguistic Study
Authors: Hallel Baitner; Dimid Duchovny; Lee-Ad Gottlieb; Amir Yorav; Nachum Dershowitz; Eshbal Ratzon
Venue: NAACL 2025 (Proceedings of the Second Workshop on Ancient Language Processing)

Abstract: The linguistic nature of Qumran Hebrew (QH) remains a central debate in the study of the Dead Sea Scrolls (DSS). Although some schol-ars view QH as an artificial imitation of Biblical Hebrew (BH), others argue that it represents a spoken dialect of ancient Judea. The present study employs computational lin-guistic techniques, clustering, classification, and machine learning, to analyze the relation-ship of QH with Biblical and Mishnaic He-brew. Preliminary findings confirm existing scholarly conclusions regarding the linguistic affinity of certain texts. This demonstrates that our methodology has a fundamental capacity to identify linguistic relationships. They also contribute new leads, on which we are now working to refine and enhance our analytical methods so as to provide founded insights into the historical development of Hebrew and the process of DSS textual composition.
A Dataset of Ancient Chinese Math Word Problems and an Application for Research in Historic Mathematics
Authors: Florian Keßler
Venue: NAACL 2025 (Proceedings of the Second Workshop on Ancient Language Processing)

Abstract: Solving math word problems, i.e. mathemati-cal problems stated in natural language, has re-ceived much attention in the Artificial Intelli-gence (AI) community over the last years. Un-surprisingly, research has focused on problems stated in contemporary languages. In contrast to this, in this article, we introduce a dataset of math word problems that is extracted from an-cient Chinese mathematical texts. The dataset is made available.1 We report a baseline per-formance for GPT-4o solving the problems in the dataset using a Program-of-Thought paradigm that translates the mathematical pro-cedures in the original texts into Python code, giving acceptable performance but showing that the model often struggles with understand-ing the pre-modern language. Finally, we de-scribe how the generated code can be used for research into the history of mathematics, by of-fering a way to search the texts by abstract op-erations instead of specific lexemes.