ymoslem/acl-abstracts huggingface dataset:
LLM`s Weakness in NER Doesn`t Stop It from Enhancing a Stronger SLM (2025)

Large Language Models (LLMs) demonstrate strong semantic understanding ability and extensive knowledge, but struggle with Named Entity Recognition (NER) due to hallucination and high training costs. Meanwhile, supervised Small Language Models (SLMs) efficiently provide structured predictions but lack adaptability to unseen entities and complex contexts. In this study, we investigate how a relatively weaker LLM can effectively support a supervised model in NER tasks. We first improve the LLM using LoRA-based fine-tuning and similarity-based prompting, achieving performance comparable to a SLM baseline. To further improve results, we propose a fusion strategy that integrates both models: prioritising SLM`s predictions while using LLM guidance in low confidence cases. Our hybrid approach outperforms both baselines on three classic Chinese NER datasets.
Named Entity Recognition in Context: Edit\_Dunhuang team Technical Report for Evahan2025 NER Competition (2025)

We present the Named Entity Recognition sys-tem developed by the Edit Dunhuang team for the EvaHan2025 competition. Our approach in-tegrates three core components: (1) Pindola, a modern transformer-based bidirectional en-coder pretrained on a large corpus of Classi-cal Chinese texts; (2) a retrieval module that fetches relevant external context for each target sequence; and (3) a generative reasoning step that summarizes retrieved context in Classical Chinese for more robust entity disambiguation. Using this approach, we achieve an average F1 score of 85.58, improving upon the competition baseline by nearly 5 points.
Make Good Use of GujiRoBERTa to Identify Entities in Ancient Chinese (2025)

This report describes our model submitted for the EvaHan 2025 shared task on named entity recognition for ancient Chinese literary works. Since we participated in the task of closed modality, our method is based on the appointed pretrained language model GujiRoBERTajian-fan and we used appointed datasets.We carried out experiments on decodingstrategies and schedulers to verify the effect of our method. In the final test, our method outperformed the official baseline, demonstrating its effectiveness. In the end, for the results, this report gives an analysis from the perspective of data composition.
GRoWE: A GujiRoBERTa-Enhanced Approach to Ancient Chinese NER via Word-Word Relation Classification and Model Ensembling (2025)

Named entity recognition is a fundamental task in ancient Chinese text analysis.Based on the pre-trained language model of ancient Chinese texts, this paper proposes a new named entity recognition method GRoWE. It uses the ancient Chinese texts pre-trained language model GujiRoBERTa as the base model, and the wordword relation prediction model is superposed upon the base model to construct a superposition model. Then ensemble strategies are used to multiple superposition models. On the EvaHan 2025 public test set, the F1 value of the proposed method reaches 86.79{\%}, which is 6.18{\%} higher than that of the mainstream BERT{\_}LSTM{\_}CRF baseline model, indicating that the model architecture and ensemble strategy play an important role in improving the recognition effect of naming entities in ancient Chinese texts.
When Less Is More: Logits-Constrained Framework with RoBERTa for Ancient Chinese NER (2025)

This report presents our team`s work on ancient Chinese Named Entity Recognition (NER) for EvaHan 20251. We propose a two-stage framework combining GujiRoBERTa with a Logits-Constrained (LC) mechanism. The first stage generates contextual embeddings using GujiRoBERTa, followed by dynamically masked decoding to enforce valid BMES transitions. Experiments on EvaHan 2025 datasets demonstrate the framework`s effectiveness. Key findings include the LC framework`s superiority over CRFs in high-label scenarios and the detrimental effect of BiLSTM modules. We also establish empirical model selection guidelines based on label complexity and dataset size.
Lemmatization of Cuneiform Languages Using the ByT5 Model (2025)

Lemmatization of cuneiform languages presents a unique challenge due to their complex writing system, which combines syllabic and logographic elements. In this study, we investigate the effectiveness of the ByT5 model in addressing this challenge by developing and evaluating a ByT5-based lemmatization system. Experimental results demonstrate that ByT5 outperforms mT5 in this task, achieving an accuracy of 80.55{\%} on raw lemmas and 82.59{\%} on generalized lemmas, where sense numbers are removed. These findings highlight the potential of ByT5 for lemmatizing cuneiform languages and provide useful insights for future work on ancient text lemmatization.
Simple Named Entity Recognition (NER) System with RoBERTa for Ancient Chinese (2025)

Named Entity Recognition (NER) is a fun-damental task in Natural Language Process-ing (NLP), particularly in the analysis of Chi-nese historical texts. In this work, we pro-pose an innovative NER model based on Gu-jiRoBERTa, incorporating Conditional Ran-dom Fields (CRF) and Long Short Term Mem-ory Network(LSTM) to enhance sequence la-beling performance. Our model is evaluated on three datasets from the EvaHan2025 competi-tion, demonstrating superior performance over the baseline model, SikuRoBERTa-BiLSTM-CRF. The proposed approach effectively cap-tures contextual dependencies and improves entity boundary recognition. Experimental re-sults show that our method achieves consistent improvements across almost all evaluation met-rics, highlighting its robustness and effective-ness in handling ancient Chinese texts.
Multi-Strategy Named Entity Recognition System for Ancient Chinese (2025)

We present a multi-strategy Named Entity Recognition (NER) system for ancient Chi-nese texts in EvaHan2025. Addressing dataset heterogeneity, we use a Conditional Random Field (CRF) for Tasks A and C to handle six entity types' complex dependencies, and a lightweight Softmax classifier for Task B`s simpler three-entity tagset. Ablation studies on training data confirm CRF`s superiority in capturing sequence dependencies and Softmax`s computational advantage for simpler tasks. On blind tests, our system achieves F1-scores of 83.94{\%}, 88.31{\%}, and 82.15{\%} for Test A, B, and C{---}outperforming baselines by 2.46{\%}, 0.81{\%}, and 9.75{\%}. With an overall F1 improvement of 4.30{\%}, it excels across historical and medical domains. This adaptability enhances knowledge extraction from ancient texts, offering a scalable NER framework for low-resource, complex languages.
Finetuning LLMs for EvaCun 2025 token prediction shared task (2025)

In this paper, we present our submission for the token prediction task of EvaCun 2025. Our sys-tems are based on LLMs (Command-R, Mistral, and Aya Expanse) fine-tuned on the task data provided by the organizers. As we only pos-sess a very superficial knowledge of the subject field and the languages of the task, we simply used the training data without any task-specific adjustments, preprocessing, or filtering. We compare 3 different approaches (based on 3 different prompts) of obtaining the predictions, and we evaluate them on a held-out part of the data.
Beyond Base Predictors: Using LLMs to Resolve Ambiguities in Akkadian Lemmatization (2025)

We present a hybrid approach for Akkadian lemmatization in the EvaCun 2025 Shared Task that combines traditional NLP techniques with large language models (LLMs). Our system employs three Base Predictors{--}a dictionary lookup and two T5 models{--}to establish initial lemma candidates. For cases where these pre-dictors disagree (18.72{\%} of instances), we im-plement an LLM Resolution module, enhanced with direct access to the electronic Babylonian Library (eBL) dictionary entries. This module includes a Predictor component that generates initial lemma predictions based on dictionary information, and a Validator component that refines these predictions through contextual rea-soning. Error analysis reveals that the system struggles most with small differences (like cap-italization) and certain ambiguous logograms (like BI). Our work demonstrates the benefits of combining traditional NLP approaches with the reasoning capabilities of LLMs when provided with appropriate domain knowledge.
A Low-Shot Prompting Approach to Lemmatization in the EvaCun 2025 Shared Task (2025)

This study explores the use of low-shot prompt-ing techniques for the lemmatization of ancient cuneiform languages using Large Language Models (LLMs). To structure the input data and systematically design effective prompt tem-plates, we employed a hierarchical clustering approach based on Levenshtein distance The prompt design followed established engineer-ing patterns, incorporating instructional and response-guiding elements to enhance model comprehension. We employed the In-Context Learning (ICL) prompting strategy, selecting example words primarily based on lemma fre-quency, ensuring a balance between commonly occurring words and rare cases to improve gen-eralization. During testing on the develop-ment set, prompts included structured examples and explicit formatting rules, with accuracy assessed by comparing model predictions to ground truth lemmas. The results showed that model performance varied significantly across different configurations, with accuracy reach-ing approximately 90{\%} in the best case for in-vocabulary words and around 9{\%} in the best case for out-of-vocabulary (OOV) words. De-spite resource constraints and the lack of input from a language expert, oour findings suggest that prompt engineering strategies hold promise for improving LLM performance in cuneiform language lemmatization.
Multi-Domain Ancient Chinese Named Entity Recognition Based on Attention-Enhanced Pre-trained Language Model (2025)

Recent advancements in digital humanities have intensified the demand for intelligent processing of ancient Chinese texts, particularly across specialized domains such as historical records and ancient medical literature. Among related research areas, Named Entity Recognition (NER) plays a crucial role, serving as the foundation for knowledge graph construction and deeper humanities computing studies. In this paper, we introduce a architecture specifically designed for multi-domain ancient Chinese NER tasks based on a pre-trained language model (PLM). Building upon the GujiRoberta backbone, we propose the GujiRoberta-BiLSTM-Attention-CRF model. Experimental results on three distinct domain-specific datasets demonstrate that our approach significantly outperforms the official baselines across all three datasets, highlighting the particular effectiveness of integrating an attention mechanism within our architecture.
EvaCun 2025 Shared Task: Lemmatization and Token Prediction in Akkadian and Sumerian using LLMs (2025)

The EvaCun 2025 Shared Task, organized as part of ALP 2025 workshop and co-located with NAACL 2025, explores how Large Language Models (LLMs) and transformer-based models can be used to improve lemmatization and token prediction tasks for low-resource ancient cuneiform texts. This year our datasets focused on the best attested ancient Near Eastern languages written in cuneiform, namely, Akkadian and Sumerian texts. However, we utilized the availability of datasets never before used on scale in NLP tasks, primarily first millennium literature (i.e. {\textquotedblleft}Canonical{\textquotedblright}) provided by the Electronic Babylonian Library (eBL), and Old Babylonian letters and archival texts, provided by Archibab. We aim to encourage the development of new computational methods to better analyze and reconstruct cuneiform inscriptions, pushing NLP forward for ancient and low-resource languages. Three teams competed for the lemmatization subtask and one for the token prediction subtask. Each subtask was evaluated alongside a baseline model, provided by the organizers.
Variable Extraction for Model Recovery in Scientific Literature (2025)

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 {\textquoteleft}infection rate ($\alpha$),{\textquotedblright} {\textquoteleft}recovery rate ($\gamma$),{\textquotedblright} and {\textquoteleft}mortality rate ($\mu$).{\textquotedblright} 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 (2025)

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 (2025)

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 (2025)

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 (2025)

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 (2025)

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 (2025)

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.
The Best of Both Worlds: Exploring Wolofal in the Context of NLP (2025)

This paper examines the three writing systems used for the Wolof language: the Latin script, the Ajami script (Wolofal), and the Garay script. Although the Latin alphabet is now the official standard for writing Wolof in Senegal, Garay and Ajami still play an important cultural and religious role, especially the latter. This article focuses specifically on Ajami, a system based on the Arabic script, and describes its history, its use, and its modern writings. We also analyze the challenges and prospects of these systems from the perspective of language preservation.
MultiProp Framework: Ensemble Models for Enhanced Cross-Lingual Propaganda Detection in Social Media and News using Data Augmentation, Text Segmentation, and Meta-Learning (2025)

Propaganda, a pervasive tool for influenc- ing public opinion, demands robust auto- mated detection systems, particularly for under- resourced languages. Current efforts largely focus on well-resourced languages like English, leaving significant gaps in languages such as Arabic. This research addresses these gaps by introducing MultiProp Framework, a cross- lingual meta-learning framework designed to enhance propaganda detection across multiple languages, including Arabic, German, Italian, French and English. We constructed a mul- tilingual dataset using data translation tech- niques, beginning with Arabic data from PTC and WANLP shared tasks, and expanded it with translations into German Italian and French, further enriched by the SemEval23 dataset. Our proposed framework encompasses three distinct models: MultiProp-Baseline, which combines ensembles of pre-trained models such as GPT-2, mBART, and XLM-RoBERTa; MultiProp-ML, designed to handle languages with minimal or no training data by utiliz- ing advanced meta-learning techniques; and MultiProp-Chunk, which overcomes the chal- lenges of processing longer texts that exceed the token limits of pre-trained models. To- gether, they deliver superior performance com- pared to state-of-the-art methods, representing a significant advancement in the field of cross- lingual propaganda detection.
Towards Unified Processing of Perso-Arabic Scripts for ASR (2025)

Automatic Speech Recognition (ASR) systems for morphologically complex languages like Urdu, Persian, and Arabic face unique challenges due to the intricacies of Perso-Arabic scripts. Conventional data processing methods often fall short in effectively handling these languages' phonetic and morphological nuances. This paper introduces a unified data processing pipeline tailored specifically for Perso-Arabic languages, addressing the complexities inherent in these scripts. The proposed pipeline encompasses comprehensive steps for data cleaning, tokenization, and phonemization, each of which has been meticulously evaluated and validated by expert linguists. Through expert-driven refinements, our pipeline presents a robust foundation for advancing ASR performance across Perso-Arabic languages, supporting the development of more accurate and linguistically informed multilingual ASR systems in future.
In-Depth Analysis of Arabic-Origin Words in the Turkish Morpholex (2025)

MorphoLex is an investigation that focuses on analyzing the roots, prefixes, and suffixes of words. Turkish Morpholex, for example, analyzes 48,472 Turkish words. Unfortunately, it lacks in-depth analysis of the Arabic-origin words, and does not include their accurate and correct roots. This study analyzes Arabic-origin words in the Turkish Morpholex, annotating their roots, morphological patterns, and semantic categories. The methodology developed for this work is adaptable to other languages influenced by Arabic, such as Urdu and Persian, offering broader implications for studying loanword integration across linguistic contexts.