Here is today's selection of cs.CL papers. The featured research explores the intersection of language models with educational and political applications, focusing on generating reading comprehension exercises and fact-checking. Additionally, innovative approaches in data augmentation and symbolic algorithms highlight advancements in text processing and unification.

A symbolic Perl algorithm for the unification of Nahuatl word spellings

In this paper, we describe a symbolic model for the automatic orthographic unification of Nawatl text documents. Our model is based on algorithms that we have previously used to analyze sentences in Nawatl, and on the corpus called $π$-yalli, consisting of texts in several Nawatl orthographies. Our automatic unification algorithm implements linguistic rules in symbolic regular expressions. We also present a manual evaluation protocol that we have proposed and implemented to assess the quality of the unified sentences generated by our algorithm, by testing in a sentence semantic task. We have obtained encouraging results from the evaluators for most of the desired features of our artificially unified sentences

Skeletons Matter: Dynamic Data Augmentation for Text-to-Query

The task of translating natural language questions into query languages has long been a central focus in semantic parsing. Recent advancements in Large Language Models (LLMs) have significantly accelerated progress in this field. However, existing studies typically focus on a single query language, resulting in methods with limited generalizability across different languages. In this paper, we formally define the Text-to-Query task paradigm, unifying semantic parsing tasks across various query languages. We identify query skeletons as a shared optimization target of Text-to-Query tasks, and propose a general dynamic data augmentation framework that explicitly diagnoses model-specific weaknesses in handling these skeletons to synthesize targeted training data. Experiments on four Text-to-Query benchmarks demonstrate that our method achieves state-of-the-art performance using only a small amount of synthesized data, highlighting the efficiency and generality of our approach and laying a solid foundation for unified research on Text-to-Query tasks. We release our code at https://github.com/jjjycaptain/Skeletron.

Generating Reading Comprehension Exercises with Large Language Models for Educational Applications

With the rapid development of large language models (LLMs), the applications of LLMs have grown substantially. In the education domain, LLMs demonstrate significant potential, particularly in automatic text generation, which enables the creation of intelligent and adaptive learning content. This paper proposes a new LLMs framework, which is named as Reading Comprehension Exercise Generation (RCEG). It can generate high-quality and personalized English reading comprehension exercises automatically. Firstly, RCEG uses fine-tuned LLMs to generate content candidates. Then, it uses a discriminator to select the best candidate. Finally, the quality of the generated content has been improved greatly. To evaluate the performance of RCEG, a dedicated dataset for English reading comprehension is constructed to perform the experiments, and comprehensive evaluation metrics are used to analyze the experimental results. These metrics include content diversity, factual accuracy, linguistic toxicity, and pedagogical alignment. Experimental results show that RCEG significantly improves the relevance and cognitive appropriateness of the generated exercises.

A Reproducible Framework for Neural Topic Modeling in Focus Group Analysis

Focus group discussions generate rich qualitative data but their analysis traditionally relies on labor-intensive manual coding that limits scalability and reproducibility. We present a rigorous, reproducible computational framework for applying neural topic modeling to focus group transcripts, addressing fundamental methodological challenges: hyperparameter sensitivity, model stability, and validation of interpretability. Using BERTopic applied to ten focus groups exploring HPV vaccine perceptions in Tunisia (1,076 utterances), we conducted systematic evaluation across 27 hyperparameter configurations, assessed stability through bootstrap resampling with 30 replicates per configuration, and validated interpretability through formal human evaluation by three domain experts. Our analysis demonstrates substantial sensitivity to hyperparameter choices and reveals that metric selection for stability assessment must align with analytical goals. A hierarchical merging strategy (extracting fine-grained topics for stability then consolidating for interpretability) effectively navigates the stability-coherence tradeoff, achieving coherence of 0.558 compared to 0.539 for direct extraction. Human validation confirmed topic quality with very good inter-rater reliability (ICC = 0.79, weighted Cohen's kappa = 0.578). Our framework provides practical guidelines that researchers can adapt to their own qualitative research contexts. All code, data processing scripts, and evaluation protocols are publicly available to support reproduction and extension of this work.

Large Language Models Require Curated Context for Reliable Political Fact-Checking -- Even with Reasoning and Web Search

Large language models (LLMs) have raised hopes for automated end-to-end fact-checking, but prior studies report mixed results. As mainstream chatbots increasingly ship with reasoning capabilities and web search tools -- and millions of users already rely on them for verification -- rigorous evaluation is urgent. We evaluate 15 recent LLMs from OpenAI, Google, Meta, and DeepSeek on more than 6,000 claims fact-checked by PolitiFact, comparing standard models with reasoning- and web-search variants. Standard models perform poorly, reasoning offers minimal benefits, and web search provides only moderate gains, despite fact-checks being available on the web. In contrast, a curated RAG system using PolitiFact summaries improved macro F1 by 233% on average across model variants. These findings suggest that giving models access to curated high-quality context is a promising path for automated fact-checking.