nlp

This paper introduces IDRAAK, an interpretable framework for detecting semantic drift in translated technical requirements using a Semantic Requirement Representation and multiple detection workflows, showing that simple few-shot prompting outperforms more complex multi-agent approaches.

This paper presents a conceptual framework for using NLP to address mental healthcare barriers in Algeria and other low-resource, multilingual settings, proposing a research and policy roadmap.

This paper presents a grounded and decomposed framework for evaluating relation-level hallucinations in abstractive summarization, introducing a normalized Relation Hallucination Index (RHI) with linguistically informed relation extraction enhancements.

This paper studies how multilingual training helps identify figurative language in proverbs across seven languages, introducing a multidimensional annotation framework and finding that about 50% of translated multilingual data is sufficient for near-optimal performance.

This paper investigates when classifier-free guidance (CFG) is actually necessary in masked diffusion language models, showing that guidance dependence is prompt-specific and can often be removed without losing constraint satisfaction, leading to a defined 'commitment horizon'.

This paper introduces APEX-VW, a new document-level English-Spanish post-editing dataset built from NHS virtual-ward documents and professional post-editing in Trados Studio. The corpus is designed to support research on terminology normalisation, correction propagation, and human-in-the-loop translation support.

Presents LitTraceQA, a benchmark for scientific question answering that requires systems to retrieve relevant papers, locate supporting evidence, and produce verified answers in multiple formats.

This paper introduces NLP Psychometrics, a framework that treats psychological prediction from text as a psychometric problem. Using LLM personas, emotional profiles, and syntactic-semantic networks with random forest regressors, it explains up to 76% of variance in mental health scores and shows promise and limits of synthetic data for psychometric prediction.

The paper introduces Stoicheia, a 405M-parameter character-level masked diffusion encoder for Ancient Greek that unifies textual restoration, parsing, and metrical scansion in a single model, outperforming prior systems like Ithaca on benchmark tasks.

This paper proposes measuring concept content in text using LLM internal activations via linear probes and RFM concept vectors, applied to ESG classification. The best linear probe approaches fine-tuned classifier accuracy without task-specific fine-tuning and outperforms the model's own output, showing activations carry concept content beyond responses.

This paper introduces MTI-GNN, a multi-perspective triad interaction graph neural network that models Beck's cognitive triad for cognitive distortion detection, outperforming supervised baselines and prompted LLMs across multiple multilingual datasets.

The paper presents an unsupervised method to extract linguistic metaphors and group them into conceptual metaphors, applying the approach to analyze framing differences in left- vs. right-leaning podcasts.

This paper introduces Factorized Hypothesis Search (FHS), a method for retrieving concepts from large taxonomies when inputs provide indirect contextual evidence, such as table cells or clinical notes. FHS achieves strong results on financial taxonomy tagging and CodiEsp clinical coding, outperforming non-oracle baselines in Recall@1, MRR, and accuracy.

This paper analyzes the embedding space of Masked Diffusion Language Models (MDLMs) and finds it is hyperspherical, making linear interpolation suboptimal. The authors introduce Spherical Soft-Masking (S-SM), which uses SLERP and Fréchet means on the hypersphere, improving MAUVE and perplexity over prior soft-masking approaches.

This paper introduces the Cross-Lingual Comprehension Gap (CLCG) metric to measure how LLM response quality degrades when content is presented in non-English languages. Across 18 languages and multiple models, it finds a significant performance drop, especially for low-resource languages, questioning the assumption of English-centric capability transfer.

SkillEval proposes an interpretable framework that decomposes agent skill quality into distinct semantic properties by projecting skill document representations onto fixed scoring directions, enabling diagnosis and targeted revision of skill documents.

This paper proposes DKG-MTI, a dual knowledge graph framework that augments LLM-based inference with structure-aware knowledge alignment to jointly predict aspect ratings and generate user intent statements from online travel reviews.

An approachable explanation of why transformers need positional encoding, using a bug report analogy and Python's Counter to illustrate how parallel processing loses word order.

The open-source novel character parsing skill gained 385 Stars in three days, and updated character chart prompts, report layout, and multilingual support. Future plans include adding a character relationship graph.

A developer shares a postmortem of building a Hindi-English voice agent for fintech, highlighting challenges with number readback, code-mixed TTS, latency under load, and compliance. Key fix was choosing TTS with first-class support for Indian code-mixing and testing at real concurrency.