In NLP, mental health conditions are often modeled as isolated phenomena, without interpersonal context. We use Reddit posts about long-distance relationships to capture both mental health distress and associated relational triggers. We introduce the Relational Stress and Psychiatry Corpus (RSPC), containing 1,799 Reddit posts annotated by psychiatrists for diagnostic categories, including the most prevalent mood disorders (anxiety and depression), relational stressor triggers, and indications of relationship phase. We benchmark seven fine-tuned transformer models and five large language models across the tasks of multi-label disorder classification, relational trigger detection, and temporal phase prediction. We find clear task-dependent differences between model families, with Claude-3-Haiku achieving the best disorder classification performance (Macro-F1 = 0.538) and GPT-4o obtaining the strongest relational trigger detection performance (Macro-F1 = 0.519), suggesting distinct model capabilities. We further find strong associations between anxiety disorders and chronic relational uncertainty. Overall, RSPC establishes a benchmark for NLP tasks that consider relational context and supports a shift from individual-centric to context-aware mental health modeling, capturing the social and temporal dynamics of distress.

Neural Cellular Automata (NCAs) generate complex spatial patterns in vision by repeatedly applying a shared local rule to persistent cell states, combining local perception, weight-shared iteration, and gated in-place updates. We test this principle in language modelling with TextNCA, a 1D causal model in which token positions act as cells, windowed attention provides local perception, and a GRU-style gate updates their states. Its hierarchical variant cascades increasingly wide attention windows w ∈ 8,32,128 and reuses each stage’s parameters for T_s iterations. We train roughly 30M-parameter models on SlimPajama for 60k steps and evaluate on WikiText-103. TextNCA trails a similarly sized Transformer-6L (60.3 versus 52.8 best-run PPL), so we study it as an analytical probe rather than a performance alternative. Reordering or flattening the windows raises PPL by 16.7–70.8, whereas a non-iterating sliding-window Transformer preserving the staged order performs comparably across three seeds. These results identify the narrow-to-wide schedule as the dominant factor and provide no reliable evidence that iterated weight sharing improves perplexity at this scale. Intermediate-state analyses support a prepare-then-decode account in which early stages reorganise representations and the final wide stage performs most next-token loss reduction.

Curriculum learning helps language models tackle complex reasoning by gradually increasing task difficulty. However, it often fails to generate consistent step-by-step reasoning, especially in multilingual and low-resource settings where cross-lingual transfer from English to Indian languages remains limited. We propose IRIS: Interleaved Reinforcement with Incremental Staged Curriculum, a two-axis framework that combines Supervised Fine-Tuning on progressively harder problems (vertical axis) with Reverse Curriculum Reinforcement Learning to reduce reliance on step-by-step guidance (horizontal axis). We design a composite reward combining correctness, step-wise alignment, continuity, and numeric incentives, optimized via Group Relative Policy Optimization (GRPO). We release CL-Math, a dataset of 29k problems with step-level annotations in English, Hindi, and Marathi. Across standard benchmarks and curated multilingual test sets, IRIS consistently improves performance, with strong results on math reasoning tasks and substantial gains in low-resource and bilingual settings, alongside modest improvements in high-resource languages. Our code and dataset will be publicly available at https://github.com/avinanand/IRIS-Interleaved-Reinforcement-

Metaphors are highly creative constructs of human language that grow old and eventually die. Popular datasets used for metaphor processing tasks were constructed from dated source texts. In this paper, we propose NewsMet, a large high-quality contemporary dataset of news headlines hand-annotated with metaphorical verbs. The dataset comprises headlines from various sources including political, satirical, reliable and fake. Our dataset serves the purpose of evaluation for the tasks of metaphor interpretation and generation. The experiments reveal several insights and limitations of using LLMs to automate metaphor processing tasks as frequently seen in the recent literature. The dataset is publicly available for research purposes https://github.com/AxleBlaze3/NewsMet_Metaphor_Dataset.

To effectively characterize the nature of paraphrase pairs without expert human annotation, we proposes two new metrics: word position deviation (WPD) and lexical deviation (LD). WPD measures the degree of structural alteration, while LD measures the difference in vocabulary used. We apply these metrics to better understand the commonly-used MRPC dataset and study how it differs from PAWS, another paraphrase identification dataset. We also perform a detailed study on MRPC and propose improvements to the dataset, showing that it improves generalizability of models trained on the dataset. Lastly, we apply our metrics to filter the output of a paraphrase generation model and show how it can be used to generate specific forms of paraphrases for data augmentation or robustness testing of NLP models.

We introduce Explainable Scientific Research Assistant (ESRA), a literature discovery platform that augments search results with relevant details and explanations, aiding users in understanding more about their queries and the returned papers beyond existing literature search systems. Enabled by a knowledge graph we extracted from abstracts of 23k papers on the arXiv’s cs.CL category, ESRA provides three main features: explanation (for why a paper is returned to the user), list of facts (that are relevant to the query), and graph visualization (drawing connections between the query and each paper with surrounding related entities). The experimental results with humans involved show that ESRA can accelerate the users’ search process with paper explanations and helps them better explore the landscape of the topics of interest by exploiting the underlying knowledge graph. We provide the ESRA web application at http://esra.cp.eng.chula.ac.th/.