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This paper presents a large collection of 57.5K transactional prompts extracted from GitHub, introducing an ontology to analyze their linguistic structure and usage patterns across languages, tasks, and modalities.
This paper examines intensional anaphora, arguing that description-based presuppositions better account for pronoun licensing than existing value-based accounts, and formalizes the proposal in a new logic called Plural Intensional Presuppositional predicate calculus (PIP).
This paper benchmarks 11 text augmentation methods, including classical, embedding-space, and LLM-based approaches, across 7 imbalanced classification datasets. It finds that retrieval-based oversampling (EmbSMOTE) outperforms LLM-based augmentation, and that preserving class-conditional structure matters more than surface-level diversity.
This academic paper introduces the SCOPEdataset, linking stigma signals in online sexual violence survivor narratives to support types in Reddit comments, finding that internalized stigma is most prevalent and that community responses remain stable across stigma types.
Introduces ELMER, an evolutionary language model that searches over natural-language policy descriptions and compiles them into executable programs, using fine-tuned Qwen3-8B with Direct Preference Optimization to control mutation strength and improve search efficiency.
This paper analyzes how soft skills are expressed in CVs of ML engineers, data scientists, and software engineers, using an LLM-based pipeline to distinguish explicit keywords from narrative descriptions, and tests demand-side hypotheses against candidate-side data.
AI has been used to translate ancient 5,000-year-old cuneiform tablets into English, demonstrating a novel application of language models for decoding historical texts.
This paper presents a machine learning pipeline for detecting self-introductions in legislative committee testimony, using features like bag-of-words and BERT probabilities. XGBoost achieves the best F1 score of 0.9747, improving further with BERT-augmented features.
Introduces SurakshaEval, a safety benchmark for LLMs covering ten Indian languages and English, with human-written prompts spanning seven harm types. Benchmarks multilingual LLMs and finds issues like over-refusal and missed implicit bias in Indic contexts.
Introduces NL2SHACL-Bench, a benchmark suite for translating natural language requirements into SHACL shapes, evaluating four state-of-the-art LLMs and showing they struggle with semantic equivalence for complex patterns.
This paper presents methods for georeferencing historical and vernacular place names found in biological specimen records that are absent from current gazetteers, comparing deterministic, probabilistic, and LLM-based approaches. Probabilistic inference achieves the highest accuracy, while LLMs offer competitive but less precise estimates.
This paper introduces ConstructCIE, a manually annotated dataset for extracting causal information from OSHA construction accident narratives, and evaluates supervised sequence taggers and instruction-tuned LLMs on end-to-end hierarchical extraction.
This paper presents an automated item evaluation (AIE) model that predicts item acceptance/rejection in standardized testing using fine-tuned DeBERTa classifiers on raw item text and Qwen3-generated critiques, achieving moderate accuracy and highlighting challenges with fairness-related rejections.
This paper surveys clinical communication processing using LLM-generated synthetic data and presents 13 case studies across EMS reports, nurse handoffs, and more, showing that synthetic data can bootstrap clinical NLP systems.
This paper presents HallDetect, a lightweight and reference-free framework for hallucination detection that decomposes generated content into atomic claims and verifies them with a compact entailment model. It outperforms comparably resourced baselines on multiple benchmarks and provides a claim-to-span audit trail.
This paper studies using sparse PPMI graph averaging to refine Random Indexing embeddings, showing it improves accuracy on a fairytales analogy benchmark but trails neural baselines on text8 and SimLex-999.
This paper proposes Abstract Event Causal Rules (AECR), a relation-level causal abstraction paradigm that converts concrete cause-effect pairs into generalized causal logic. It introduces a multi-agent system for causal induction and an attention-based encoder that improves event prediction, especially for rare and unseen events.
This paper proposes speculative correction, a training-free draft-then-refine decoding strategy for diffusion language models, showing quality-latency improvements using LLaDA2.1 models.
This paper evaluates whether structure-preserving de-identification via surrogate substitution maintains PHI detectability across multiple detectors, using equivalence testing on 57k paired spans across 7 languages and 11 detectors.
This paper presents Observatorio Lázaro, a continuously updated database and public web/API resource that monitors anglicism usage in the Spanish digital press using a neural sequence-labeling model, recording over two million borrowings from 2020 to 2026.