arXiv:2601.20476v2 Announce Type: replace Abstract: GenAI is widespread in educational applications; however, it is known to generate content with intrinsic and extrinsic hallucination. We introduce...
Imag‑Eval is a new language‑grounded benchmark for evaluating Text‑to‑Image models, focusing on how well they follow compositional natural‑language instructions. It disentangles prompt length from compositional difficulty by independently varying the number of instances and the combination of constraints (rules), providing 1,140 prompts and 8,842 rule combinations. The study shows that for structured skills, the difficulty is mainly driven by the number of grounded rules and their binding to instances rather than prompt length alone.
arXiv:2607. 28683v1 Announce Type: cross Abstract: Large language models benefit from elements in natural language, such as metaphors and analogies in training data and inference input to achieve generalisability across different domains.
arXiv:2511. 14967v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown great promise in generating structured diagrams from natural language descriptions, particularly Mermaid sequence diagrams for software engineering.
arXiv:2604. 22207v2 Announce Type: replace-cross Abstract: Due to the textual and repetitive nature of many Requirements Engineering (RE) artefacts, Large Language Models (LLMs) have proven useful to automate their generation and processing.
arXiv:2606. 28406v1 Announce Type: new Abstract: Text-to-image and multimodal generative models are increasingly used to produce scientific figures such as mechanism diagrams, experimental-design schematics, conceptual frameworks, and graphical abstracts.
arXiv:2601. 06196v3 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently generate factually incorrect or unsupported content, commonly referred to as hallucinations.
arXiv:2607. 07117v1 Announce Type: cross Abstract: In text-to-image in-context learning (T2I-ICL), a model has to infer a latent compositional pattern from fewshot demonstrations for generating a query image.
arXiv:2608. 07460v1 Announce Type: cross Abstract: While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly require creativity (e.
The paper presents InstruBPM, an instruction‑tuned open‑weight large language model that generates BPMN process models from natural language descriptions. By preparing paired text‑diagram data and applying parameter‑efficient fine‑tuning with quantization, the authors demonstrate that the tuned model outperforms both untuned open‑weight baselines and strong proprietary models in sequence, structural, and guideline‑conformance metrics. The approach is cost‑effective, privacy‑preserving, and supports on‑prem deployment, with publicly released models and scripts for reproducibility.
arXiv:2607. 28073v1 Announce Type: new Abstract: In demanding professional environments and meeting review scenarios, lengthy text often imposes a high cognitive load.
PRISM is a modality‑agnostic, category‑theoretic framework that measures and refines multimodal analogies by representing them as explicit relational mappings. It introduces a pullback score to quantify relational alignment and an iterative refinement loop that uses this score as feedback to improve generated images. On the AnaloBench benchmark, PRISM’s pullback score alone achieves 82.5% accuracy, and human evaluations show a 57.65% preference for refined outputs, though refinement may sometimes favor visually crowded compositions.