We present VWEN, a 0.8B-parameter model for Vietnamese paraphrasing at a requested level of change and for five related tasks: generating several paraphrases, simplification, gist, register transfer and paraphrase judgement. VWEN is Qwen3.5-0.8B fine-tuned on about 7k samples chosen by a four-stage pipeline: rules and deduplication, a bidirectional entailment filter, a divergence score that assigns low and high paraphrase levels, and selection for coverage and instruction-following difficulty. We compare it with paraphrase-only recipes from the same pipeline and with training on the ViSP corpus under matched and larger step budgets. On high-divergence paraphrasing, VWEN reaches a success rate (requested level reached and meaning preserved) of 0.61 against 0.46 for the ViSP model at matched compute, a difference that holds on inputs resembling ViSP. Given four times more training examples, the ViSP model closes the gap on such inputs but not on questions, which ViSP lacks and which it often turns into statements. Across all comparisons the entailment filter is the most effective stage. Training on paraphrases alone erases abilities of the base model that the multi-task data preserves.
Bao Truong, Quang Nguyen, Baoru Huang, Jinpei Han, Van Nguyen, Ngan Le, Minh-Tan Pham, Doan Huy Hien 等 9 位作者
原文摘要
Seismic images reconstruct subsurface reflectivity from field recordings, guiding exploration and reservoir monitoring. Gas chimneys are vertical anomalies caused by subsurface fluid migration. Understanding these phenomena is crucial for assessing hydrocarbon potential and avoiding drilling hazards. However, accurate detection is challenging due to strong seismic attenuation and scattering. Traditional physics-based methods are computationally expensive and sensitive to model errors, while deep learning offers efficient alternatives, yet lacks labeled datasets. In this work, we introduce \textbf{SIGMA}, a new physics-based dataset for gas chimney understanding in seismic images, featuring (i) pixel-level gas-chimney mask for detection and (ii) paired degraded and ground-truth image for enhancement. We employed physics-based methods that cover a wide range of geological settings and data acquisition conditions. Comprehensive experiments demonstrate that SIGMA serves as a challenging benchmark for gas chimney interpretation and benefits general seismic understanding.