arXiv:2301. 03709v3 Announce Type: replace-cross Abstract: Consistent and holistic expression of software requirements is important for the success of software projects.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2602. 22456v2 Announce Type: replace-cross Abstract: Requirements are inherently interconnected through various types of dependencies.
arXiv:2311. 17633v2 Announce Type: replace-cross Abstract: Transformers have dominated empirical machine learning models of natural language processing.
arXiv:2608. 04215v1 Announce Type: cross Abstract: The growing diversity of code clone types, from syntactic copies to cross-language semantic clones to AI-generated duplicates, has created a fragmentation crisis in clone detection.
CoMerge introduces a conflict‑driven preference optimization framework for merging multi‑task large language models, reframing merging as a preference problem that uses self‑supervised hard negative samples derived from naive merging defects. By optimizing lightweight, tensor‑wise merging coefficients, the method mitigates parameter‑space conflicts while preserving task‑specific capabilities. Experiments show CoMerge achieves an average normalized performance of 0.9968 on MergeBench and improves conflict‑sensitive tasks on Llama‑3.1‑8B‑Instruct, outperforming both data‑free and data‑driven baselines while optimizing only 1,445 scalar coefficients.
CoMerge is a conflict‑driven preference optimization framework for merging multiple expert language models into a single multi‑task model without full retraining. It treats model merging as a preference optimization problem, using self‑supervised, conflict‑driven hard negative samples derived from naive merging defects to refine lightweight, tensor‑wise merging coefficients. Experiments show CoMerge achieves near‑perfect performance on MergeBench and improves instruction‑following and safety on Llama‑3.1‑8B‑Instruct while optimizing only 1,445 scalar coefficients.
Stack Trace-Based Crash Deduplication with Transformer Adaptation introduces dedupT, a transformer‑based method that models entire stack traces instead of isolated frames. The approach first fine‑tunes a pretrained language model on stack traces and then trains a fully‑connected network to rank duplicate crashes. Experiments on four public datasets show dedupT improves Mean Reciprocal Rank by over 15% versus the best deep‑learning baseline and up to 10% over traditional methods, while also achieving higher ROC‑AUC for unique crash detection.