The paper introduces Adaptive Dual-Teacher Distillation with Subnetwork Rectification (DDSR), a method for black-box domain adaptation that fuses predictions from a black-box source model and a vision‑language model to generate reliable pseudo‑labels for the target domain. DDSR uses adaptive prediction fusion, subnetwork regularization to prevent overfitting, iterative refinement of pseudo‑labels and prompts, and class‑wise prototypes for self‑training. Experiments on several benchmark datasets show that DDSR outperforms existing state‑of‑the‑art approaches, even those that have access to source data or source model parameters.
By Zhe Zhang, Jing Li, Wanli Xue, Xu Cheng, Jianhua Zhang, Qinghua Hu, Shengyong Chen
RePro is a web‑recycling technique that trains a small language model (as little as 1 B parameters) with reinforcement learning to produce high‑quality, faithful rephrasings of pretraining data. The method uses one quality reward and three faithfulness rewards to preserve core semantics and structure while converting organic data into better training examples. Experiments show that RePro boosts downstream accuracy by 3.7–14.5 % over organic‑only baselines and improves data efficiency 2–3×, outperforming prior prompting‑based recycling approaches.
By Zichun Yu, Chenyan Xiong
arXiv:2609.37076v1 Announce Type: new
Abstract: Large language models trained on vast corpora inherently risk memorizing harmful content that may later re-emerge in their outputs. To mitigate this is...
By Puning Yang, Qizhou Wang, Junchi Yu, Bo Han, Xiuying Chen
arXiv:2607. 19604v1 Announce Type: cross Abstract: Injecting factual knowledge into large language models (LLMs) reliably and at scale remains an open challenge.
By Nischay Dhankhar, Dos Baha, Abulhair Saparov
arXiv:2607. 17467v1 Announce Type: cross Abstract: Few-shot Test-Time Domain Adaptation (FSTT-DA) seeks to adapt models to novel domains using only a handful of unlabeled target samples.
By Siobhan Reid, Zhixiang Chi, Li Gu, Omid Reza Heidari, Ziqiang Wang, Yang Wang
arXiv:2503.22764v3 Announce Type: replace-cross
Abstract: The large language model (LLM) is typically integrated into the mainstream optimization protocol. However, it remains underexplored whether m...
By Mingyuan Zhang, Yue Bai, Huan Wang, Yizhou Wang, Qihua Dong, Yitian Zhang, Yun Fu
NADS (Navigator‑Guided Data Selection) is a fine‑tuning framework for large language models that mitigates catastrophic forgetting when the original pretraining data are unavailable. It first trains a navigator model without preservation regularization to detect capability drift, then scores synthetic data candidates using the predictive divergence between the navigator and the pretrained model. Finally, it selects a diverse, utility‑aware subset of synthetic data and fine‑tunes while distilling pretrained behavior, achieving a better balance between new‑task performance and general capability preservation with reduced selection overhead.
By Jianhao Zhang, Ou Wu, Yi Du
arXiv:2607. 04733v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is the standard approach for adapting pretrained language models to downstream domains, yet it often improves target-domain behavior at the cost of degrading pre-existing capabilities.
By Yueyang Wang, Baolong Bi, Shuo Lu, Jingyuan Zhang
Hypernetworks have recently shown success in dynamically adapting the parameters of Large Language Models (LLMs) at runtime based on signals such as task descriptions or additional demostrations. Here...
The paper investigates how the choice of Transformer layers for fine‑tuning affects both task performance and the preservation of pre‑trained capabilities. By measuring layer sensitivity with a forward‑only input–output cosine similarity metric, the authors identify layers that are more suitable for adaptation. They introduce Layer‑Selective LoRA (LS‑LoRA), which trains adapters only in low‑similarity layers, and demonstrate that this approach improves target‑task results while better retaining commonsense reasoning compared to standard all‑layer LoRA.
By Zhiqiang Pang, Zihong Sun, Qi Xie, Jun Shu, Deyu Meng, Zongben Xu
arXiv:2506. 11042v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a resource-efficient strategy for adapting Pretrained Foundation Models (PFMs) by learning a small number of task-specific updates $\Delta W$.
By Guangning Xu, Baoquan Zhang, Michael. K. Ng
The paper introduces query‑conditioned hypernetworks that predict distributions over LoRA weight updates for large language models. By learning a distribution rather than a single point estimate, the method allows sampling multiple adapted models for the same query, improving performance over deterministic hypernetworks and token‑sampling baselines. The study also shows that these learned updates can transfer across different queries, indicating reusable adaptation patterns.
By Azal Ahmad Khan, Keshav Ramji, Tahira Naseem, Ali Anwar, Ram\'on Fernandez Astudillo