Data augmentation is a critical technique in deep learning. Traditional methods like Back-translation typically focus on lexical-level rephrasing, which primarily produces variations with the same semantics. While large language models (LLMs) have enhanced text augmentation by their “knowledge emergence” capability, controlling the style and structure of these outputs remains challenging and requires meticulous prompt engineering. In this paper, we propose LMTransplant, a novel text augmentation paradigm leveraging LLMs. The core idea of LMTransplant is transplant-then-regenerate: incorporating seed text into a context expanded by LLM, and asking the LLM to regenerate a variant based on the expanded context. This strategy allows the model to create more diverse and creative content-level variants by fully leveraging the knowledge embedded in LLMs, while preserving the core attributes of the original text. We evaluate LMTransplant across various text-related tasks, demonstrating its superior performance over existing text augmentation methods. Moreover, LMTransplant demonstrates exceptional scalability as the size of augmented data grows.

The increasing size and complexity of large language models (LLMs) raise concerns about their ability to “cheat” on standard Question Answering (QA) benchmarks by memorizing task-specific data. This undermines the validity of benchmark evaluations, as they no longer reflect genuine model capabilities but instead the effects of data leakage. While existing methods detect such leakage, they fail to address the long-term challenge of mitigating it. In this paper, we introduce LastingBench, a novel approach to reinforce and safeguard existing benchmarks against knowledge leakage. Our method involves identifying leakage points through perturbation-based detection, followed by counterfactual rewriting to disrupt memorization while preserving the benchmark’s original evaluative intent. We demonstrate that our approach significantly reduces memorization effects in long-context QA benchmarks, providing a more accurate assessment of model reasoning and generalization abilities. Our experiments show that LastingBench not only uncovers substantial leakage in benchmarks like HotpotQA but also yields a more reliable evaluation of state-of-the-art models, ensuring that benchmarks remain effective and resilient over time.

Attention based methods for image-text generation often focus on visual features individually, while ignoring relationship information among image features that provides important guidance for generating sentences. To alleviate this issue, in this work we propose the Joint Relationship Attention Network (JRAN) that novelly explores the relationships among the features. Specifically, different from the previous relationship based approaches that only explore the single relationship in the image, our JRAN can effectively learn two relationships, the visual relationships among region features and the visual-semantic relationships between region features and semantic features, and further make a dynamic trade-off between them during outputting the relationship representation. Moreover, we devise a new relationship based attention, which can adaptively focus on the output relationship representation when predicting different words. Extensive experiments on large-scale MSCOCO and small-scale Flickr30k datasets show that JRAN achieves state-of-the-art performance. More remarkably, JRAN achieves new 28.3% and 58.2% performance in terms of BLEU4 and CIDEr metric on Flickr30k dataset.

While Transformers have had significant success in paragraph generation, they treat sentences as linear sequences of tokens and often neglect their hierarchical information. Prior work has shown that decomposing the levels of granularity (e.g., word, phrase, or sentence) for input tokens has produced substantial improvements, suggesting the possibility of enhancing Transformers via more fine-grained modeling of granularity. In this work, we present continuous decomposition of granularity for neural paraphrase generation (C-DNPG): an advanced extension of multi-head self-attention with: 1) a granularity head that automatically infers the hierarchical structure of a sentence by neurally estimating the granularity level of each input token; and 2) two novel attention masks, namely, granularity resonance and granularity scope, to efficiently encode granularity into attention. Experiments on two benchmarks, including Quora question pairs and Twitter URLs have shown that C-DNPG outperforms baseline models by a significant margin. Qualitative analysis reveals that C-DNPG indeed captures fine-grained levels of granularity with effectiveness.