Document parsing from scanned images into structured formats remains a significant challenge due to its complexly intertwined elements such as text paragraphs, figures, formulas, and tables. Existing supervised fine-tuning methods often struggle to generalize across diverse document types, leading to poor performance, particularly on out-of-distribution data. This issue is further exacerbated by the limited availability of high-quality training data for layout-aware parsing tasks. To address these challenges, we introduce layoutRL, a reinforcement learning framework that optimizes layout understanding through composite rewards integrating normalized edit distance, paragraph count accuracy, and reading order preservation. To support this training, we construct the Infinity-Doc-400K dataset, which we use to train Infinity-Parser, a vision-language model demonstrating robust generalization across various domains. Extensive evaluations on benchmarks including OmniDocBench, olmOCR-Bench, PubTabNet, and FinTabNet show that Infinity-Parser consistently achieves state-of-the-art performance across a broad range of document types, languages, and structural complexities, substantially outperforming both specialized document parsing systems and general-purpose vision-language models. We will release our code, dataset, and model to facilitate reproducible research in document parsing.

In dialogue intent detection, the challenge of acquiring sufficient corpora and the high cost of manual annotation often lead to incorrectly labeled or unrepresentative samples, which can hinder the generalization ability of classification models. Additionally, as using large language models for generating synthetic samples for data augmentation becomes more common, these synthetic samples may exacerbate the problem by introducing additional noise due to the models’ limited prior knowledge. To address this challenge, this paper proposes an interpretable Sample Filter by Topic Modeling (SFTM) framework. By evaluating the diversity and authenticity of the samples, SFTM effectively reduces the quantity of real and synthetic samples while improving the performance of the classification models. Our codes are publicly available at https://github.com/gumbouh/SFTM.

In real-world scenarios, cross-domain slot filling in spoken language understanding remains a significant challenge due to data scarcity. Previous works exhibit limited generalization ability in the target domain, demonstrating effective knowledge transfer only on seen slots while performing poorly on unseen slots. Although large language models (LLMs) can alleviate this issue to some extent, they underperform on seen slots compared to small models. To address these challenges, we introduce a novel framework that harnesses the power of a small model to augment the inferential capabilities of LLMs without additional training. Initially, we utilize target domain samples synthesized by LLMs as pre-calculated demonstrations, which are curated and chosen using confidence metrics derived from a small model. We further extract slot predictions from the small model to fully exploit its robust learning of familiar slots. Finally, during the inference process for test inputs, we integrate these demonstrations and slot prediction insights as references to enhance the slot filling performance of LLMs. Experiments on a slot filling dataset and a NER dataset including eight cross-domain settings show our framework achieves the best results. Our codes are publicly available at https://github.com/SIGSDSscau/SLSF.

Pre-trained Language Models (PLMs) have achieved significant success in text classification. However, they still face challenges with hard samples, which refer to instances where the model exhibits diminished confidence in distinguishing new samples. Existing research has addressed related issues, but often overlooks the semantic information inherent in the labels, treating them merely as one-hot vectors. In this paper, we propose Logits Reranking via Semantic Labels (LRSL), a model-agnostic post-processing method that leverages label semantics and auto detection of hard samples to improve classification accuracy. LRSL automatically identifies hard samples, which are then jointly processed by MLP-based and Similarity-based approaches. Applied only during inference, LRSL operates solely on classification logits, reranking them based on semantic similarities without interfering with the model’s training process. The experiments demonstrate the effectiveness of our method, showing significant improvements across different PLMs. Our codes are publicly available at https://github.com/SIGSDSscau/LRSL.