Information extraction (IE) in specialized domains like computer science and chemistry is challenged by the poor generalization of traditional models and the knowledge deficits of general-purpose Large Language Models (LLMs). We introduce a robust, LLM-based framework featuring two core contributions: an end-to-end training and inference paradigm that combines continual pre-training (CPT) for knowledge injection, supervised fine-tuning (SFT) for task alignment, and retrieval-augmented generation (RAG) for inference-time enhancement; and a novel LLM-assisted data annotation pipeline for the efficient creation of high-quality training data. Comprehensive experiments demonstrate that while fine-tuning alone yields strong in-domain performance, our complete framework exhibits superior robustness and generalization. It consistently achieves state-of-the-art results in challenging domain-shift and novel-schema scenarios, validating our integrated approach for building adaptable and high-performance domain-specific IE systems.

With an auxiliary corpus (non-target speaker corpus) for model pre-training, Text-to-Speech (TTS) methods can generate high-quality speech with a limited target speaker corpus. However, this approach comes with expensive training costs. To overcome the challenge, a high-quality TTS method is proposed, significantly reducing training costs while maintaining the naturalness of synthesized speech. In this paper, we propose an auxiliary corpus compression algorithm that reduces the training cost while the naturalness of the synthesized speech is not significantly degraded. We then use the compressed corpus to pre-train the proposed TTS model CMDTTS, which fuses phoneme and word multi-level prosody modeling components and denoises the generated mel-spectrograms using denoising diffusion probabilistic models (DDPMs). In addition, a fine-tuning step that the conditional generative adversarial network (cGAN) is introduced to embed the target speaker feature and improve speech quality using the target speaker corpus. Experiments are conducted on Chinese and English single speaker’s corpora, and the results show that the method effectively balances the model training speed and the synthesized speech quality and outperforms the current models.

Automated Essay Scoring (AES) aims to score essays written in response to specific prompts. Many AES models have been proposed, but most of them are either prompt-specific or prompt-adaptive and cannot generalize well on “unseen” prompts. This work focuses on improving the generalization ability of AES models from the perspective of domain generalization, where the data of target prompts cannot be accessed during training. Specifically, we propose a prompt-aware neural AES model to extract comprehensive representation for essay scoring, including both prompt-invariant and prompt-specific features. To improve the generalization of representation, we further propose a novel disentangled representation learning framework. In this framework, a contrastive norm-angular alignment strategy and a counterfactual self-training strategy are designed to disentangle the prompt-invariant information and prompt-specific information in representation. Extensive experimental results on datasets of both ASAP and TOEFL11 demonstrate the effectiveness of our method under the domain generalization setting.