Post-training quantization (PTQ) has become a key approach for large language model (LLM) compression. Existing methods commonly adopt block-level reconstruction as a surrogate to calibrate each quantized Transformer block. However, our analysis shows that this loose objective can admit cancellation patterns, where the aggregated block-level reconstruction error is small even though the per-head errors remain large. We therefore revisit the PTQ formulation from a variational perspective. By treating per-head representations as latent variables and the block output as the observed variable, we find that conventional block-level reconstruction corresponds to optimizing only the observation-level term, while omitting a latent alignment term that captures head-level quantization error. Motivated by this finding, we propose HEAL, a Latent HEad ALignment framework that turns block-level reconstruction calibration into joint observation-latent calibration with stop-gradient head alignment. Experiments on LLaMA-2/3 and Qwen2.5 families under low-bit weight-activation quantization show that HEAL generally improves perplexity and zero-shot accuracy over block-level reconstruction baselines.

End-to-end Aspect-based Sentiment Analysis (EASA) is a natural language processing (NLP) task that involves extracting aspect terms and identifying the sentiments for them, which provides a fine-grained level of text analysis and thus requires a deep understanding of the running text. Many previous studies leverage advanced text encoders to extract context information and use syntactic information, e.g., the dependency structure of the input sentence, to improve the model performance. However, such models may reach a bottleneck since the dependency structure is not designed to provide semantic information of the text, which is also important for identifying the sentiment and thus leave room for further improvement. Considering that combinatory categorial grammar (CCG) is a formalism that expresses both syntactic and semantic information of a sentence, it has the potential to be beneficial to EASA. In this paper, we propose a novel approach to improve EASA with CCG supertags, which carry the syntactic and semantic information of the associated words and serve as the most important part of the CCG derivation. Specifically, our approach proposes a CCG supertag decoding process to learn the syntactic and semantic information carried by CCG supertags and use the information to guide the attention over the input words so as to identify important contextual information for EASA. Furthermore, a gate mechanism is used in incorporating the weighted contextual information into the backbone EASA decoding process. We evaluate our approach on three publicly available English datasets for EASA, and show that it outperforms strong baselines and achieves state-of-the-art results on all datasets.

Text style transfer (TST) is an important task in natural language generation, which aims to alter the stylistic attributes (e.g., sentiment) of a sentence and keep its semantic meaning unchanged. Most existing studies mainly focus on the transformation between styles, yet ignore that this transformation can be actually carried out via different hidden transfer patterns. To address this problem, we propose a novel approach, contrastive transfer pattern mining (CTPM), which automatically mines and utilizes inherent latent transfer patterns to improve the performance of TST. Specifically, we design an adaptive clustering module to automatically discover hidden transfer patterns from the data, and introduce contrastive learning based on the discovered patterns to obtain more accurate sentence representations, and thereby benefit the TST task. To the best of our knowledge, this is the first work that proposes the concept of transfer patterns in TST, and our approach can be applied in a plug-and-play manner to enhance other TST methods to further improve their performance. Extensive experiments on benchmark datasets verify the effectiveness and generality of our approach.