Click-through rate (CTR) prediction is a critical task for e-commerce advertising systems. However, industrial ranking models still rely on sparse IDs and historical behavior signals, which limits their generalization to cold-start products, long-tail queries, and complex user intents. Although large language models (LLMs) provide strong semantic understanding and can generate behavioral rationales, directly using them for online ranking is impractical under strict latency constraints.

We propose GRAM-V2, a rationale-supervised ranking framework that transfers LLM-generated behavioral rationales into an efficient CTR model. GRAM-V2 uses a Teacher LLM to generate click-oriented rationales, distills them into a lightweight Student LLM, and aligns a two-tower CTR model with the Student’s representations and rationale signals. During online serving, GRAM-V2 uses only distilled representations and cached item embeddings, without generating rationales autoregressively. Large-scale offline experiments and online A/B testing show that GRAM-V2 improves CTR prediction accuracy, CTR, and advertising revenue while satisfying production latency constraints.

Beyond conventional paradigms of translating speech and text, recently, there has been interest in automated transcreation of images to facilitate localization of visual content across different cultures. Attempts to define this as a formal Machine Learning (ML) problem have been impeded by the lack of automatic evaluation mechanisms, with previous work relying solely on human evaluation. In this paper, we seek to close this gap by proposing a suite of automatic evaluation metrics inspired by machine translation (MT) metrics, categorized into: a) Object-based, b) Embedding-based, and c) VLM-based. Drawing on theories from translation studies and real-world transcreation practices, we identify three critical dimensions of image transcreation: cultural relevance, semantic equivalence and visual similarity, and design our metrics to evaluate systems along these axes. Our results show that proprietary VLMs best identify cultural relevance and semantic equivalence, while vision-encoder representations are adept at measuring visual similarity. Meta-evaluation across 7 countries shows our metrics agree strongly with human ratings, with average segment-level correlations ranging from 0.55-0.87. Finally, through a discussion of the merits and demerits of each metric, we offer a robust framework for automated image transcreation evaluation, grounded in both theoretical foundations and practical application. Our code can be found here: https://github.com/simran-khanuja/automatic-eval-transcreation

Machine Translation of Culture-Specific Items (CSIs) poses significant challenges. Recent work on CSI translation has shown some success using Large Language Models (LLMs) to adapt to different languages and cultures; however, a deeper analysis is needed to examine the benefits and pitfalls of each method. In this paper, we introduce the ChineseMenuCSI dataset, the largest for Chinese-English menu corpora, annotated with CSI vs Non-CSI labels and a fine-grained test set. We define three levels of CSI figurativeness for a more nuanced analysis and develop a novel methodology for automatic CSI identification, which outperforms GPT-based prompts in most categories. Importantly, we are the first to integrate human translation theories into LLM-driven translation processes, significantly improving translation accuracy, with COMET scores increasing by up to 7 points. The code and dataset are available at https://github.com/Henry8772/ChineseMenuCSI.

“Pretrained language models are making a more profound impact on our lives than ever before. They exhibit promising performance on a variety of general domain Natural Language Process-ing (NLP) tasks. However, few work focuses on Chinese financial NLP tasks, which comprisea significant portion of social communication. To this end, we propose FinBART, a pretrainedseq2seq language model for Chinese financial communication tasks. Experiments show thatFinBART outperforms baseline models on a series of downstream tasks including text classifica-tion, sequence labeling and text generation. We further pretrain the model on customer servicecorpora, and results show that our model outperforms baseline models and achieves promisingperformance on various real world customer service text mining tasks.”