Achieving consistent high-quality machine translation (MT) across diverse domains remains a significant challenge, primarily due to the limited and imbalanced parallel training data available in various domains. While large language models (LLMs) have demonstrated impressive general understanding and generation abilities, their potential in multi-domain MT is under-explored. We establish a comprehensive benchmark for multi-domain translation, featuring 25 German⇔English and 22 Chinese⇔English test sets respectively covering 15 domains. Our evaluation of prominent LLMs reveals a discernible performance gap against traditional MT systems, highlighting domain overfitting and catastrophic forgetting issues after fine-tuning on domain-limited corpora. To mitigate this, we propose a domain Chain of Thought (CoT) fine-tuning technique that utilizes the intrinsic multi-domain intelligence of LLMs to improve translation performance. This method inspires the LLM to perceive domain information from the source text, which then serves as a helpful hint to guide the translation process. Despite being trained on a small dataset of four domains, our CoT fine-tune approach achieves notable enhancements in translation accuracy and domain robustness than traditional fine-tuning, as evidenced by an average 1.53 BLEU score increase in over 20 German→English distinct out-of-domain tests.

Vision-language models like CLIP, utilizing class proxies derived from class name text features, have shown a notable capability in zero-shot medical image diagnosis which is vital in scenarios with limited disease databases or labeled samples. However, insufficient medical text precision and the modal disparity between text and vision spaces pose challenges for such paradigm. We show analytically and experimentally that enriching medical texts with detailed descriptions can markedly enhance the diagnosis performance, with the granularity and phrasing of these enhancements having a crucial impact on CLIP’s understanding of medical images; and learning proxies within the vision domain can effectively circumvent the modal gap issue. Based on our analysis, we propose a medical visual proxy learning framework comprising two key components: a text refinement module that create high quality medical text descriptions, and a stable Sinkhorn algorithm for an efficient generation of pseudo labels which further guide the visual proxy learning. Our method elevates the Vanilla CLIP inference by supplying meticulously crafted clues to leverage CLIP’s existing interpretive power and using the feature of refined texts to bridge the vision-text gap. The effectiveness and robustness of our method are clearly demonstrated through extensive experiments. Notably, our method outperforms the state-of-the-art zero-shot medical image diagnosis by a significant margin, ranging from 1.69% to 15.31% on five datasets covering various diseases, confirming its immense potential in zero-shot diagnosis across diverse medical applications.

We participate in the translation task on Spanish to Aragonese, Spanish to Aranese and Spanish to Asturian. Initially, we conduct preliminary experiments to assess the basic translation capabilities of various models and evaluate the impact of fine-tuning with different data types. We then choose to fine-tune the Qwen2-0.5B model using a forward synthesized pseudo-corpus from the Apertium translation system to replicate its fundamental performance. Building on this distillation model, we explore three optimization strategies across the three language directions: (1) Assembling the provided FLORES+ dev sets into a 5-shot format translation training dataset and performing few-shot fine-tuning to enhance model performance. (2) Utilizing the FLORES+ dev sets as training data and applying the Contrastive Preference Optimization (CPO) strategy for further refinement. (3) Retrieving the 20 most similar translation examples from the FLORES+ dev sets using the BM25 algorithm and performing 20-shot translations with the Claude 3.5-sonnet model. After evaluating these strategies, we select the best-performing approach for each language pair as our submission result.

This paper describes Shanghai Jiao Tong University (SJTU LoveFiction) Discourse-Level Literary Translation systems for the WMT24shared task. We participate in the literary translation task on Chinese → English, Chinese →German and Chinese → Russian with uncon-strained tack.Check our paper for detail.

Concerns regarding Large Language Models (LLMs) to memorize and disclose private information, particularly Personally Identifiable Information (PII), become prominent within the community. Many efforts have been made to mitigate the privacy risks.However, the mechanism through which LLMs memorize PII remains poorly understood. To bridge this gap, we introduce a pioneering method for pinpointing PII-sensitive neurons (privacy neurons) within LLMs. Our method employs learnable binary weight masks to localize specific neurons that account for the memorization of PII in LLMs through adversarial training. Our investigations discover that PII is memorized by a small subset of neurons across all layers, which shows the property of PII specificity. Furthermore, we propose to validate the potential in PII risk mitigation by deactivating the localized privacy neurons. Both quantitative and qualitative experiments demonstrate the effectiveness of our neuron localization algorithm.