Label Delay in Continual Learning

Online continual learning, the process of training models on streaming data, has gained increasing attention in recent years. However, a critical aspect often overlooked is the label delay, where new data may not be labeled due to slow and costly annotation processes. We introduce a new continual learning framework with explicit modeling of the label delay between data and label streams over time steps. In each step, the framework reveals both unlabeled data from the current time step $t$ and labels delayed with $d$ steps, from the time step $t-d$. In our extensive experiments amounting to 1060 GPU days, we show that merely augmenting the computational resources is insufficient to tackle this challenge. Our findings underline a notable performance decline when solely relying on labeled data when the label delay becomes significant. More surprisingly, when using state-of-the-art SSL and TTA techniques to utilize the newer, unlabeled data, they fail to surpass the performance of a na\"ive method that simply trains on the delayed supervised stream. To this end, we introduce a simple, efficient baseline that rehearses from the labeled memory samples that are most similar to the new unlabeled samples. This method bridges the accuracy gap caused by label delay without significantly increasing computational complexity. We show experimentally that our method is the least affected by the label delay factor and in some cases successfully recovers the accuracy of the non-delayed counterpart. We conduct various ablations and sensitivity experiments, demonstrating the effectiveness of our approach.

SeaLLMs -- Large Language Models for Southeast Asia

Despite the remarkable achievements of large language models (LLMs) in various tasks, there remains a linguistic bias that favors high-resource languages, such as English, often at the expense of low-resource and regional languages. To address this imbalance, we introduce SeaLLMs, an innovative series of language models that specifically focuses on Southeast Asian (SEA) languages. SeaLLMs are built upon the Llama-2 model and further advanced through continued pre-training with an extended vocabulary, specialized instruction and alignment tuning to better capture the intricacies of regional languages. This allows them to respect and reflect local cultural norms, customs, stylistic preferences, and legal considerations. Our comprehensive evaluation demonstrates that SeaLLM-13b models exhibit superior performance across a wide spectrum of linguistic tasks and assistant-style instruction-following capabilities relative to comparable open-source models. Moreover, they outperform ChatGPT-3.5 in non-Latin languages, such as Thai, Khmer, Lao, and Burmese, by large margins while remaining lightweight and cost-effective to operate.

Bridging the Information Gap Between Domain-Specific Model and General LLM for Personalized Recommendation

Generative large language models(LLMs) are proficient in solving general problems but often struggle to handle domain-specific tasks. This is because most of domain-specific tasks, such as personalized recommendation, rely on task-related information for optimal performance. Current methods attempt to supplement task-related information to LLMs by designing appropriate prompts or employing supervised fine-tuning techniques. Nevertheless, these methods encounter the certain issue that information such as community behavior pattern in RS domain is challenging to express in natural language, which limits the capability of LLMs to surpass state-of-the-art domain-specific models. On the other hand, domain-specific models for personalized recommendation which mainly rely on user interactions are susceptible to data sparsity due to their limited common knowledge capabilities. To address these issues, we proposes a method to bridge the information gap between the domain-specific models and the general large language models. Specifically, we propose an information sharing module which serves as an information storage mechanism and also acts as a bridge for collaborative training between the LLMs and domain-specific models. By doing so, we can improve the performance of LLM-based recommendation with the help of user behavior pattern information mined by domain-specific models. On the other hand, the recommendation performance of domain-specific models can also be improved with the help of common knowledge learned by LLMs. Experimental results on three real-world datasets have demonstrated the effectiveness of the proposed method.

Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents

Proactive dialogues serve as a practical yet challenging dialogue problem in the era of large language models (LLMs), where the dialogue policy planning is the key to improving the proactivity of LLMs. Most existing studies enable the dialogue policy planning of LLMs using various prompting schemes or iteratively enhance this capability in handling the given case with verbal AI feedback. However, these approaches are either bounded by the policy planning capability of the frozen LLMs or hard to be transferred to new cases. In this work, we introduce a new dialogue policy planning paradigm to strategize LLMs for proactive dialogue problems with a tunable language model plug-in as a plug-and-play dialogue policy planner, named PPDPP. Specifically, we develop a novel training framework to facilitate supervised fine-tuning over available human-annotated data as well as reinforcement learning from goal-oriented AI feedback with dynamic interaction data collected by the LLM-based self-play simulation. In this manner, the LLM-powered dialogue agent can not only be generalized to different cases after the training, but also be applicable to different applications by just substituting the learned plug-in. In addition, we propose to evaluate the policy planning capability of dialogue systems under the interactive setting. Experimental results demonstrate that PPDPP consistently and substantially outperforms existing approaches on three different proactive dialogue applications, including negotiation, emotional support, and tutoring dialogues.

SOUL: Towards Sentiment and Opinion Understanding of Language

Sentiment analysis is a well-established natural language processing task, with sentiment polarity classification being one of its most popular and representative tasks. However, despite the success of pre-trained language models in this area, they often fall short of capturing the broader complexities of sentiment analysis. To address this issue, we propose a new task called Sentiment and Opinion Understanding of Language (SOUL). SOUL aims to evaluate sentiment understanding through two subtasks: Review Comprehension (RC) and Justification Generation (JG). RC seeks to validate statements that focus on subjective information based on a review text, while JG requires models to provide explanations for their sentiment predictions. To enable comprehensive evaluation, we annotate a new dataset comprising 15,028 statements from 3,638 reviews. Experimental results indicate that SOUL is a challenging task for both small and large language models, with a performance gap of up to 27% when compared to human performance. Furthermore, evaluations conducted with both human experts and GPT-4 highlight the limitations of the small language model in generating reasoning-based justifications. These findings underscore the challenging nature of the SOUL task for existing models, emphasizing the need for further advancements in sentiment analysis to address its complexities. The new dataset and code are available at https://github.com/DAMO-NLP-SG/SOUL.

Multilingual Jailbreak Challenges in Large Language Models

While large language models (LLMs) exhibit remarkable capabilities across a wide range of tasks, they pose potential safety concerns, such as the ``jailbreak'' problem, wherein malicious instructions can manipulate LLMs to exhibit undesirable behavior. Although several preventive measures have been developed to mitigate the potential risks associated with LLMs, they have primarily focused on English data. In this study, we reveal the presence of multilingual jailbreak challenges within LLMs and consider two potential risk scenarios: unintentional and intentional. The unintentional scenario involves users querying LLMs using non-English prompts and inadvertently bypassing the safety mechanisms, while the intentional scenario concerns malicious users combining malicious instructions with multilingual prompts to deliberately attack LLMs. The experimental results reveal that in the unintentional scenario, the rate of unsafe content increases as the availability of languages decreases. Specifically, low-resource languages exhibit three times the likelihood of encountering harmful content compared to high-resource languages, with both ChatGPT and GPT-4. In the intentional scenario, multilingual prompts can exacerbate the negative impact of malicious instructions, with astonishingly high rates of unsafe output: 80.92\% for ChatGPT and 40.71\% for GPT-4. To handle such a challenge in the multilingual context, we propose a novel \textsc{Self-Defense} framework that automatically generates multilingual training data for safety fine-tuning. Experimental results show that ChatGPT fine-tuned with such data can achieve a substantial reduction in unsafe content generation. Data is available at https://github.com/DAMO-NLP-SG/multilingual-safety-for-LLMs. Warning: This paper contains examples with potentially harmful content.

JsonTuning: Towards Generalizable, Robust, and Controllable Instruction Tuning

Instruction tuning has emerged as a crucial process for harnessing the capabilities of large language models (LLMs) by providing explicit task instructions, leading to improved performance in various tasks. However, prevalent text-to-text instruction tuning (TextTuning) methods suffer from limitations in generalization, robustness, and controllability due to the ambiguity and lack of explicit structure in tasks. In this paper, we propose JsonTuning, a novel structure-to-structure approach for instruction tuning. By leveraging the versatility and structured nature of JSON to represent tasks, JsonTuning enhances generalization by helping the model understand essential task elements and their relations, improves robustness by minimizing ambiguity, and increases controllability by providing explicit control over the output. We conduct a comprehensive comparative study with diverse language models and evaluation benchmarks. Experimental results show that JsonTuning outperforms TextTuning in various applications, showcasing improved performance, adaptability, robustness, and controllability. By overcoming the limitations of TextTuning, JsonTuning demonstrates significant potential for more effective and reliable LLMs capable of handling diverse scenarios.

Social Media Fashion Knowledge Extraction as Captioning

Social media plays a significant role in boosting the fashion industry, where a massive amount of fashion-related posts are generated every day. In order to obtain the rich fashion information from the posts, we study the task of social media fashion knowledge extraction. Fashion knowledge, which typically consists of the occasion, person attributes, and fashion item information, can be effectively represented as a set of tuples. Most previous studies on fashion knowledge extraction are based on the fashion product images without considering the rich text information in social media posts. Existing work on fashion knowledge extraction in social media is classification-based and requires to manually determine a set of fashion knowledge categories in advance. In our work, we propose to cast the task as a captioning problem to capture the interplay of the multimodal post information. Specifically, we transform the fashion knowledge tuples into a natural language caption with a sentence transformation method. Our framework then aims to generate the sentence-based fashion knowledge directly from the social media post. Inspired by the big success of pre-trained models, we build our model based on a multimodal pre-trained generative model and design several auxiliary tasks for enhancing the knowledge extraction. Since there is no existing dataset which can be directly borrowed to our task, we introduce a dataset consisting of social media posts with manual fashion knowledge annotation. Extensive experiments are conducted to demonstrate the effectiveness of our model.

ARFA: An Asymmetric Receptive Field Autoencoder Model for Spatiotemporal Prediction

Spatiotemporal prediction aims to generate future sequences by paradigms learned from historical contexts. It holds significant importance in numerous domains, including traffic flow prediction and weather forecasting. However, existing methods face challenges in handling spatiotemporal correlations, as they commonly adopt encoder and decoder architectures with identical receptive fields, which adversely affects prediction accuracy. This paper proposes an Asymmetric Receptive Field Autoencoder (ARFA) model to address this issue. Specifically, we design corresponding sizes of receptive field modules tailored to the distinct functionalities of the encoder and decoder. In the encoder, we introduce a large kernel module for global spatiotemporal feature extraction. In the decoder, we develop a small kernel module for local spatiotemporal information reconstruction. To address the scarcity of meteorological prediction data, we constructed the RainBench, a large-scale radar echo dataset specific to the unique precipitation characteristics of inland regions in China for precipitation prediction. Experimental results demonstrate that ARFA achieves consistent state-of-the-art performance on two mainstream spatiotemporal prediction datasets and our RainBench dataset, affirming the effectiveness of our approach. This work not only explores a novel method from the perspective of receptive fields but also provides data support for precipitation prediction, thereby advancing future research in spatiotemporal prediction.

Overcoming General Knowledge Loss with Selective Parameter Finetuning

Foundation models encompass an extensive knowledge base and offer remarkable transferability. However, this knowledge becomes outdated or insufficient over time. The challenge lies in updating foundation models to accommodate novel information while retaining their original ability. In this paper, we present a novel approach to achieving continual model updates by effecting localized modifications to a small subset of parameters. Guided by insights gleaned from prior analyses of foundational models, we first localize a specific layer for model refinement and then introduce an importance scoring mechanism designed to update only the most crucial weights. Our method is exhaustively evaluated on foundational vision-language models, measuring its efficacy in both learning new information and preserving pre-established knowledge across a diverse spectrum of continual learning tasks, including Aircraft, Birdsnap CIFAR-100, CUB, Cars, and GTSRB. The results show that our method improves the existing continual learning methods by 0.5\% - 10\% on average, and reduces the loss of pre-trained knowledge from around 5\% to 0.97\%. Comprehensive ablation studies substantiate our method design, shedding light on the contributions of each component to controllably learning new knowledge and mitigating the forgetting of pre-trained knowledge.