Pre-trained language models (PLMs) have been the de facto paradigm for most natural language processing tasks. This also benefits the biomedical domain: researchers from informatics, medicine, and computer science communities propose various PLMs trained on biomedical datasets, e.g., biomedical text, electronic health records, protein, and DNA sequences for various biomedical tasks. However, the cross-discipline characteristics of biomedical PLMs hinder their spreading among communities; some existing works are isolated from each other without comprehensive comparison and discussions. It is nontrivial to make a survey that not only systematically reviews recent advances in biomedical PLMs and their applications but also standardizes terminology and benchmarks. This article summarizes the recent progress of pre-trained language models in the biomedical domain and their applications in downstream biomedical tasks. Particularly, we discuss the motivations of PLMs in the biomedical domain and introduce the key concepts of pre-trained language models. We then propose a taxonomy of existing biomedical PLMs that categorizes them from various perspectives systematically. Plus, their applications in biomedical downstream tasks are exhaustively discussed, respectively. Last, we illustrate various limitations and future trends, which aims to provide inspiration for the future research.

Large language models (LLMs) have enabled remarkable advances in automated task-solving with multi-agent systems. However, most existing LLM-based multi-agent approaches rely on predefined agents to handle simple tasks, limiting the adaptability of multi-agent collaboration to different scenarios. Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks. Specifically, AutoAgents couples the relationship between tasks and roles by dynamically generating multiple required agents based on task content and planning solutions for the current task based on the generated expert agents. Multiple specialized agents collaborate with each other to efficiently accomplish tasks. Concurrently, an observer role is incorporated into the framework to reflect on the designated plans and agents' responses and improve upon them. Our experiments on various benchmarks demonstrate that AutoAgents generates more coherent and accurate solutions than the existing multi-agent methods. This underscores the significance of assigning different roles to different tasks and of team cooperation, offering new perspectives for tackling complex tasks. The repository of this project is available at https://github.com/Link-AGI/AutoAgents.

Reasoning, a crucial ability for complex problem-solving, plays a pivotal role in various real-world settings such as negotiation, medical diagnosis, and criminal investigation. It serves as a fundamental methodology in the field of Artificial General Intelligence (AGI). With the ongoing development of foundation models, there is a growing interest in exploring their abilities in reasoning tasks. In this paper, we introduce seminal foundation models proposed or adaptable for reasoning, highlighting the latest advancements in various reasoning tasks, methods, and benchmarks. We then delve into the potential future directions behind the emergence of reasoning abilities within foundation models. We also discuss the relevance of multimodal learning, autonomous agents, and super alignment in the context of reasoning. By discussing these future research directions, we hope to inspire researchers in their exploration of this field, stimulate further advancements in reasoning with foundation models, e.g. Large Language Models (LLMs), and contribute to the development of AGI.

Shuai Zhao, Jinming Wen, Anh Luu, Junbo Zhao, Jie Fu. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

Despite being widely applied, pre-trained language models have been proven vulnerable to backdoor attacks. Backdoor attacks are designed to introduce targeted vulnerabilities into models by poisoning a subset of training samples through trigger injection and label modification. Traditional textual backdoor attacks suffer several flaws: the triggers lead to abnormal natural language expressions, and poisoned sample labels are mistakenly labeled. These flaws reduce the stealthiness of the attack and can be easily detected by defense models. In this study, we introduce Cbat, a novel and efficient method to perform clean-label backdoor attack with text style, which does not require external trigger, and the poisoned samples are correctly labeled. Specifically, we develop a sentence rewriting model by leveraging the powerful few-shot learning capability of prompt tuning to generate clean label poisoned samples. Cbat then injects text style as an abstract trigger into the victim model through poisoned samples. We also introduce an algorithm for defending against backdoor attacks, named CbatD, which effectively erases the poisoned samples by locating the lowest training loss and calculating feature relevance. The experiments on text classification tasks demonstrate that our Cbat and CbatD show overall competitive performance in textual backdoor attack and defense. It is noteworthy that Cbat attained leading results in the clean-label backdoor attack benchmark without triggers.

AI alignment aims to make AI systems behave in line with human intentions and values. As AI systems grow more capable, so do risks from misalignment. To provide a comprehensive and up-to-date overview of the alignment field, in this survey, we delve into the core concepts, methodology, and practice of alignment. First, we identify four principles as the key objectives of AI alignment: Robustness, Interpretability, Controllability, and Ethicality ( RICE ). Guided by these four principles, we outline the landscape of current alignment research and decompose them into two key components: forward alignment and backward alignment . The former aims to make AI systems aligned via alignment training, while the latter aims to gain evidence about the systems’ alignment and govern them appropriately to avoid exacerbating misalignment risks. On forward alignment, we discuss techniques for learning from feedback and learning under the distribution shift. Specifically, we survey traditional preference modeling methods and reinforcement learning from human feedback and further discuss potential frameworks to reach scalable oversight for tasks where effective human oversight is hard to obtain. Within learning under distribution shift, we also cover data distribution interventions such as adversarial training that helps expand the distribution of training data and algorithmic interventions to combat goal misgeneralization. On backward alignment, we discuss assurance techniques and governance practices. Specifically, we survey assurance methods of AI systems throughout their lifecycle, covering safety evaluation, interpretability, and human value compliance. We discuss current and prospective governance practices adopted by governments, industry actors, and other third parties, aimed at managing existing and future AI risks. This survey aims to provide a comprehensive yet beginner-friendly review of alignment research topics. Based on this, we also release and continually update the website www.alignmentsurvey.com which features tutorials, collections of papers, blog posts, and other resources.