VER: Learning Natural Language Representations for Verbalizing Entities and Relations

Entities and relationships between entities are vital in the real world. Essentially, we understand the world by understanding entities and relations. For instance, to understand a field, e.g., computer science, we need to understand the relevant concepts, e.g., machine learning, and the relationships between concepts, e.g., machine learning and artificial intelligence. To understand a person, we should first know who he/she is and how he/she is related to others. To understand entities and relations, humans may refer to natural language descriptions. For instance, when learning a new scientific term, people usually start by reading its definition in dictionaries or encyclopedias. To know the relationship between two entities, humans tend to create a sentence to connect them. In this paper, we propose VER: A Unified Model for Verbalizing Entities and Relations. Specifically, we attempt to build a system that takes any entity or entity set as input and generates a sentence to represent entities and relations, named ``natural language representation''. Extensive experiments demonstrate that our model can generate high-quality sentences describing entities and entity relationships and facilitate various tasks on entities and relations, including definition modeling, relation modeling, and generative commonsense reasoning

Citation: A Key to Building Responsible and Accountable Large Language Models

Large Language Models (LLMs) bring transformative benefits alongside unique challenges, including intellectual property (IP) and ethical concerns. This position paper explores a novel angle to mitigate these risks, drawing parallels between LLMs and established web systems. We identify "citation" - the acknowledgement or reference to a source or evidence - as a crucial yet missing component in LLMs. Incorporating citation could enhance content transparency and verifiability, thereby confronting the IP and ethical issues in the deployment of LLMs. We further propose that a comprehensive citation mechanism for LLMs should account for both non-parametric and parametric content. Despite the complexity of implementing such a citation mechanism, along with the potential pitfalls, we advocate for its development. Building on this foundation, we outline several research problems in this area, aiming to guide future explorations towards building more responsible and accountable LLMs

Transitional Justice in Taiwan: Changes and Challenges

Taiwan’s experience with transitional justice over the past three decades suggests that dealing with historical injustice is a dynamic and fluid process that is fundamentally shaped and constrained by the balance of power and socio-political reality in a particular transitional society. This Article provides a contextualized legal-political analysis of the evolution of Taiwan’s transitional justice regime, with special attention to its limits and challenges. Since Taiwan’s democratization began, the transitional justice project developed by the former authoritarian Chinese Nationalist Party (Kuomintang, KMT) has been rather disproportionately focused on restorative over retributive mechanisms, with the main emphasis placed on reparations and apology and little consideration of truth recovery and individual accountability. But since the Democratic Progressive Party began to control the government and legislature in 2016, its new transitional justice initiatives have introduced significant changes, including, among others, investigating the KMT’s “illicit party assets” and removing authoritarian symbols such as Chiang Kai-shek’s statues, eliciting various contentions and contestations along the way. In our view, Taiwan is now confronted with profound challenges in developing a holistic, thoughtful transitional justice regime: fierce partisan politics that could interrupt progress at any time, conflation of transitional justice and identity politics, pending legal complications and a general distrust of the judiciary, and limited public engagement in transitional justice issues. Whether Taiwan can continue to thrive depends on how it grapples with these challenges in pursuit of justice and reconciliation that will strengthen and sustain tomorrow’s democratic Taiwan

Descriptive Knowledge Graph in Biomedical Domain

We present a novel system that automatically extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates the efficient search for relational knowledge. Unlike previous search engines or exploration systems that retrieve unconnected passages, our system organizes descriptive sentences as a relational graph, enabling researchers to explore closely related biomedical entities (e.g., diseases treated by a chemical) or indirectly connected entities (e.g., potential drugs for treating a disease). Our system also uses ChatGPT and a fine-tuned relation synthesis model to generate concise and reliable descriptive sentences from retrieved information, reducing the need for extensive human reading effort. With our system, researchers can easily obtain both high-level knowledge and detailed references and interactively steer to the information of interest. We spotlight the application of our system in COVID-19 research, illustrating its utility in areas such as drug repurposing and literature curation.Comment: EMNLP 2023 Dem