42 citations · 219 across the 31 of their papers we have counts for
28 papers · 1 filter
Long-form factuality in large language models
Jerry Wei, Chengrun Yang, Xinying Song +9
Large language models (LLMs) often generate content that contains factual errors when responding to fact-seeking prompts on open-ended topics. To benchmark a model's long-form fact…
Let the Pretrained Language Models "Imagine" for Short Texts Topic Modeling
Pritom Saha Akash, Jie Huang, Kevin Chen-Chuan Chang
Topic models are one of the compelling methods for discovering latent semantics in a document collection. However, it assumes that a document has sufficient co-occurrence informati…
Text Fact Transfer
Nishant Balepur, Jie Huang, Kevin Chen-Chuan Chang
Text style transfer is a prominent task that aims to control the style of text without inherently changing its factual content. To cover more text modification applications, such a…
Ask To The Point: Open-Domain Entity-Centric Question Generation
Yuxiang Liu, Jie Huang, Kevin Chen-Chuan Chang
We introduce a new task called *entity-centric question generation* (ECQG), motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checkin…
Descriptive Knowledge Graph in Biomedical Domain
Kerui Zhu, Jie Huang, Kevin Chen-Chuan Chang
We present a novel system that automatically extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates the efficient search for relati…
Large Language Models Cannot Self-Correct Reasoning Yet
Jie Huang, Xinyun Chen, Swaroop Mishra +4
Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities across various applications. Nevertheless, concerns pe…
Situated Natural Language Explanations
Zining Zhu, Haoming Jiang, Jingfeng Yang +6
Natural language is among the most accessible tools for explaining decisions to humans, and large pretrained language models (PLMs) have demonstrated impressive abilities to genera…
RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models
Jie Huang, Wei Ping, Peng Xu +3
In this paper, we investigate the in-context learning ability of retrieval-augmented encoder-decoder language models. We first conduct a comprehensive analysis of existing models a…
Citation: A Key to Building Responsible and Accountable Large Language Models
Jie Huang, Kevin Chen-Chuan Chang
Large Language Models (LLMs) bring transformative benefits alongside unique challenges, including intellectual property (IP) and ethical concerns. This position paper explores a no…
Mastering the ABCDs of Complex Questions: Answer-Based Claim Decomposition for Fine-grained Self-Evaluation
Nishant Balepur, Jie Huang, Samraj Moorjani +2
When answering complex questions, large language models (LLMs) may produce answers that do not satisfy all criteria of the question. While existing self-evaluation techniques aim t…
CCGen: Explainable Complementary Concept Generation in E-Commerce
Jie Huang, Yifan Gao, Zheng Li +7
We propose and study Complementary Concept Generation (CCGen): given a concept of interest, e.g., "Digital Cameras", generating a list of complementary concepts, e.g., 1) Camera Le…
Quantifying Association Capabilities of Large Language Models and Its Implications on Privacy Leakage
Hanyin Shao, Jie Huang, Shen Zheng +1
The advancement of large language models (LLMs) brings notable improvements across various applications, while simultaneously raising concerns about potential private data exposure…
Expository Text Generation: Imitate, Retrieve, Paraphrase
Nishant Balepur, Jie Huang, Kevin Chen-Chuan Chang
Expository documents are vital resources for conveying complex information to readers. Despite their usefulness, writing expository text by hand is a challenging process that requi…
Why Does ChatGPT Fall Short in Providing Truthful Answers?
Shen Zheng, Jie Huang, Kevin Chen-Chuan Chang
Recent advancements in large language models, such as ChatGPT, have demonstrated significant potential to impact various aspects of human life. However, ChatGPT still faces challen…
Multi-step Jailbreaking Privacy Attacks on ChatGPT
Haoran Li, Dadi Guo, Wei Fan +4
With the rapid progress of large language models (LLMs), many downstream NLP tasks can be well solved given appropriate prompts. Though model developers and researchers work hard o…
DimonGen: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships
Chenzhengyi Liu, Jie Huang, Kerui Zhu +1
In this paper, we propose DimonGen, which aims to generate diverse sentences describing concept relationships in various everyday scenarios. To support this, we first create a benc…
Towards Reasoning in Large Language Models: A Survey
Jie Huang, Kevin Chen-Chuan Chang
Reasoning is a fundamental aspect of human intelligence that plays a crucial role in activities such as problem solving, decision making, and critical thinking. In recent years, la…
VER: Unifying Verbalizing Entities and Relations
Jie Huang, Kevin Chen-Chuan Chang
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…
MetaASSIST: Robust Dialogue State Tracking with Meta Learning
Fanghua Ye, Xi Wang, Jie Huang +3
Existing dialogue datasets contain lots of noise in their state annotations. Such noise can hurt model training and ultimately lead to poor generalization performance. A general fr…
Coordinated Topic Modeling
Pritom Saha Akash, Jie Huang, Kevin Chen-Chuan Chang
We propose a new problem called coordinated topic modeling that imitates human behavior while describing a text corpus. It considers a set of well-defined topics like the axes of a…
Can Language Models Be Specific? How?
Jie Huang, Kevin Chen-Chuan Chang, Jinjun Xiong +1
"He is a person", "Paris is located on the earth". Both statements are correct but meaningless - due to lack of specificity. In this paper, we propose to measure how specific the l…
Are Large Pre-Trained Language Models Leaking Your Personal Information?
Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang
Are Large Pre-Trained Language Models Leaking Your Personal Information? In this paper, we analyze whether Pre-Trained Language Models (PLMs) are prone to leaking personal informat…
DEER: Descriptive Knowledge Graph for Explaining Entity Relationships
Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang +2
We propose DEER (Descriptive Knowledge Graph for Explaining Entity Relationships) - an open and informative form of modeling entity relationships. In DEER, relationships between en…
Domain Representative Keywords Selection: A Probabilistic Approach
Pritom Saha Akash, Jie Huang, Kevin Chen-Chuan Chang +3
We propose a probabilistic approach to select a subset of a \textit{target domain representative keywords} from a candidate set, contrasting with a context domain. Such a task is c…