83 citations · 185 across the 24 of their papers we have counts for
25 papers
QACHECK: A Demonstration System for Question-Guided Multi-Hop Fact-Checking
Liangming Pan, Xinyuan Lu, Min-Yen Kan +1
Fact-checking real-world claims often requires complex, multi-step reasoning due to the absence of direct evidence to support or refute them. However, existing fact-checking system…
FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation
Yan Meng, Liangming Pan, Yixin Cao +1
Humans ask follow-up questions driven by curiosity, which reflects a creative human cognitive process. We introduce the task of real-world information-seeking follow-up question ge…
Investigating Zero- and Few-shot Generalization in Fact Verification
Liangming Pan, Yunxiang Zhang, Min-Yen Kan
In this paper, we explore zero- and few-shot generalization for fact verification (FV), which aims to generalize the FV model trained on well-resourced domains (e.g., Wikipedia) to…
Automatically Correcting Large Language Models: Surveying the landscape of diverse self-correction strategies
Liangming Pan, Michael Saxon, Wenda Xu +3
Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks. However, their efficacy is undermined by undesired and inconsistent behavior…
Fact-Checking Complex Claims with Program-Guided Reasoning
Liangming Pan, Xiaobao Wu, Xinyuan Lu +4
Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning. In this paper, we present Program-Guided Fact-Check…
Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation
Xuan Long Do, Bowei Zou, Shafiq Joty +4
Conversational Question Generation (CQG) is a critical task for machines to assist humans in fulfilling their information needs through conversations. The task is generally cast in…
Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models
Alfonso Amayuelas, Kyle Wong, Liangming Pan +2
This paper investigates the capabilities of Large Language Models (LLMs) in the context of understanding their knowledge and uncertainty over questions. Specifically, we focus on a…
On the Risk of Misinformation Pollution with Large Language Models
Yikang Pan, Liangming Pan, Wenhu Chen +3
In this paper, we comprehensively investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation and its subsequent impact…
SCITAB: A Challenging Benchmark for Compositional Reasoning and Claim Verification on Scientific Tables
Xinyuan Lu, Liangming Pan, Qian Liu +2
Current scientific fact-checking benchmarks exhibit several shortcomings, such as biases arising from crowd-sourced claims and an over-reliance on text-based evidence. We present S…
Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning
Liangming Pan, Alon Albalak, Xinyi Wang +1
Large Language Models (LLMs) have shown human-like reasoning abilities but still struggle with complex logical problems. This paper introduces a novel framework, Logic-LM, which in…
Hashtag-Guided Low-Resource Tweet Classification
Shizhe Diao, Sedrick Scott Keh, Liangming Pan +3
Social media classification tasks (e.g., tweet sentiment analysis, tweet stance detection) are challenging because social media posts are typically short, informal, and ambiguous.…
CoHS-CQG: Context and History Selection for Conversational Question Generation
Xuan Long Do, Bowei Zou, Liangming Pan +3
Conversational question generation (CQG) serves as a vital task for machines to assist humans, such as interactive reading comprehension, through conversations. Compared to traditi…
Modeling and Leveraging Prerequisite Context in Recommendation
Hengchang Hu, Liangming Pan, Yiding Ran +1
Prerequisites can play a crucial role in users' decision-making yet recommendation systems have not fully utilized such contextual background knowledge. Traditional recommendation…
Attacking Open-domain Question Answering by Injecting Misinformation
Liangming Pan, Wenhu Chen, Min-Yen Kan +1
With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing a…
Interpreting the Robustness of Neural NLP Models to Textual Perturbations
Yunxiang Zhang, Liangming Pan, Samson Tan +1
Modern Natural Language Processing (NLP) models are known to be sensitive to input perturbations and their performance can decrease when applied to real-world, noisy data. However,…
Zero-shot Fact Verification by Claim Generation
Liangming Pan, Wenhu Chen, Wenhan Xiong +2
Neural models for automated fact verification have achieved promising results thanks to the availability of large, human-annotated datasets. However, for each new domain that requi…
Exploring Question-Specific Rewards for Generating Deep Questions
Yuxi Xie, Liangming Pan, Dongzhe Wang +2
Recent question generation (QG) approaches often utilize the sequence-to-sequence framework (Seq2Seq) to optimize the log-likelihood of ground-truth questions using teacher forcing…
Unsupervised Multi-hop Question Answering by Question Generation
Liangming Pan, Wenhu Chen, Wenhan Xiong +2
Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. We explore the possibility to train a well-performed multi-hop QA model with…
Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment
Zhiyuan Liu, Yixin Cao, Liangming Pan +2
Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising pe…
Multi-modal Cooking Workflow Construction for Food Recipes
Liangming Pan, Jingjing Chen, Jianlong Wu +5
Understanding food recipe requires anticipating the implicit causal effects of cooking actions, such that the recipe can be converted into a graph describing the temporal workflow…
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge Base
Shulin Cao, Jiaxin Shi, Liangming Pan +6
Complex question answering over knowledge base (Complex KBQA) is challenging because it requires various compositional reasoning capabilities, such as multi-hop inference, attribut…
Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen
Yixin Cao, Ruihao Shui, Liangming Pan +3
The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the g…
Semantic Graphs for Generating Deep Questions
Liangming Pan, Yuxi Xie, Yansong Feng +2
This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information of the input…
Recent Advances in Neural Question Generation
Liangming Pan, Wenqiang Lei, Tat-Seng Chua +1
Emerging research in Neural Question Generation (NQG) has started to integrate a larger variety of inputs, and generating questions requiring higher levels of cognition. These tren…