274 citations · 891 across the 147 of their papers we have counts for
26 papers · 2 filters
Rethinking Tabular Data Understanding with Large Language Models
Tianyang Liu, Fei Wang, Muhao Chen
Large Language Models (LLMs) have shown to be capable of various tasks, yet their capability in interpreting and reasoning over tabular data remains an underexplored area. In this…
Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking
Nan Xu, Fei Wang, Ben Zhou +3
While large language models (LLMs) have demonstrated increasing power, they have also given rise to a wide range of harmful behaviors. As representatives, jailbreak attacks can pro…
Test-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations
Wenjie Mo, Jiashu Xu, Qin Liu +5
Existing studies in backdoor defense have predominantly focused on the training phase, overlooking the critical aspect of testing time defense. This gap becomes pronounced in the c…
Deceptive Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination?
Bangzheng Li, Ben Zhou, Fei Wang +3
Despite the recent advancement in large language models (LLMs) and their high performances across numerous benchmarks, recent research has unveiled that LLMs suffer from hallucinat…
How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities
Lingbo Mo, Boshi Wang, Muhao Chen +1
The rapid progress in open-source Large Language Models (LLMs) is significantly driving AI development forward. However, there is still a limited understanding of their trustworthi…
Dense Retrieval as Indirect Supervision for Large-space Decision Making
Nan Xu, Fei Wang, Mingtao Dong +1
Many discriminative natural language understanding (NLU) tasks have large label spaces. Learning such a process of large-space decision making is particularly challenging due to th…
Affective and Dynamic Beam Search for Story Generation
Tenghao Huang, Ehsan Qasemi, Bangzheng Li +4
Storytelling's captivating potential makes it a fascinating research area, with implications for entertainment, education, therapy, and cognitive studies. In this paper, we propose…
GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding
Zekun Li, Wenxuan Zhou, Yao-Yi Chiang +1
Humans subconsciously engage in geospatial reasoning when reading articles. We recognize place names and their spatial relations in text and mentally associate them with their phys…
DOMINO: A Dual-System for Multi-step Visual Language Reasoning
Peifang Wang, Olga Golovneva, Armen Aghajanyan +4
Visual language reasoning requires a system to extract text or numbers from information-dense images like charts or plots and perform logical or arithmetic reasoning to arrive at a…
Primacy Effect of ChatGPT
Yiwei Wang, Yujun Cai, Muhao Chen +2
Instruction-tuned large language models (LLMs), such as ChatGPT, have led to promising zero-shot performance in discriminative natural language understanding (NLU) tasks. This invo…
AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models
Xiaogeng Liu, Nan Xu, Muhao Chen +1
The aligned Large Language Models (LLMs) are powerful language understanding and decision-making tools that are created through extensive alignment with human feedback. However, th…
Self-Augmentation Improves Zero-Shot Cross-Lingual Transfer
Fei Wang, Kuan-Hao Huang, Kai-Wei Chang +1
Zero-shot cross-lingual transfer is a central task in multilingual NLP, allowing models trained in languages with more sufficient training resources to generalize to other low-reso…
UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition
Wenxuan Zhou, Sheng Zhang, Yu Gu +2
Large language models (LLMs) have demonstrated remarkable generalizability, such as understanding arbitrary entities and relations. Instruction tuning has proven effective for dist…
Contrastive Bootstrapping for Label Refinement
Shudi Hou, Yu Xia, Muhao Chen +1
Traditional text classification typically categorizes texts into pre-defined coarse-grained classes, from which the produced models cannot handle the real-world scenario where fine…
Robust Natural Language Understanding with Residual Attention Debiasing
Fei Wang, James Y. Huang, Tianyi Yan +2
Natural language understanding (NLU) models often suffer from unintended dataset biases. Among bias mitigation methods, ensemble-based debiasing methods, especially product-of-expe…
Improving Factuality of Abstractive Summarization without Sacrificing Summary Quality
Tanay Dixit, Fei Wang, Muhao Chen
Improving factual consistency of abstractive summarization has been a widely studied topic. However, most of the prior works on training factuality-aware models have ignored the ne…
Preconditioned Visual Language Inference with Weak Supervision
Ehsan Qasemi, Amani R. Maina-Kilaas, Devadutta Dash +2
Humans can infer the affordance of objects by extracting related contextual preconditions for each scenario. For example, upon seeing an image of a broken cup, we can infer that th…
Take a Break in the Middle: Investigating Subgoals towards Hierarchical Script Generation
Xinze Li, Yixin Cao, Muhao Chen +1
Goal-oriented Script Generation is a new task of generating a list of steps that can fulfill the given goal. In this paper, we propose to extend the task from the perspective of co…
Getting Sick After Seeing a Doctor? Diagnosing and Mitigating Knowledge Conflicts in Event Temporal Reasoning
Tianqing Fang, Zhaowei Wang, Wenxuan Zhou +3
Event temporal reasoning aims at identifying the temporal relations between two or more events from narratives. However, knowledge conflicts arise when there is a mismatch between…
Adversarial Demonstration Attacks on Large Language Models
Jiongxiao Wang, Zichen Liu, Keun Hee Park +5
With the emergence of more powerful large language models (LLMs), such as ChatGPT and GPT-4, in-context learning (ICL) has gained significant prominence in leveraging these models…
From Shortcuts to Triggers: Backdoor Defense with Denoised PoE
Qin Liu, Fei Wang, Chaowei Xiao +1
Language models are often at risk of diverse backdoor attacks, especially data poisoning. Thus, it is important to investigate defense solutions for addressing them. Existing backd…
Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models
Jiashu Xu, Mingyu Derek Ma, Fei Wang +2
We investigate security concerns of the emergent instruction tuning paradigm, that models are trained on crowdsourced datasets with task instructions to achieve superior performanc…
A Causal View of Entity Bias in (Large) Language Models
Fei Wang, Wenjie Mo, Yiwei Wang +2
Entity bias widely affects pretrained (large) language models, causing them to rely on (biased) parametric knowledge to make unfaithful predictions. Although causality-inspired met…
Bridging Continuous and Discrete Spaces: Interpretable Sentence Representation Learning via Compositional Operations
James Y. Huang, Wenlin Yao, Kaiqiang Song +3
Traditional sentence embedding models encode sentences into vector representations to capture useful properties such as the semantic similarity between sentences. However, in addit…