274 citations · 891 across the 147 of their papers we have counts for
18 papers · 2 filters
FoodPuzzle: Developing Large Language Model Agents as Flavor Scientists
Tenghao Huang, Donghee Lee, John Sweeney +5
Flavor development in the food industry is increasingly challenged by the need for rapid innovation and precise flavor profile creation. Traditional flavor research methods typical…
SudoLM: Learning Access Control of Parametric Knowledge with Authorization Alignment
Qin Liu, Fei Wang, Chaowei Xiao +1
Existing preference alignment is a one-size-fits-all alignment mechanism, where the part of the large language model (LLM) parametric knowledge with non-preferred features is unifo…
Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation
Dongwon Jung, Qin Liu, Tenghao Huang +2
Retrieval-augmented generation (RAG) improves large language models (LMs) by incorporating non-parametric knowledge through evidence retrieved from external sources. However, it of…
Are Large Language Models Capable of Generating Human-Level Narratives?
Yufei Tian, Tenghao Huang, Miri Liu +5
This paper investigates the capability of LLMs in storytelling, focusing on narrative development and plot progression. We introduce a novel computational framework to analyze narr…
CLIMB: A Benchmark of Clinical Bias in Large Language Models
Yubo Zhang, Shudi Hou, Mingyu Derek Ma +3
Large language models (LLMs) are increasingly applied to clinical decision-making. However, their potential to exhibit bias poses significant risks to clinical equity. Currently, t…
Securing Multi-turn Conversational Language Models From Distributed Backdoor Triggers
Terry Tong, Jiashu Xu, Qin Liu +1
Large language models (LLMs) have acquired the ability to handle longer context lengths and understand nuances in text, expanding their dialogue capabilities beyond a single uttera…
FamiCom: Further Demystifying Prompts for Language Models with Task-Agnostic Performance Estimation
Bangzheng Li, Ben Zhou, Xingyu Fu +3
Language models have shown impressive in-context-learning capabilities, which allow them to benefit from input prompts and perform better on downstream end tasks. Existing works in…
Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing
Hadi Askari, Anshuman Chhabra, Muhao Chen +1
Large Language Models (LLMs) have achieved state-of-the-art performance at zero-shot generation of abstractive summaries for given articles. However, little is known about the robu…
Red Teaming Language Models for Processing Contradictory Dialogues
Xiaofei Wen, Bangzheng Li, Tenghao Huang +1
Most language models currently available are prone to self-contradiction during dialogues. To mitigate this issue, this study explores a novel contradictory dialogue processing tas…
Planning and Editing What You Retrieve for Enhanced Tool Learning
Tenghao Huang, Dongwon Jung, Muhao Chen
Recent advancements in integrating external tools with Large Language Models (LLMs) have opened new frontiers, with applications in mathematical reasoning, code generators, and sma…
Two Heads are Better than One: Nested PoE for Robust Defense Against Multi-Backdoors
Victoria Graf, Qin Liu, Muhao Chen
Data poisoning backdoor attacks can cause undesirable behaviors in large language models (LLMs), and defending against them is of increasing importance. Existing defense mechanisms…
Offset Unlearning for Large Language Models
James Y. Huang, Wenxuan Zhou, Fei Wang +4
Despite the strong capabilities of Large Language Models (LLMs) to acquire knowledge from their training corpora, the memorization of sensitive information in the corpora such as c…
X-Shot: A Unified System to Handle Frequent, Few-shot and Zero-shot Learning Simultaneously in Classification
Hanzi Xu, Muhao Chen, Lifu Huang +2
In recent years, few-shot and zero-shot learning, which learn to predict labels with limited annotated instances, have garnered significant attention. Traditional approaches often…
Monotonic Paraphrasing Improves Generalization of Language Model Prompting
Qin Liu, Fei Wang, Nan Xu +3
Performance of large language models (LLMs) may vary with different prompts or instructions of even the same task. One commonly recognized factor for this phenomenon is the model's…
Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation
Juan Manuel Zambrano Chaves, Shih-Cheng Huang, Yanbo Xu +24
The scaling laws and extraordinary performance of large foundation models motivate the development and utilization of such models in biomedicine. However, despite early promising r…
Privacy-Preserving Language Model Inference with Instance Obfuscation
Yixiang Yao, Fei Wang, Srivatsan Ravi +1
Language Models as a Service (LMaaS) offers convenient access for developers and researchers to perform inference using pre-trained language models. Nonetheless, the input data and…
Contrastive Instruction Tuning
Tianyi Lorena Yan, Fei Wang, James Y. Huang +5
Instruction tuning has been used as a promising approach to improve the performance of large language models (LLMs) on unseen tasks. However, current LLMs exhibit limited robustnes…
DeepEdit: Knowledge Editing as Decoding with Constraints
Yiwei Wang, Muhao Chen, Nanyun Peng +1
How to edit the knowledge in multi-step reasoning has become the major challenge in the knowledge editing (KE) of large language models (LLMs). The difficulty arises because the ha…