1.5k citations · 5.2k across the 138 of their papers we have counts for
18 papers · 1 filter
On the Opportunities and Challenges of Offline Reinforcement Learning for Recommender Systems
Xiaocong Chen, Siyu Wang, Julian McAuley +2
Reinforcement learning serves as a potent tool for modeling dynamic user interests within recommender systems, garnering increasing research attention of late. However, a significa…
Large Language Models as Zero-Shot Conversational Recommenders
Zhankui He, Zhouhang Xie, Rahul Jha +6
In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contribution…
Learning Concise and Descriptive Attributes for Visual Recognition
An Yan, Yu Wang, Yiwu Zhong +6
Recent advances in foundation models present new opportunities for interpretable visual recognition -- one can first query Large Language Models (LLMs) to obtain a set of attribute…
Reinforcement Learning for Generative AI: A Survey
Yuanjiang Cao, Quan Z. Sheng, Julian McAuley +1
Deep Generative AI has been a long-standing essential topic in the machine learning community, which can impact a number of application areas like text generation and computer visi…
Comparing Apples to Apples: Generating Aspect-Aware Comparative Sentences from User Reviews
Jessica Echterhoff, An Yan, Julian McAuley
It is time-consuming to find the best product among many similar alternatives. Comparative sentences can help to contrast one item from others in a way that highlights important fe…
CLIPSonic: Text-to-Audio Synthesis with Unlabeled Videos and Pretrained Language-Vision Models
Hao-Wen Dong, Xiaoyu Liu, Jordi Pons +5
Recent work has studied text-to-audio synthesis using large amounts of paired text-audio data. However, audio recordings with high-quality text annotations can be difficult to acqu…
LongCoder: A Long-Range Pre-trained Language Model for Code Completion
Daya Guo, Canwen Xu, Nan Duan +2
In this paper, we introduce a new task for code completion that focuses on handling long code input and propose a sparse Transformer model, called LongCoder, to address this task.…
Generative Flow Network for Listwise Recommendation
Shuchang Liu, Qingpeng Cai, Zhankui He +5
Personalized recommender systems fulfill the daily demands of customers and boost online businesses. The goal is to learn a policy that can generate a list of items that matches th…
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations
Myeongjun Jang, Bodhisattwa Prasad Majumder, Julian McAuley +2
While recent works have been considerably improving the quality of the natural language explanations (NLEs) generated by a model to justify its predictions, there is very limited r…
RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems
Tianyang Liu, Canwen Xu, Julian McAuley
Large Language Models (LLMs) have greatly advanced code auto-completion systems, with a potential for substantial productivity enhancements for developers. However, current benchma…
Text Is All You Need: Learning Language Representations for Sequential Recommendation
Jiacheng Li, Ming Wang, Jin Li +4
Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequen…
Small Models are Valuable Plug-ins for Large Language Models
Canwen Xu, Yichong Xu, Shuohang Wang +3
Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are often publicly unavailable and their immense sizes make the models difficult to be tuned wit…
"Nothing Abnormal": Disambiguating Medical Reports via Contrastive Knowledge Infusion
Zexue He, An Yan, Amilcare Gentili +2
Sharing medical reports is essential for patient-centered care. A recent line of work has focused on automatically generating reports with NLP methods. However, different audiences…
Large Language Models are Zero-Shot Rankers for Recommender Systems
Yupeng Hou, Junjie Zhang, Zihan Lin +4
Recently, large language models (LLMs) (e.g., GPT-4) have demonstrated impressive general-purpose task-solving abilities, including the potential to approach recommendation tasks.…
Unsupervised Improvement of Factual Knowledge in Language Models
Nafis Sadeq, Byungkyu Kang, Prarit Lamba +1
Masked language modeling (MLM) plays a key role in pretraining large language models. But the MLM objective is often dominated by high-frequency words that are sub-optimal for lear…
Mirror: A Natural Language Interface for Data Querying, Summarization, and Visualization
Canwen Xu, Julian McAuley, Penghan Wang
We present Mirror, an open-source platform for data exploration and analysis powered by large language models. Mirror offers an intuitive natural language interface for querying da…
CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets
Zachary Novack, Julian McAuley, Zachary C. Lipton +1
Open vocabulary models (e.g. CLIP) have shown strong performance on zero-shot classification through their ability generate embeddings for each class based on their (natural langua…
Data Distillation: A Survey
Noveen Sachdeva, Julian McAuley
The popularity of deep learning has led to the curation of a vast number of massive and multifarious datasets. Despite having close-to-human performance on individual tasks, traini…