Retrieval-augmented generation for large language models: A survey
Y Gao, Y **ong, X Gao, K Jia, J Pan, Y Bi, Y Dai… - arxiv preprint arxiv …, 2023 - arxiv.org
Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …
challenges in practical applications, such as hallucinations, slow knowledge updates, and …
Recommender systems in the era of large language models (llms)
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an important component of our daily life, providing personalized suggestions …
have become an important component of our daily life, providing personalized suggestions …
A survey on large language models for recommendation
Abstract Large Language Models (LLMs) have emerged as powerful tools in the field of
Natural Language Processing (NLP) and have recently gained significant attention in the …
Natural Language Processing (NLP) and have recently gained significant attention in the …
Data-efficient Fine-tuning for LLM-based Recommendation
Leveraging Large Language Models (LLMs) for recommendation has recently garnered
considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the …
considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the …
Adapting large language models by integrating collaborative semantics for recommendation
Recently, large language models (LLMs) have shown great potential in recommender
systems, either improving existing recommendation models or serving as the backbone …
systems, either improving existing recommendation models or serving as the backbone …
Learning to tokenize for generative retrieval
As a new paradigm in information retrieval, generative retrieval directly generates a ranked
list of document identifiers (docids) for a given query using generative language models …
list of document identifiers (docids) for a given query using generative language models …
How can recommender systems benefit from large language models: A survey
With the rapid development of online services and web applications, recommender systems
(RS) have become increasingly indispensable for mitigating information overload and …
(RS) have become increasingly indispensable for mitigating information overload and …
A review of modern recommender systems using generative models (gen-recsys)
Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …
source. However, deep generative models now have the capability to model and sample …
Multimodal pretraining, adaptation, and generation for recommendation: A survey
Personalized recommendation serves as a ubiquitous channel for users to discover
information tailored to their interests. However, traditional recommendation models primarily …
information tailored to their interests. However, traditional recommendation models primarily …
Bridging items and language: A transition paradigm for large language model-based recommendation
Harnessing Large Language Models (LLMs) for recommendation is rapidly emerging, which
relies on two fundamental steps to bridge the recommendation item space and the language …
relies on two fundamental steps to bridge the recommendation item space and the language …