Retrieval-Augmented Controllable Review Generation
In this paper, we study review generation given a set of attribute identifiers which are user ID, product ID and rating. This is a difficult subtask of natural language generation since models are limited to the given identifiers, without any specific descriptive information regarding the inputs, when generating the text. The capacity of these models is thus confined and dependent to how well the models can capture vector representations of attributes. We thus propose to additionally leverage references, which are selected from a large pool of texts labeled with one of the attributes, as textual information that enriches inductive biases of given attributes. With these references, we can now pose the problem as an instance of text-to-text generation, which makes the task easier since texts that are syntactically, semantically similar with the output text are provided as input. Using this framework, we address issues such as selecting references from a large candidate set without textual context and improving the model complexity for generation. Our experiments show that our models improve over previous approaches on both automatic and human evaluation metrics.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeDescriptiveRetrievalReview GenerationText GenerationSimilar Papers 제목 키워드 기반
A Survey on Retrieval-Augmented Text Generation
Recently, retrieval-augmented text generation attracted increasing attention of the computational linguistics community. Compared with conventional generation models, retrieval-augmented text generation has remarkable ad…
Machine TranslationResponse GenerationRetrievalRetrieval-augmented Generation+3RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios
Simulation plays a crucial role in the development of autonomous vehicles (AVs) due to the potential risks associated with real-world testing. Although significant progress has been made in the visual aspects of simulato…
Autonomous VehiclesIn-Context LearningRetrievalRetrieval-augmented GenerationRetrieval-Augmented Layout Transformer for Content-Aware Layout Generation
Content-aware graphic layout generation aims to automatically arrange visual elements along with a given content, such as an e-commerce product image. In this paper, we argue that the current layout generation approaches…
Layout GenerationRetrievalAutomated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation
This research presents and compares multiple approaches to automate the generation of literature reviews using several Natural Language Processing (NLP) techniques and retrieval-augmented generation (RAG) with a Large La…
ArticlesLanguage ModelingLanguage ModellingLarge Language Model+3Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
Despite their remarkable capabilities, large language models (LLMs) often produce responses containing factual inaccuracies due to their sole reliance on the parametric knowledge they encapsulate. Retrieval-Augmented Gen…
Fact VerificationQuestion AnsweringRAGResponse Generation+2