Synthetic Query Generation using Large Language Models for Virtual Assistants

• URL: http://arxiv.org/abs/2406.06729v1
• Date: Mon, 10 Jun 2024 18:50:57 GMT
• Title: Synthetic Query Generation using Large Language Models for Virtual Assistants
• Authors: Sonal Sannigrahi, Thiago Fraga-Silva, Youssef Oualil, Christophe Van Gysel,
• Abstract summary: We explore the use of Large Language Models (LLMs) to generate synthetic queries that are complementary to template-based methods. We find that LLMs generate more verbose queries, compared to template-based methods, and reference aspects specific to the entity.
• Score: 7.446599238906526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Virtual Assistants (VAs) are important Information Retrieval platforms that help users accomplish various tasks through spoken commands. The speech recognition system (speech-to-text) uses query priors, trained solely on text, to distinguish between phonetically confusing alternatives. Hence, the generation of synthetic queries that are similar to existing VA usage can greatly improve upon the VA's abilities -- especially for use-cases that do not (yet) occur in paired audio/text data. In this paper, we provide a preliminary exploration of the use of Large Language Models (LLMs) to generate synthetic queries that are complementary to template-based methods. We investigate whether the methods (a) generate queries that are similar to randomly sampled, representative, and anonymized user queries from a popular VA, and (b) whether the generated queries are specific. We find that LLMs generate more verbose queries, compared to template-based methods, and reference aspects specific to the entity. The generated queries are similar to VA user queries, and are specific enough to retrieve the relevant entity. We conclude that queries generated by LLMs and templates are complementary.

Related papers

• Data Fusion of Synthetic Query Variants With Generative Large Language Models [1.864807003137943]
  This work explores the feasibility of using synthetic query variants generated by instruction-tuned Large Language Models in data fusion experiments. We introduce a lightweight, unsupervised, and cost-efficient approach that exploits principled prompting and data fusion techniques. Our analysis shows that data fusion based on synthetic query variants is significantly better than baselines with single queries and also outperforms pseudo-relevance feedback methods.
  arXiv  Detail & Related papers  (2024-11-06T12:54:27Z)
• Less is More: Making Smaller Language Models Competent Subgraph Retrievers for Multi-hop KGQA [51.3033125256716]
  We model the subgraph retrieval task as a conditional generation task handled by small language models. Our base generative subgraph retrieval model, consisting of only 220M parameters, competitive retrieval performance compared to state-of-the-art models. Our largest 3B model, when plugged with an LLM reader, sets new SOTA end-to-end performance on both the WebQSP and CWQ benchmarks.
  arXiv  Detail & Related papers  (2024-10-08T15:22:36Z)
• UQE: A Query Engine for Unstructured Databases [71.49289088592842]
  We investigate the potential of Large Language Models to enable unstructured data analytics. We propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections.
  arXiv  Detail & Related papers  (2024-06-23T06:58:55Z)
• Prompting-based Synthetic Data Generation for Few-Shot Question Answering [23.97949073816028]
  We show that using large language models can improve Question Answering performance on various datasets in the few-shot setting. We suggest that language models contain valuable task-agnostic knowledge that can be used beyond the common pre-training/fine-tuning scheme.
  arXiv  Detail & Related papers  (2024-05-15T13:36:43Z)
• Redefining Information Retrieval of Structured Database via Large Language Models [10.117751707641416]
  This paper introduces a novel retrieval augmentation framework called ChatLR. It primarily employs the powerful semantic understanding ability of Large Language Models (LLMs) as retrievers to achieve precise and concise information retrieval. Experimental results demonstrate the effectiveness of ChatLR in addressing user queries, achieving an overall information retrieval accuracy exceeding 98.8%.
  arXiv  Detail & Related papers  (2024-05-09T02:37:53Z)
• Generating Multi-Aspect Queries for Conversational Search [6.974395116689502]
  We show that the same retrieval model would perform better with more than one rewritten query by 85% in terms of nDCG@3. We propose a multi-aspect query generation and retrieval framework, called MQ4CS.
  arXiv  Detail & Related papers  (2024-03-28T10:40:22Z)
• An Interactive Query Generation Assistant using LLM-based Prompt Modification and User Feedback [9.461978375200102]
  The proposed interface is a novel search interface which supports automatic and interactive query generation over a mono-linguial or multi-lingual document collection. The interface enables the users to refine the queries generated by different LLMs, to provide feedback on the retrieved documents or passages, and is able to incorporate the users' feedback as prompts to generate more effective queries.
  arXiv  Detail & Related papers  (2023-11-19T04:42:24Z)
• Large Language Models are Strong Zero-Shot Retriever [89.16756291653371]
  We propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios. Our method, the Language language model as Retriever (LameR), is built upon no other neural models but an LLM.
  arXiv  Detail & Related papers  (2023-04-27T14:45:55Z)
• Generate rather than Retrieve: Large Language Models are Strong Context Generators [74.87021992611672]
  We present a novel perspective for solving knowledge-intensive tasks by replacing document retrievers with large language model generators. We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextutal documents based on a given question, and then reads the generated documents to produce the final answer.
  arXiv  Detail & Related papers  (2022-09-21T01:30:59Z)
• UnifieR: A Unified Retriever for Large-Scale Retrieval [84.61239936314597]
  Large-scale retrieval is to recall relevant documents from a huge collection given a query. Recent retrieval methods based on pre-trained language models (PLM) can be coarsely categorized into either dense-vector or lexicon-based paradigms. We propose a new learning framework, UnifieR which unifies dense-vector and lexicon-based retrieval in one model with a dual-representing capability.
  arXiv  Detail & Related papers  (2022-05-23T11:01:59Z)
• Text Summarization with Latent Queries [60.468323530248945]
  We introduce LaQSum, the first unified text summarization system that learns Latent Queries from documents for abstractive summarization with any existing query forms. Under a deep generative framework, our system jointly optimize a latent query model and a conditional language model, allowing users to plug-and-play queries of any type at test time. Our system robustly outperforms strong comparison systems across summarization benchmarks with different query types, document settings, and target domains.
  arXiv  Detail & Related papers  (2021-05-31T21:14:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.