QueryExplorer: Interactive Query Generation Assistant

核心概念

Facilitating effective search queries through interactive query generation and reformulation.

要約

Abstract:

Formulating effective search queries remains challenging, especially for non-experts.
QueryExplorer proposes an interactive query generation tool to support Human-In-The-Loop experiments.

Introduction:

Retrieving documents in multiple languages is crucial but challenging.
Query-by-example (QBE) allows exploring document collections effectively.

Related Work:

Existing tools focus on ad-hoc search without query generation or feedback incorporation.
Recent advancements in transformer models enhance search functionalities.

QueryExplorer:

Offers a simple document search with multi-lingual support and automated query generation.
Supports rapid prototyping and extensive instrumentation for query experiments.

Conclusion:

QueryExplorer aids in qualitative evaluation of query generation and retrieval processes.

統計

Formulating effective search queries remains a challenging task, particularly when users lack expertise in a specific domain or are not proficient in the language of the content. Providing example documents of interest might be easier for a user. However, such query-by-example scenarios are prone to concept drift, and the retrieval effectiveness is highly sensitive to the query generation method, without a clear way to incorporate user feedback.

引用

"Extensive prior work has shown that automatically generated queries can be improved with the searcher’s inputs."
"A tool that can facilitate generating queries with a Human-In-The-Loop (HITL) setting can result in even more effective search."

抽出されたキーインサイト

QueryExplorer

by Kaustubh D. ... 場所 arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15667.pdf

深掘り質問

How can incorporating user feedback improve the effectiveness of automated query generation?

Incorporating user feedback in automated query generation can significantly enhance its effectiveness by refining and optimizing the generated queries based on real-world interactions. User feedback provides valuable insights into the relevance, context, and nuances that may not be captured through algorithms alone. By allowing users to interact with the system, provide annotations on document relevance, suggest modifications to queries, or select preferred search results, automated systems can adapt and learn from this input.
User feedback enables iterative improvements in query generation models by fine-tuning them according to user preferences and domain-specific requirements. This iterative process helps in creating more accurate and tailored queries that align better with user intent. Additionally, incorporating user feedback fosters a collaborative approach where human expertise complements machine-generated suggestions, leading to more refined and contextually appropriate search queries.

What are the potential biases or challenges associated with using larger language models for keyword generation?

While larger language models offer significant benefits in generating keywords for various tasks like query reformulation or expansion, they also come with potential biases and challenges:

Bias Amplification: Larger language models trained on vast amounts of data may inadvertently perpetuate biases present in the training data. This could lead to biased keyword suggestions that reflect societal prejudices or stereotypes.

Complexity: Larger language models are computationally intensive and require substantial resources for training and inference. Managing these complexities can be challenging for researchers working with limited computational resources.

Data Privacy Concerns: Using large language models raises concerns about data privacy as sensitive information might get exposed during model training or when generating keywords from private datasets.

Domain Adaptation: Language models trained on general text corpora may struggle to generate relevant keywords accurately within specific domains due to lack of domain-specific knowledge.

Ethical Considerations: Ensuring ethical usage of large language models is crucial as they have the potential to influence decision-making processes based on their outputs.

How does QueryExplorer contribute to advancing research on interactive query generation beyond existing tools?

QueryExplorer makes significant contributions towards advancing research on interactive query generation through several key features:

Interactive Interface: QueryExplorer offers an intuitive interface that allows users (searchers) to actively participate in the query formulation process through example documents, edits, reformulations, and relevance annotations.

2Extensive Logging: The tool records detailed interactions between users and the system including changes made during query formulation,reformulation,and retrieval.This logging feature facilitates qualitative analysis,user behavior studies,and dataset annotation.
3Support for Multiple Models: QueryExplorer integrates PyTerrier's retrieval pipelines,HuggingFace's generative LLMs,and IR-Datasets,enabling researchers flexibilityin experimenting across different frameworksand datasets.
4Configurability: Researcherscan customize settings,such as choiceof retriever,instructionfor prompt-basedgeneration,andfeedback mechanisms,to tailor experimentsbasedon specificresearch goalsor scenarios
5**Human-in-the-Loop Studies:By enablingusersto collaboratewithautomatedmodelsandincorporatefeedbackintothequerygenerationprocess.QueryExplorercanfacilitateHITLstudieswhichenhance theresultsofautomatedquerygenerationby leveraginghumanexpertiseandpreferences
Overall,the comprehensive capabilitiesofQueryExplorermakeitavaluabletoolforconductingqualitativeanalysis,gatheringuserannotations,studiyingsearcherbehavior,andperformingadvancedIRexperimentsbeyondwhatexistingtoolscurrentlyoffer