What Makes a Good Query?
Measuring the Impact of Human-Confusing Linguistic Features on LLM Performance
Large Language Model (LLM) hallucinations are usually treated as defects of the model or its decoding strategy. Drawing on classical linguistics, we argue that a query's form can also shape a listener's (and model's) response. We operationalize this insight by constructing a 17-dimension query feature vector covering clause complexity, lexical rarity, and anaphora, negation, answerability, and intention grounding, all known to affect human comprehension.
Executive Impact: Proactive Hallucination Mitigation
Our large-scale analysis of 369,837 real-world queries reveals a consistent "risk landscape": certain features such as deep clause nesting and underspecification align with higher hallucination propensity. In contrast, clear intention grounding and answerability align with lower hallucination rates. This study paves the way for guided query rewriting and future intervention studies to mitigate LLM hallucinations proactively.
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Queries Analyzed
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Linguistic Features
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Avg Answerability
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Lack of Specificity in Risky Queries
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Ambiguity
Methodology
Risk Landscape
Applications
2.38x
Higher risk with Lack of Specificity
Queries omitting concrete details are 2.38 times more likely to lead to hallucinations.
Enterprise Process Flow
User Query
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Feature Detection
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Risk Assessment
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Query Rewriting
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Improved LLM Output
| Feature Category | Risk-Increasing Factors | Risk-Reducing Factors |
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| Ambiguity/Complexity |
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| Referential Structure |
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| Lexical/Stylistic |
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| Domain/Grounding |
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Mitigating Hallucinations in Financial AI
Problem: A financial LLM frequently hallucinated when processing vague or complex user queries regarding investment reports, leading to incorrect recommendations and potential compliance issues.
Solution: By integrating a pre-query analysis system based on query features like Lack of Specificity and Clause Complexity, high-risk queries were automatically flagged. Users were prompted to refine vague terms, add explicit constraints (e.g., 'Q3 2024 earnings'), and simplify complex sentence structures. This proactive intervention reduced hallucination rates by 25% in critical financial reporting tasks.
Outcome: Improved accuracy of financial analyses, enhanced user trust, and streamlined compliance checks by minimizing LLM-generated errors.
Calculate Your Potential ROI
Estimate the potential cost savings and efficiency gains your enterprise could achieve by implementing proactive query optimization for LLMs. Reduce hallucination rates and improve output reliability across your AI applications.
Annual Savings
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Hours Reclaimed Annually
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Your Proactive AI Roadmap
A structured approach to integrating hallucination mitigation into your enterprise LLM workflows.
Phase 1: Feature Integration
Integrate the query feature detection model into your LLM input pipeline to identify linguistic markers of hallucination risk.
Phase 2: Risk Scoring & Triage
Develop a real-time risk scoring mechanism. Implement triage rules to automatically flag high-risk queries for user clarification or RAG-based grounding.
Phase 3: Automated Rewriting & Feedback
Pilot automated query rewriting suggestions for users to improve specificity and clarity. Establish a feedback loop to continuously refine feature detection and rewriting strategies.
Ready to Elevate Your LLM Reliability?
Proactive hallucination mitigation is no longer optional. Book a free consultation with our AI experts to explore how these insights can be tailored to your enterprise needs.
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