Should We Type or Talk to LLM Agents? A Comprehensive Study of Voice and Keyboard Input Perturbations
A new research paper introduces HIVE, a toolkit to test how voice and keyboard input distortions impact LLM performance. Voice transcription errors consistently reduce accuracy across tasks.
- Voice transcription errors consistently reduce LLM accuracy across all tested instruction types.
- HIVE is the first comprehensive framework to systematically evaluate input perturbations from voice and keyboard sources.
- AI-backed dictation tools restructure spoken language, introducing unique noise patterns that differ from conventional transcription errors.
- The study highlights a gap in current LLM robustness, particularly for voice-based interactions.
Researchers have built HIVE (Human Input-Variation Engine), a framework designed to evaluate how different human input methods affect the performance of large language models. The study focuses on two primary input channels: voice and keyboard, each introducing distinct types of noise. Voice input, when processed through conventional transcription systems, introduces disfluencies and errors, while AI-backed dictation tools restructure spoken language into written form. Keyboard input, on the other hand, introduces orthographic noise typical of typing errors, such as misspellings or autocorrect distortions.
Using HIVE, the team conducted a series of experiments to measure the robustness of LLMs to these perturbations. The results reveal that voice transcription errors consistently lower model accuracy across all tested instruction types. This finding suggests that current LLMs may be more sensitive to voice input distortions than previously understood, highlighting a potential area for improvement in model training and input processing pipelines.
The paper also outlines seven key findings from the study, emphasizing the need for models to better handle real-world input variations. The research underscores the importance of considering input modality as a critical factor in AI system design, particularly for applications where voice interaction is prevalent.
Developers should consider input noise robustness when designing voice-enabled AI systems.
Companies deploying voice AI must account for transcription errors to improve user experience.
Students studying AI robustness and human-computer interaction should review this research for insights into input variability.
- HIVE
- Human Input-Variation Engine, a framework for testing how input noise affects LLM performance.
- Orthographic noise
- Errors introduced by typing, such as misspellings or autocorrect distortions.
- Disfluency
- Interruptions or irregularities in spoken language, such as pauses or filler words.
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