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ARXIV:2605.30913 · LLM RELIABILITY · SUBMITTED 01 JUN · 20:32 UTC · FRESHNESS STALE
ARXIV:2605.30913LLM RELIABILITYSUBMITTED 01 JUN · 20:32 UTCFRESHNESS STALESoorya Ram Shimgekar · Agam Goyal · Amruta Parulekar · Joshua Chen · Yian Wang · Navin Kumar · +3 at arXiv
This research investigates how toxic language in prompts degrades LLM factual reliability and internal computation, finding that lexical toxicity significantly reduces accuracy and increases uncertainty.
Opportunity summary
Pain This research investigates how toxic language in prompts degrades LLM factual reliability and internal computation, finding that lexical toxicity significantly reduces accuracy and increases uncertainty.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
This research investigates how toxic language in prompts degrades LLM factual reliability and internal computation, finding that lexical toxicity significantly reduces accuracy and increases uncertainty. We study how lexical and tone-based prompt perturbations affect…
Large language models (LLMs) are increasingly deployed in conversational settings where user tone ranges from polite to adversarial or toxic, yet less is known about whether toxic language in otherwise semantically equivalent prompts can…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. These findings position prompt tone as a critical dimension of LLM reliability and provide behavioral and mechanistic evidence that surface-level lexical variation can alter…
LLM Reliability moved forward this cycle; last verified June 2026. Public score 3.0/10.
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This research investigates how toxic language in prompts degrades LLM factual reliability and internal computation, finding that lexical toxicity significantly reduces accuracy and increases uncertainty.
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10.48550/arXiv.2605.30913This research investigates how toxic language in prompts degrades LLM factual reliability and internal computation, finding that lexical toxicity significantly reduces accuracy and increases uncertainty.
Abstract
Large language models (LLMs) are increasingly deployed in conversational settings where user tone ranges from polite to adversarial or toxic, yet less is known about whether toxic language in otherwise semantically equivalent prompts can degrade factual reliability. We study how lexical and tone-based prompt perturbations affect the factual reliability of LLMs. Using controlled prompt variations across polite, random, and three toxicity levels, we evaluate five LLMs on ARC-Easy, GSM8K, and MMLU. We find that toxic lexical perturbations consistently reduce factual accuracy and increase uncertainty, while polite phrasing yields limited and inconsistent changes. To examine whether these answer inconsistencies correspond to internal changes, we conduct attribution-graph analyses of model activations and influences. We find that increasing toxicity selectively amplifies perturbation-sensitive variant nodes while relatively stable core reasoning nodes remain more invariant. These findings position prompt tone as a critical dimension of LLM reliability and provide behavioral and mechanistic evidence that surface-level lexical variation can alter factual outputs and internal computation.
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PROBLEM
This research investigates how toxic language in prompts degrades LLM factual reliability and internal computation, finding that lexical toxicity significantly reduces accuracy and increases uncertainty. We study how lexical and tone-based prompt perturbations affect the factual...
METHOD
Large language models (LLMs) are increasingly deployed in conversational settings where user tone ranges from polite to adversarial or toxic, yet less is known about whether toxic language in otherwise semantically equivalent prompts can degrade factual reliability. We study how...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. These findings position prompt tone as a critical dimension of LLM reliability and provide behavioral and mechanistic evidence that surface-level lexical variation can alter factual outputs and internal c...
WHY NOW
LLM Reliability moved forward this cycle; last verified June 2026. Public score 3.0/10.
{"file name": "input.pdf", "number of pages": 13, "author": "Soorya Ram Shimgekar; Agam Goyal; Amruta Parulekar; Joshua Chen; Yian Wang; Navin Kumar; Hari Sundaram; Eshwar Chandrasekharan; Koustuv Saha"
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This research investigates how toxic language in prompts degrades LLM factual reliability and internal computation, finding that lexical toxicity significantly reduces accuracy and increases uncertainty.
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LLM Reliability
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