# Is Little Women AI-generated? 3 of 7 AI detectors said yes

Source: https://toolbenchmarks.com/famous-texts/little-women/  
Tested: 30 October 2026. Publisher: Tool Benchmarks. Data licence: CC BY 4.0.

> No, Louisa May Alcott published Little Women in 1868. Three of the 7 AI detectors we tested still flagged its opening, Fakespot with a 96.8% AI probability, TMR with 98.0%, and Fast-DetectGPT. Desklib, which had the fewest false positives among detectors that catch a real share of AI text, gave it 1.5%.

Little Women (opening), Louisa May Alcott, 1868. Flagged as AI by 3 of 7 detectors.

| Detector | Verdict |
|---|---|
| Fast-DetectGPT (Qwen2.5-1.5B) | AI-generated |
| Desklib AI text detector | Human |
| Fakespot AI text detector | AI-generated |
| MAGE | Human |
| E5-small LoRA detector | Human |
| OpenAI RoBERTa detector | Human |
| TMR (Target Mining RoBERTa) | AI-generated |

Passage tested: "Christmas won’t be Christmas without any presents,” grumbled Jo, lying on the rug. “It’s so dreadful to be poor!” sighed Meg, looking down at her old dress. “I don’t think it’s fair for some girls to have plenty of pretty things, and other girls nothing at all,” added little Amy, with an injured sniff. “We’ve got Father and Mother, and..."

## Who flagged the March sisters

Fakespot gave the passage a 96.8% AI probability and TMR 98.0%. Fast-DetectGPT also called it AI. Desklib (1.5%), the E5-small LoRA detector (38.7%), the OpenAI RoBERTa detector (0.0%) and MAGE all passed it.

Two of those flags are unusual for the detector involved. Fast-DetectGPT flagged no human fiction at all in our main test, and Fakespot flagged 12.5% of it, so neither normally treats novels this way. TMR's flag is routine: it flagged every human fiction passage we gave it, which mostly reflects its reaction to [paragraph breaks](/guides/paragraph-breaks-change-ai-detector-results/).

## A famous first line and a tidy moral

The opening line, "Christmas won't be Christmas without any presents," is among the best-known first lines in American literature. A language model that has met it in quotations and adaptations many times will likely find the scene easy to predict, and predictability is what Fast-DetectGPT measures. That is the likeliest reason for its flag.

Fakespot's flag is harder to explain from outside, but the passage has a few features that may resemble AI fiction. The dialogue tags follow one pattern over and over: "grumbled Jo", "sighed Meg", "said Beth contentedly", "said Amy decidedly". The talk itself is earnest and moral, with Meg explaining that they ought to make "our little sacrifices" gladly. Gentle scenes with a tidy lesson may be close to the default tone of chatbot stories, and a classifier could have learned to associate that tone with machine text.

## Checking a story that draws one flag

Desklib passed this passage easily. Still, a warm, dialogue-heavy scene with a clear moral can trip Fakespot, and a familiar or widely quoted line can trip Fast-DetectGPT. If you check a story and get one flag from either, see what Desklib says, and treat none of these verdicts as proof. Another Christmas opening, [A Christmas Carol](/famous-texts/a-christmas-carol/), was also flagged by Fast-DetectGPT, and [Jane Eyre](/famous-texts/jane-eyre/) drew a similar Fakespot score. All 30 texts are on the [famous texts page](/famous-texts/).

## Questions people ask

### Why did Fast-DetectGPT flag Little Women?

Probably because the passage is so familiar. Fast-DetectGPT flags text a language model finds predictable, and the opening line of Little Women is one of the most quoted in American fiction, so the model has likely seen it many times.

### Do AI detectors usually flag fiction?

Less than other genres. In our main test Desklib and Fast-DetectGPT flagged none of the 24 human fiction passages, and Fakespot flagged 3 of 24. TMR was the exception, flagging all of them.