This article is based on the following paper: Bot or Human? Detecting ChatGPT imposters with a Single Question that presents a framework for detecting  LLM bots from humans. A very interesting paper that even if you're not interested in the topic presents some interesting details on LLMs.

Bot or Human? Detecting ChatGPT Imposters with A Single Question
Large language models like ChatGPT have recently demonstrated impressivecapabilities in natural language understanding and generation, enabling variousapplications including translation, essay writing, and chit-chatting. However,there is a concern that they can be misused for malicious purposes,…

Challenges in Differentiating Bots and Humans

The Evolution of Conversational Bots

Ever wondered how far technology has come? In recent years, large language models like ChatGPT have demonstrated remarkable capabilities in natural language understanding and generation. These models have enabled a plethora of applications, including translation, essay writing, and casual chit-chat. But as Uncle Ben once said, "With great power comes great responsibility." The concern lies in the potential misuse of these advanced language models for malicious purposes, such as fraud or denial-of-service attacks.

The Need for Efficient Detection Methods

How do we strike a balance between harnessing the power of these language models while safeguarding against potential harm? The answer lies in developing efficient methods to detect whether a participant in a conversation is a bot or a human. This is where FLAIR (Finding Large Language Model Authenticity via a Single Inquiry and Response) comes into the picture. This framework aims to address the problem of detecting conversational bots in an online setting, specifically targeting a single-question scenario that can effectively differentiate human users from bots.

FLAIR's Approach: Two Categories of Questions

Questions Easy for Humans, Difficult for Bots

Remember that time when you had to prove you were human by solving a captcha? FLAIR takes a similar approach, but with a twist. Instead of captcha images, it divides questions into two categories. The first category includes questions that are relatively easy for humans but difficult for bots. These questions involve tasks such as counting, substitution, positioning, noise filtering, and even ASCII art. By doing so, FLAIR exploits the weaknesses of large language models to discern the difference between genuine human responses and bot-generated answers.

Questions Easy for Bots, Difficult for Humans

Now, let's flip the script. The second category of questions comprises those that are easy for bots but difficult for humans, focusing on areas like memorization and computation. Think about it: have you ever tried to calculate the square root of 7,351 without a calculator? Not an easy task, right? However, bots excel at these types of questions, which enables FLAIR to identify them based on their unique strengths.

Questions Easy for Humans, Difficult for Bots

Despite the impressive capabilities of state-of-the-art Large Language Models (LLMs), they still struggle with certain tasks, such as counting, substitution, positioning, random editing, noise injection, and ASCII art interpretation, where humans excel. In this essay, we explore these limitations and their implications for differentiating between LLMs and humans in various contexts.

Counting: A Human Strength

A striking limitation of LLMs is their inability to accurately count characters in a string, a task humans can perform with ease. The example provided demonstrates that both GPT-3 and ChatGPT struggle to correctly count the number of times a given character appears in a string, while humans provide the correct answer effortlessly. This weakness has led researchers to develop counting-based tasks to differentiate humans and LLMs, providing an interesting insight into the limitations of these advanced models.

Substitution: Consistency Matters

LLMs often output content that is inconsistent with context, a shared weakness among these models. When asked to spell a random word under a given substitution rule, LLMs, such as GPT-3 and ChatGPT, fail to follow the rule consistently, whereas humans can apply it correctly. The example of substituting letters in the word "peach" highlights this limitation. This concept can be generalized to encryption schemes where a string is transformed based on specific rules.

Positioning: Locating Characters Accurately

The positioning task further investigates the LLMs' counting-related weaknesses. In this task, LLMs must output the k-th character in a string after the j-th appearance of a given character, c. Both GPT-3 and ChatGPT struggle to accurately locate the correct character, as shown in the example provided. This limitation is crucial to understanding the potential boundaries of LLMs' capabilities.

Random Editing: Robustness Against Noisy Inputs

Random editing is a technique used to evaluate the robustness of natural language processing models against noisy inputs. LLMs, such as GPT-3 and ChatGPT, are asked to perform random operations like dropping, inserting, swapping, or substituting characters in a string. In the example of randomly dropping two "1" characters from a given string, both GPT-3 and ChatGPT fail to provide correct outputs, while humans can solve the problem with ease. This highlights the challenges LLMs face when dealing with noisy or altered inputs.

Noise Injection: Confusing LLMs with Uppercase Letters

Noise injection is another method to test LLMs' robustness against unexpected inputs. By appending uppercase letters to words within a question, we can create confusion for LLMs, such as GPT-3 and ChatGPT, which rely on subword tokens. In the example provided, the added noise leads to confusion, and the LLMs fail to answer the question correctly. In contrast, humans can easily ignore the noise and provide the correct answer.

ASCII Art: The Challenge of Visual Abstraction

Understanding ASCII art requires visual abstraction capabilities, which LLMs lack. In the example provided, both GPT-3 and ChatGPT struggle to correctly identify the ASCII art representation of a spider. While ChatGPT attempts to analyze the art by locating character groups, it fails to process the characters globally, which results in an incorrect answer. This limitation demonstrates that graphical understanding remains a challenge for LLMs, providing another way to differentiate them from humans.

Implications for Differentiating LLMs and Humans

The limitations of LLMs in tasks such as counting, substitution, positioning, random editing, noise injection, and ASCII art interpretation provide valuable insights for differentiating between LLM-generated content and human responses. These weaknesses can be leveraged to design various tasks or tests, known as FLAIRs (Functionality-based Language AI Robustness tests), which can effectively identify LLMs' outputs and differentiate them from human responses.

Future Directions: Overcoming LLM Limitations

As LLMs continue to improve, it is important to address these weaknesses to enable more robust and accurate natural language processing. Potential avenues for research include developing new techniques to enhance LLMs' counting and positioning abilities, improving their robustness against noisy inputs, and incorporating visual abstraction capabilities to enable better understanding of ASCII art and other graphical representations.

State-of-the-art LLMs, such as GPT-3 and ChatGPT, possess impressive capabilities but still struggle with certain tasks where humans excel. By understanding these limitations and their implications, we can not only differentiate between LLMs and humans more effectively but also guide future research in improving LLMs' robustness and accuracy. The exploration of these limitations is crucial to advancing the field of artificial intelligence and unlocking the full potential of natural language processing models.

Leveraging the Strength of LLMs Against Them

Leveraging the Strength of LLMs in Memorization

Memorization: A Strength of LLMs

Large Language Models like GPT-4 are known for their impressive memorization abilities. They can recall vast amounts of information from their pre-training on massive text corpora. On the other hand, humans generally struggle with memorization, especially when it comes to long lists of items or specific, domain-specific information. So, how can we utilize LLMs' memorization abilities effectively?

Designing Enumeration Questions for LLMs

One approach to capitalize on the memorization strength of LLMs is to design enumeration questions. These questions ask users to list items within a given category. For example, a question might ask for the capitals of all U.S. states or the names of all Intel CPU series. The idea is to create questions that are challenging for humans due to their extensive memory requirements. The more items in the list or the more obscure the information, the harder it becomes for humans to answer correctly.

Domain-Specific Questions for LLMs

Domain-specific questions can also take advantage of LLMs' memorization abilities. These questions typically involve specialized knowledge that most humans wouldn't encounter in daily life. Examples include asking for the first 50 digits of π or the cabin volume of a typical Boeing 737. LLMs are well-equipped to answer these long-tail questions, whereas humans may struggle to provide accurate responses.

Leveraging the Strength of LLMs in Computation

Computation: Another LLM Strength

In addition to memorization, LLMs excel in computation. They can perform complex calculations and recall the results of common equations with relative ease. Humans, on the other hand, usually find complex calculations challenging, especially without external aids like calculators.

Designing Computation Questions for LLMs

To leverage LLMs' computational abilities, one can design questions that involve intricate mathematical problems, such as multiplication or algebraic equations. For example, a question might ask for the square of π or the result of a specific multiplication operation. Since LLMs can solve these problems quickly and accurately, they can provide precise answers that might be difficult for humans to compute mentally.

Uncommon Equations and LLM Hallucination

One interesting aspect of LLMs' computational abilities is that they may hallucinate false answers when faced with uncommon equations. For example, if asked to compute the result of 3256 * 354, GPT-3 might provide an incorrect response like 1153664 instead of the actual answer, 1152624. This behavior can be used to distinguish LLMs from humans, as humans are less likely to fabricate answers and more likely to admit they don't know the solution.

By designing questions that leverage the strengths of LLMs in memorization and computation, one can effectively distinguish between LLMs and humans. Such questions can be employed in various scenarios to identify whether a response comes from an LLM or a human, ultimately showcasing the power and limitations of these advanced language models.


FLAIR presents a novel framework for detecting conversational bots by employing a single-question approach that capitalizes on the contrasting abilities of humans and bots. By utilizing questions that exploit their respective strengths and weaknesses, FLAIR offers online service providers a new way to protect themselves against malicious activities and ensure they are serving real users.

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