GPT-4 passed the Turing test with a winning rate of 54%! UCSD new work: Humans cannot recognize GPT-4

Can GPT-4 pass the Turing test?

When a powerful enough model is born, people often use the Turing test to measure the intelligence of this LLM.

Recently, researchers from the Department of Cognitive Science at UCSD discovered that:

In the Turing test, people simply cannot tell the difference GPT-4 and humans!

##Paper address: https://arxiv.org/pdf/2405.08007

In the Turing test, GPT-4 was judged to be human 54% of the time.

The experimental results show that this is the first time that a system has been empirically tested in the "interactive" two-person Turing test.

Researcher Cameron R.Jones recruited 500 volunteers, who were divided into 5 roles: 4 evaluators, namely GPT -4, GPT-3.5, ELIZA and humans, the other character "plays" humans themselves, hiding on the other side of the screen, waiting for the evaluator to discover.

The following is an excerpt from the game. Can you tell which dialog box is human?

Figure 1: Part of the conversation between the human interrogator (green) and the witness (grey)

In fact, among these four conversations, one was with a human witness, and the rest were with artificial intelligence.

The controlled "Turing test" was launched for the first time

In the past 74 years, people have made many attempts at Turing tests, but very few Few controlled experiments have been conducted.

The famous Loebner Award is an annual competition. However, from the first time it was held in 1990 to 2020, no system has passed the test.

#A recent large-scale study found that humans can identify a series of LLMs with 60% accuracy during a 2-minute online conversation.

To date, there have been no "controlled" experimental demonstrations where any machine has passed the test.

To test whether it is possible for people to see through the deception of current AI systems, the researchers used GPT-4 to conduct a randomly controlled two-person Turing test.

The test requirements are very simple, that is, human participants start a 5-minute conversation with humans and artificial intelligence, and determine whether the interlocutor is a human.

As mentioned at the beginning, the researchers evaluated a total of 3 models-GPT-4, GPT-3.5, and ELIZA.

For the first two LLMs, the researchers adapted the best-performing prompts from the GPT-4 and GPT-3.5 exploratory studies to indicate How the large model responds to messages.

Researchers say a core challenge in interpreting the Turing test is that a system's superior performance may stem from two reasons:

- One is an excellent imitation of humans

-The second is that the judge is too gullible

The latter situation is often called the ELIZA effect, that is, participation Authors will humanize even simple systems.

The ELIZA system is very familiar to everyone.

It was a simple rules-based chatbot from the 1960s, but users found themselves attributing human characteristics to it as well.

Therefore, in the experiment, the researchers used ELIZA as a baseline, on the one hand to empirically quantify the ELIZA effect, and on the other hand to conduct operational checks in the experiment .

So, what are the specific results of this Turing test?

GPT-4 pass rate is 54%

First look at an important indicator: pass rate.

The researchers define the pass rate as the proportion of evaluation objects in the game that are judged to be human by the evaluators.

The pass rate of GPT-4 is 54%, exceeding GPT-3.5 (50%) and ELIZA baseline (22%), but lower than human subjects (67%) .

Evaluator pass rate (left) and evaluator trust (right). Each point in the picture on the right represents a game

This result is actually not surprising and is basically consistent with the assumptions in the initial stage of the research.

Before the experiment, the researchers made several assumptions: First, it was assumed that humans’ ability to detect GPT-4 would not exceed chance (50%).

To test this, the researchers fit a logistic regression model to predict the passing rate of only the intercept.

There is no significant difference between the intercept and 0 (z=0.14, p=0.482), which indicates that the pass rate of GPT-4 can be approximately equal to 50%. The Bayesian model predicts a 95% confidence interval for the GPT-4 intercept of -0.24-0.55 (equivalent to a pass rate of 44%-63%).

This means that when humans face GPT-4, the probability of successful discrimination is about the same as tossing a coin!

The results also confirmed the researchers’ second and third hypotheses: the human pass rate was significantly higher than the GPT-4 pass rate (z=2.42, p=0.017), The pass rate of GPT-4 is higher than that of ELIZA (z=4.11, p

However, what is inconsistent with the researcher’s experimental assumptions is that the performance of GPT-4 and GPT-3.5 is almost the same (z=0.497, p=0.619).

In terms of "pretending to be human", it's a tie.

Also unexpectedly, participants who said they had a better understanding of how LLMs work were not more successful in identifying LLMs (z = -0.023, p = 0.869).

In exploratory analyses, the researchers found that age had a negative impact on interrogator accuracy (z = -2.24, p = 0.025), but other demographic variables, including Gender, education level, and frequency of interaction with chatbots) had no significant effect.

Why is this the result? Let us analyze the reasons behind it.

To gain insight into what exactly influences evaluators’ decisions, we categorized the strategies evaluators used and the reasons they provided for their decisions.

36% of evaluators would chat with the person being evaluated and ask about some personal details or daily activities.

The most commonly used strategies in Categories 2 and 3 are social and emotional (25%)—for example, asking about the assessee’s opinions and experiences and observing whether they have a sense of humor.

Others focus on knowledge and reasoning (13%), including questions about mathematics, logic and general knowledge.

Ask for strategy (left) and average accuracy (right)

To compare each strategy For effectiveness, the researchers calculated the average accuracy using these strategies.

Usually when the evaluator asks the other person about human experience (75%), logic and mathematics (78%) or directly "accuses" the other person of artificial intelligence (80%) Get closer to the "correct answer".

In games, the reason why artificial intelligence "shows off" is usually because the evaluator feels that they seem to be forced to play a role, either too casually, or lack personality.

And ELIZA is easily "exposed", mainly due to unnatural reactions and lack of contextual awareness.

It can be seen that both in terms of the strategies used by the volunteers participating in the game and the reasons for their decision-making, they pay more attention to language style and social-emotional factors rather than more traditional Intellectual categories (knowledge and reasoning).

This seems to reflect an underlying assumption of the volunteers—social intelligence is the most inimitable human characteristic for AI.

Turing test, passed

How to determine when the system has passed the Turing test?

Turing predicted in his 1950 paper that by the end of the century, AI systems would be able to play the imitation game so well that examiners, after a 5-minute exchange, The chance of correct identification will not exceed 70%.

This conclusion has led to whether the pass rate of AI can reach 30%, which has become the discussion goal of many researchers.

However, this benchmark seems arbitrary, and it is unclear whether Turing intended it as a definition of success.

In fact, the 50% baseline is more reasonable because it better proves that human reviewers are no better than chance at identifying AI.

This definition is particularly relevant to the goal of discovering whether users can reliably identify other humans during online interactions.

In the investigators' pre-publication analysis, this issue was addressed by using the ELIZA baseline as a manipulation check.

Only when the analysis shows that the pass rate of ELIZA is lower than the probability, and the pass rate of GPT-4 is not lower than the probability, the model can be judged to have passed the test.

According to this definition, GPT-4 clearly passed this version of the Turing test.

In addition, the participants’ confidence scores and decision-making basis showed that they were not randomly guessing: GPT-4 has an average human confidence level of 73% (see Figure 2).

Netizen discussion

Some netizens said that if we want to equate the Turing test with intelligence, The Turing test is not a good test. However, the fact that AI can almost always “fool” humans is a bit concerning.

Some people said that they were doubtful about this kind of test. Because GPT-4 will outperform most humans, it will be easy to distinguish who is human and who is artificial intelligence.

Researchers said that this is indeed a problem we have encountered. For example, GPT-4’s knowledge reserve is “too rich” or it masters too many languages. We explicitly prompt the model to avoid this situation, which is effective to a certain extent.

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