Summary of Self-directed Turing Test For Large Language Models, by Weiqi Wu et al.
Self-Directed Turing Test for Large Language Models
by Weiqi Wu, Hongqiu Wu, Hai Zhao
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to evaluating Large Language Models (LLMs) is proposed in this paper, introducing the Self-Directed Turing Test. This extended test format adopts a burst dialogue style, enabling more dynamic conversations with multiple consecutive messages. The LLM self-directs most of the testing process, iteratively generating dialogues that simulate interactions with humans. A pseudo-dialogue history is maintained, followed by a shorter human-LLM conversation on the same topic, which is then evaluated using questionnaires. The X-Turn Pass-Rate metric assesses the human likeness of LLMs across varying dialogue durations. Results show that LLMs like GPT-4 initially perform well, achieving pass rates of 51.9% and 38.9% during 3 turns and 10 turns of dialogues respectively, but their performance drops as the dialogue progresses, highlighting the challenge of maintaining consistency in long-term conversations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test how good AI language models are at talking like humans. The traditional Turing test doesn’t work well because it’s too simple and has people involved all the time. This new test lets the AI talk more freely, making its own decisions about what to say next. It also gets rid of some human involvement by having the AI decide most of the conversation itself. To make sure the AI is doing a good job, researchers use questionnaires to see how well it does compared to humans. The results show that AI models like GPT-4 are pretty good at first, but get worse as they keep talking. |
Keywords
» Artificial intelligence » Gpt
