X-TURING: Towards an Enhanced and Efficient Turing Test for Long-Term Dialogue Agents
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| Format: | Preprint |
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2024
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| _version_ | 1866908383649464320 |
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| author | Wu, Weiqi Wu, Hongqiu Zhao, Hai |
| author_facet | Wu, Weiqi Wu, Hongqiu Zhao, Hai |
| contents | The Turing test examines whether AIs exhibit human-like behaviour in natural language conversations. The traditional setting limits each participant to one message at a time and requires constant human participation. This fails to reflect a natural conversational style and hinders the evaluation of dialogue agents based on Large Language Models (LLMs) in complex and prolonged interactions. This paper proposes \textbf{\textsc{X-Turing}}, which enhances the original test with a \textit{burst dialogue} pattern, allowing more dynamic exchanges using consecutive messages. It further reduces human workload by iteratively generating dialogues that simulate the long-term interaction between the agent and a human to compose the majority of the test process. With the \textit{pseudo-dialogue} history, the agent then engages in a shorter dialogue with a real human, which is paired with a human-human conversation on the same topic to be judged using questionnaires. We introduce the \textit{X-Turn Pass-Rate} metric to assess the human likeness of LLMs across varying durations. While 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, their performance drops as the dialogue progresses, which underscores the difficulty in maintaining consistency in the long term. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_09853 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | X-TURING: Towards an Enhanced and Efficient Turing Test for Long-Term Dialogue Agents Wu, Weiqi Wu, Hongqiu Zhao, Hai Computation and Language Artificial Intelligence The Turing test examines whether AIs exhibit human-like behaviour in natural language conversations. The traditional setting limits each participant to one message at a time and requires constant human participation. This fails to reflect a natural conversational style and hinders the evaluation of dialogue agents based on Large Language Models (LLMs) in complex and prolonged interactions. This paper proposes \textbf{\textsc{X-Turing}}, which enhances the original test with a \textit{burst dialogue} pattern, allowing more dynamic exchanges using consecutive messages. It further reduces human workload by iteratively generating dialogues that simulate the long-term interaction between the agent and a human to compose the majority of the test process. With the \textit{pseudo-dialogue} history, the agent then engages in a shorter dialogue with a real human, which is paired with a human-human conversation on the same topic to be judged using questionnaires. We introduce the \textit{X-Turn Pass-Rate} metric to assess the human likeness of LLMs across varying durations. While 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, their performance drops as the dialogue progresses, which underscores the difficulty in maintaining consistency in the long term. |
| title | X-TURING: Towards an Enhanced and Efficient Turing Test for Long-Term Dialogue Agents |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2408.09853 |