The Task-oriented Queries Benchmark (ToQB)
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914824697413632 |
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| author | Yim, Keun Soo |
| author_facet | Yim, Keun Soo |
| contents | Task-oriented queries (e.g., one-shot queries to play videos, order food, or call a taxi) are crucial for assessing the quality of virtual assistants, chatbots, and other large language model (LLM)-based services. However, a standard benchmark for task-oriented queries is not yet available, as existing benchmarks in the relevant NLP (Natural Language Processing) fields have primarily focused on task-oriented dialogues. Thus, we present a new methodology for efficiently generating the Task-oriented Queries Benchmark (ToQB) using existing task-oriented dialogue datasets and an LLM service. Our methodology involves formulating the underlying NLP task to summarize the original intent of a speaker in each dialogue, detailing the key steps to perform the devised NLP task using an LLM service, and outlining a framework for automating a major part of the benchmark generation process. Through a case study encompassing three domains (i.e., two single-task domains and one multi-task domain), we demonstrate how to customize the LLM prompts (e.g., omitting system utterances or speaker labels) for those three domains and characterize the generated task-oriented queries. The generated ToQB dataset is made available to the public. We further discuss new domains that can be added to ToQB by community contributors and its practical applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_02943 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Task-oriented Queries Benchmark (ToQB) Yim, Keun Soo Information Retrieval Artificial Intelligence Computation and Language Human-Computer Interaction Neural and Evolutionary Computing Task-oriented queries (e.g., one-shot queries to play videos, order food, or call a taxi) are crucial for assessing the quality of virtual assistants, chatbots, and other large language model (LLM)-based services. However, a standard benchmark for task-oriented queries is not yet available, as existing benchmarks in the relevant NLP (Natural Language Processing) fields have primarily focused on task-oriented dialogues. Thus, we present a new methodology for efficiently generating the Task-oriented Queries Benchmark (ToQB) using existing task-oriented dialogue datasets and an LLM service. Our methodology involves formulating the underlying NLP task to summarize the original intent of a speaker in each dialogue, detailing the key steps to perform the devised NLP task using an LLM service, and outlining a framework for automating a major part of the benchmark generation process. Through a case study encompassing three domains (i.e., two single-task domains and one multi-task domain), we demonstrate how to customize the LLM prompts (e.g., omitting system utterances or speaker labels) for those three domains and characterize the generated task-oriented queries. The generated ToQB dataset is made available to the public. We further discuss new domains that can be added to ToQB by community contributors and its practical applications. |
| title | The Task-oriented Queries Benchmark (ToQB) |
| topic | Information Retrieval Artificial Intelligence Computation and Language Human-Computer Interaction Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2406.02943 |