{"url":"/dataset/indirect-requests","name":"indirect-requests","full_name":null,"description_markdown":"IndirectRequests is an LLM-generated dataset of user utterances in a task-oriented dialogue setting where the user does not directly specify their preferred slot value.\r\n\r\nIndirectRequests was generated by crowdsourcing human labels over a dataset generated using a combination of GPT-3.5 (turbo) and GPT-4. Each utterance is labelled along two dimensions:\r\n\r\nWorld Understanding (the degree of world understanding it takes to understand the utterance)\r\nUnambiguity (whether or not the generated utterance unambiguously entails a single target slot value among a set of candidate possible values).","description_withheld":null,"homepage":"https://huggingface.co/datasets/msamogh/indirect-requests","introduced_date":"2023-07-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/indirectrequests-making-task-oriented","title":"Making Task-Oriented Dialogue Datasets More Natural by Synthetically Generating Indirect User Requests","first_author":"Amogh Mannekote","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["indirect-requests"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}