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Description
A dataset designed to strengthen multi-turn, interactive capabilities, including open-ended chat and precise instruction following. The chat subset uses human-written prompts from sources like lmarena, lmsys, and wildchat as seed prompts, with responses generated by GLM-5 and selected via pairwise comparisons using a reward model. It was authored by NVIDIA and last updated on the platform in June 2026.
Use Cases
Fine-tuning language models for multi-turn conversational ability based on the described chat subset.
Training models on precise instruction following based on the dataset's stated design goal.
Benchmarking or improving response quality in open-ended dialogue systems based on the use of pairwise comparisons for response selection.
Studying human-AI interaction patterns using the human-written prompts from specified sources.
Strengths
Responses are generated by the GLM-5 model and the best is selected via pairwise comparisons using a specific reward model (Qwen3-Nemotron-235B-A22B-GenRM-2603).
Prompts are sourced from multiple human-written datasets including lmarena, lmsys, and wildchat.
Dataset is designed with a specific focus on multi-turn, interactive capabilities.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Row count, file formats, and license information are unknown, which may limit suitability assessment.
Data may reflect source bias inherent to the specific prompt platforms used.
Provenance
Source
NVIDIA
Collection Method
Prompts sourced from human-written datasets; responses generated by GLM-5 and selected via model-based pairwise comparison.
Freshness
Last updated 2026-06-04 04:37:43; freshness should be verified.
License is unknown; users must verify terms of use before application.