Automatic Generation of Large-scale Multi-turn Dialogues from Reddit
This paper presents novel methods to automatically convert posts and their comments from discussion forums such as Reddit into multi-turn dialogues. Our methods are generalizable to any forums; thus, they allow us to generate a massive amount of dialogues for diverse topics that can be used to pretrain language models. Four methods are introduced, Greedy_Baseline, Greedy_Advanced, Beam Search and Threading, which are applied to posts from 10 subreddits and assessed. Each method makes a noticeable improvement over its predecessor such that the best method shows an improvement of 36.3% over the baseline for appropriateness. Our best method is applied to posts from those 10 subreddits for the creation of a corpus comprising 10,098 dialogues (3.3M tokens), 570 of which are compared against dialogues in three other datasets, Blended Skill Talk, Daily Dialogue, and Topical Chat. Our dialogues are found to be more engaging but slightly less natural than the ones in the other datasets, while it costs a fraction of human labor and money to generate our corpus compared to the others. To the best of our knowledge, it is the first work to create a large multi-turn dialogue corpus from Reddit that can advance neural dialogue systems.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation
Retrieval-Augmented Generation (RAG) has become a powerful paradigm for enhancing large language models (LLMs) through external knowledge retrieval. Despite its widespread attention, existing academic research predominan…
BenchmarkingPassage RetrievalRAGResponse Generation+2X-ReCoSa: Multi-Scale Context Aggregation For Multi-Turn Dialogue Generation
In multi-turn dialogue generation, responses are not only related to the topic and background of the context but also related to words and phrases in the sentences of the context. However, currently widely used hierarchi…
DecoderDialogue GenerationResponse GenerationSentenceUser-Oriented Multi-Turn Dialogue Generation with Tool Use at scale
The recent paradigm shift toward large reasoning models (LRMs) as autonomous agents has intensified the demand for sophisticated, multi-turn tool-use capabilities. Yet, existing datasets and data-generation approaches ar…
Dialogue GenerationOpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts
In order to better simulate the real human conversation process, models need to generate dialogue utterances based on not only preceding textual contexts but also visual contexts. However, with the development of multi-m…
Dialogue GenerationMulti-modal Dialogue GenerationWhat Makes Large Language Models Reason in (Multi-Turn) Code Generation?
Prompting techniques such as chain-of-thought have established themselves as a popular vehicle for improving the outputs of large language models (LLMs). For code generation, however, their exact mechanics and efficacy a…
Code Generation