Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization

By Haolan Zhan, Hongshen Chen, Xiaofang Zhao, Xiao-Dan Zhu. 2021

20 citations recorded by the source corpus.

Being able to reply with a related, fluent, and informative response is an indispensable requirement for building high-quality conversational agents. In order to generate better responses, some approaches have been proposed, such as feeding extra information by collecting large-scale datasets with human annotations, designing neural conversational models (NCMs) with complex architecture and loss functions, or filtering out untrustworthy samples based on a dialogue attribute, e.g., Relatedness or Genericness. In this paper, we follow the third research branch and present a data filtering method for open-domain dialogues, which identifies untrustworthy samples from training data with a quality measure that linearly combines seven dialogue attributes. The attribute weights are obtained via Bayesian Optimization (BayesOpt) that aims to optimize an objective function for dialogue generation iteratively on the validation set. Then we score training samples with the quality measure, sort them in descending order, and filter out those at the bottom. Furthermore, to accelerate the "filter-train-evaluate'' iterations involved in BayesOpt on large-scale datasets, we propose a training framework that integrates maximum likelihood estimation (MLE) and negative training method (NEG). The training method updates parameters of a trained NCMs on two small sets with newly maintained and removed samples, respectively. Specifically, MLE is applied to maximize the log-likelihood of newly maintained samples, while NEG is used to minimize the log-likelihood of newly removed ones. Experimental results on two datasets show that our method can effectively identify untrustworthy samples, and NCMs trained on the filtered datasets achieve better performance.

Subfields: Artificial Intelligence, Natural Language Processing, Optimization & Training

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Citations in the corpus

Survey of Hallucination in Natural Language Generation (2022, background): “Some works [114, 153, 167] have dealt with the hallucination issue at the instance level by using a score for each source-reference pair and filtering out hallucinated ones.”
Hallucination is Inevitable: An Innate Limitation of Large Language Models (2024, methodology): “For data-related issues, solutions include fact-focused datasets [17, 21] and developing automatic data cleaning techniques [42, 51, 54].”
Factuality Enhanced Language Models for Open-Ended Text Generation (2022, background): “Hallucination in downstream NLG tasks There are active efforts to reduce the unfaithfulness or factual errors of task-speciﬁc LMs ﬁne-tuned for various downstream natural language generation (NLG) tasks such as summarization [42–48], data-to-text [49, 50, 20, 51–53] and dialogue system [54–58].”