Tackling Hallucinations in Neural Chart Summarization (2023)

Abstract: Hallucinations in text generation occur when the system produces text that is not grounded in the input. In this work, we tackle the problem of hallucinations in neural chart summarization. Our analysis shows that the target side of chart summarization training datasets often contains additional information, leading to hallucinations. We propose a natural language inference (NLI) based method to preprocess the training data and show through human evaluation that our method significantly reduces hallucinations. We also found that shortening long-distance dependencies in the input sequence and adding chart-related information like title and legends improves the overall performance.

## introduction the task of generating a summary to accompany a chart is an instance of data-to-text generation and has a long tradition in natural language generation (nlg) @xcite @xcite . recent neural models for chart summarization @xcite @xcite carry the promise to be trainable from data and hence more versatile than approaches using manually constructed templates, and to produce more fluent text than previous statistical nlg systems. however, texts generated by state-of-theart neural systems frequently include information which is not grounded in the input ("extrinsic hallucination"), or is even contradictory to it ("intrinsic hallucination"), see an example in hallucinations in nlg @xcite have been a concern in neural models for various tasks @xcite @xcite . we identify two reasons for hallucinations in chart summarization: (1) complexity and missing information in the input format of chart data; (2) presence of ungrounded information in chart summaries of the training data. our contributions are as follows: situation share of respondents 0% 20% 40% 60% on the receiving end of a rude gesture yelled or used profanity made a rude gesture felt physically threatened exited their vehicle to engage angrily road rage behavior among drivers in the u.s. as of 2015. this statistic shows the road rage behavior of drivers in the united states as of 2015. four percent of the drivers said they have been on the receiving end of a rude gesture. the survey was conducted online and all the participants had a valid u.s. driving license. our experimental code and model output will be released on github under an open license. 1 2 background and related work 2.1 recent work in chart summarization several chart summarization datasets and models were developed recently. obeid and hoque (2020) created the chart-to-text data with english charts from statista.com (dubbed c2t-small in this paper). they model chart summarization as a data-to-text problem and adapt a transformer by gong et al. (2019). kantharaj et al. (2022) released an extended dataset crawled from the same platform, also called chart-to-text (c2t-big in this paper). dataset training validation test total c2t-small 5,703 1,222 1,222 8,147 c2t-big 24,367 5,222 5,222 34,811 they finetune multiple pretrained models, such as bart @xcite and t5 @xcite . ## problem identification we manually analyzed two samples of 50 generated chart summaries of obeid and hoque (2020)'s transformer trained on c2t-small and kantharaj et al. (2022)'s t5 model finetuned on c2t-big. for the c2t-small transformer, 22 summaries had intrinsic and 13 had extrinsic hallucinations. some summaries were also incoherent and repetitive. for the c2t-big t5 model, 4 out of 50 summaries had intrinsic and 11 had extrinsic hallucinations. based on our analysis, we speculate that intrinsic and extrinsic hallucinations have different causes road rage behavior among drivers in the u.s. as of 2015 x-y labels situation -share of respondents x-y values on the receiving end of a rude gesture 53%, yelled or used profanity 26%, made a rude gesture 17%, felt physically threatened 13%, exited their vehicle to engage angrily 4% as identified by @xcite . intrinsic hallucinations may happen due to input sequence formatting (i.e., modeling choices), while extrinsic hallucinations are caused by the source-reference divergence in training data (missing input information and noise) @xcite . we address the input format in section 4 by adding context and reducing long-distance dependencies, and we further address noise in references in section 5. ## discussions 6.1 ungrounded information in training data in section 5.1, we reported on ungrounded information in training data and showed in section 5.2 that this leads to hallucinations in generated outputs. while there are good reasons for ungrounded information in human-written summaries (e.g., providing additional detail/background or linking to other events; cf. @xcite , using such data to train an end-to-end model that does not distinguish between describing the chart and providing additional information is not appropriate and leads to inaccurate outputs, which is unsuitable for real-world scenarios (cf. @xcite . ## conclusions we show that reducing long-distance dependencies and providing more context on the model's input results in fewer intrinsic hallucinations, and demonstrate that extrinsic hallucinations are a result of ungrounded information in the training summaries. furthermore, we show through human evaluation that employing nli to filter training summaries results in a significant drop in hallucinations.