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Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation

2023-11-16 00:13:19
Yifu Qiu, Varun Embar, Shay B. Cohen, Benjamin Han

Abstract

Neural knowledge-to-text generation models often struggle to faithfully generate descriptions for the input facts: they may produce hallucinations that contradict the given facts, or describe facts not present in the input. To reduce hallucinations, we propose a novel decoding method, TWEAK (Think While Effectively Articulating Knowledge). TWEAK treats the generated sequences at each decoding step and its future sequences as hypotheses, and ranks each generation candidate based on how well their corresponding hypotheses support the input facts using a Hypothesis Verification Model (HVM). We first demonstrate the effectiveness of TWEAK by using a Natural Language Inference (NLI) model as the HVM and report improved faithfulness with minimal impact on the quality. We then replace the NLI model with our task-specific HVM trained with a first-of-a-kind dataset, FATE (Fact-Aligned Textual Entailment), which pairs input facts with their faithful and hallucinated descriptions with the hallucinated spans marked. The new HVM improves the faithfulness and the quality further and runs faster. Overall the best TWEAK variants improve on average 2.22/7.17 points on faithfulness measured by FactKB over WebNLG and TekGen/GenWiki, respectively, with only 0.14/0.32 points degradation on quality measured by BERTScore over the same datasets. Since TWEAK is a decoding-only approach, it can be integrated with any neural generative model without retraining.

Abstract (translated)

神经知识到文本生成模型通常在生成输入事实的描述时存在问题:它们可能会产生与给定事实相矛盾的幻觉,或者描述在输入中不存在的事实。为了减少幻觉,我们提出了一个新颖的解码方法:TWEAK(在生成过程中思考有效地表达知识)。TWEAK在解码过程中处理生成的序列及其未来序列作为假设,并使用假设验证模型(HVM)根据假设对输入事实的支持程度对每个生成候选者进行排名。我们首先通过使用自然语言推理(NLI)模型作为HVM并报告最低影响和最佳质量改善来证明TWEAK的有效性。然后,我们将NLI模型用我们使用第一个数据集FATE(事实与文本一致性)训练的特定任务HVM替换,该HVM将输入事实与它们的忠实和幻觉描述与标记的幻觉段对齐。新的HVM进一步提高了 faithfulness 和 quality,并运行更快。总体而言,最佳 TWEAK 变体在 FactKB 和 TekGen/GenWiki 上分别改进了 2.22/7.17 个点,而仅在同一数据集上 BERTScore 上降低了 0.14/0.32 个点。由于 TWEAK 是一种解码- only 方法,因此可以将其集成到任何神经生成模型中而无需重新训练。

URL

https://arxiv.org/abs/2311.09467

PDF

https://arxiv.org/pdf/2311.09467.pdf


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