Generating Faithful Text From a Knowledge Graph with Noisy Reference Text (2023)

Abstract: Knowledge Graph (KG)-to-Text generation aims at generating fluent natural-language text that accurately represents the information of a given knowledge graph. While significant progress has been made in this task by exploiting the power of pre-trained language models (PLMs) with appropriate graph structure-aware modules, existing models still fall short of generating faithful text, especially when the ground-truth natural-language text contains additional information that is not present in the graph. In this paper, we develop a KG-to-text generation model that can generate faithful natural-language text from a given graph, in the presence of noisy reference text. Our framework incorporates two core ideas: Firstly, we utilize contrastive learning to enhance the model’s ability to differentiate between faithful and hallucinated information in the text, thereby encouraging the decoder to generate text that aligns with the input graph. Secondly, we empower the decoder to control the level of hallucination in the generated text by employing a controllable text generation technique. We evaluate our model’s performance through the standard quantitative metrics as well as a ChatGPT-based quantitative and qualitative analysis. Our evaluation demonstrates the superior performance of our model over state-of-the-art KG-to-text models on faithfulness.

## introduction a knowledge graph (kg) is a structured representation of information as a network of interconnected real-world entities, and relationships. the task of kg-to-text generation has been proposed @xcite to make this structured information more accessible to humans, aiming to generate fluent, informative, and faithful natural-language sentences that should describe the contents of an input kg. recently, this task plays a significant role in a variety of applications such as knowledge-grounded dialogue generation @xcite , story generation @xcite , event narration @xcite , and questionanswering @xcite @xcite . significant progress has been made in the kg-to-text generation task by utilizing a set of transformer-based @xcite pre-trained language models (plms) such as bart @xcite , t5 @xcite or gpt @xcite with appropriate graph structure-aware modules @xcite @xcite . however, ensuring the faithfulness of kg-to-text generation, i.e. reducing hallucinations @xcite @xcite , is an under-explored problem, and existing kg-to-text models fall short of generating faithful text when the ground-truth text of the training dataset contains wrong or extra information that is not consistent with the input. figure 1 shows an example of a small kg about a house, which contains information on its internal features and neighborhood, and the corresponding ground-truth reference text, from a real-world realestate kg @xcite . the ground-truth text, while summarizing the features of the house accurately, also mentions some information that is not available in the input kg (i.e. extrinsic hallucination, highlighted in red). when a kg-to-text model is trained with such hallucinated reference text, it is likely to produce text that is also hallucinated. this hallucination problem significantly reduces the faithfulness and thus trustworthiness of the generated text. thus, the ability to reduce hallucination in the presence of noisy reference text is important for the practi-cal application of kg-to-text and other nlg techniques, especially in mission-and safety-critical domains such as medical diagnostics and scientific research. a number of techniques have been proposed @xcite to control this hallucination problem in abstractive summarization, table-to-text generation, generative question-answering, neural machine translation, and knowledge-grounded dialogue generation @xcite @xcite @xcite . however, to the best of our knowledge, controlling hallucination in graph-to-text generation with noisy reference text has not been investigated. in this paper, we propose a novel framework to address this important and practical problem. our framework combines contrastive learning technique and controllable text generation. contrastive learning enables the model to distinguish between faithful and hallucinated text and guides the decoder to generate faithful text instead of hallucinated text. the controllable text generation technique learns the level of hallucination from noisy training text and controls (i.e. minimizes) the level of hallucinated information in the generated text. our framework can be employed in any kg-to-text encoder-decoder model to generate faithful natural language text from a given kg, in the presence of noisy reference text. our contributions are as follows: our framework comprises two core ideas: (i) employing contrastive learning to enable the kg-to-text generation model to better differentiate between faithful and hallucinated information in the reference text and guide the decoder to generate text that is faithful to kg. (ii) controlling the level of hallucination while generating text from kg using a controllable text generation technique. • we conduct experiments and evaluate performance using a standard quantitative analysis with automatic metrics. our comprehensive evaluation on two noisy datasets demonstrates the superior performance of our proposed model over the state-of-art kg-to-text generation models on faithfulness metrics. • we further propose and perform novel chatgpt-based quantitative and qualitative evaluations to assess the performance of our model more comprehensively. the evaluation also shows our model's effectiveness in generating faithful text over existing kg-to-text generation models. ## conclusion in this paper, we have proposed a novel approach to generate faithful text from a knowledge graph having noisy ground-truth text. to ensure faithful text generation, we have introduced two key ideas: (i) contrastive learning to better differentiate between faithful and hallucinated information, (ii) control token to regulate the level of hallucination in the generated text. experimental results on two noisy kg-to-text datasets demonstrates that kgto-text model with our framework outperforms all the baseline models in terms of faithfulness metrics. moreover, we have proposed a novel chat-gpt based evaluation technique for an in-depth quantitative and qualitative analysis, which further verifies the superior performance of our model on precision, recall and faithfulness. limitation and future work we have applied our proposed framework only in plm based kg-totext encoder-decoder model. in future, we plan to explore the hallucination problem in amr (abstract meaning representations) graph datasets, which can also preserve a number of meaningful semantic relations and widely used in nlp areas.