CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization (2023.findings-acl)
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| Challenge: | Existing work suggests that the degree of hallucination depends on factual errors in training data. |
| Approach: | They propose a method to use training data to reduce hallucination by ensembling parameter variations in training data. |
| Outcome: | The proposed method improves on XSUM and CNN/DM datasets on human evaluations and factual metrics. |
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| Challenge: | State-of-the-art abstractive summarization systems often generate hallucinations, i.e., content that is not directly inferable from the source document. |
| Approach: | They propose a detection approach that separates factual from non-factual hallucinations of entities by masked language models. |
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ACUEval: Fine-grained Hallucination Evaluation and Correction for Abstractive Summarization (2024.findings-acl)
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| Challenge: | Recent-proposed evaluation metrics for large language models have a preference-bias . however, such metrics often lack interpretability and only offer a single score . |
| Approach: | They propose a metric that leverages the power of large language models to perform two sub-tasks: decomposing summaries into atomic content units and validating them against the source document. |
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Reducing Quantity Hallucinations in Abstractive Summarization (2020.findings-emnlp)
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| Challenge: | Abstractive summaries are subject to hallucination, but they are not very informative. |
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On Faithfulness and Factuality in Abstractive Summarization (2020.acl-main)
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| Challenge: | Existing conditional text generation models produce unfaithful and unfaithed summaries . current models accomplish a high level of fluency and coherence . |
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CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization (2021.emnlp-main)
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| Challenge: | Existing methods for generating abstractive summarization are inconsistent and rely on heuristically created data for error handling. |
| Approach: | They propose a contrastive learning formulation that leverages both positive and negative summaries to train summarization systems that are better at distinguishing between them. |
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| Challenge: | Language models (LMs) often struggle to pay enough attention to the input context, and generate texts that are unfaithful or contain hallucinations. |
| Approach: | They propose a context-aware decoding technique that amplifies the difference between the output probabilities when a model is used with and without context. |
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Elastic Weight Removal for Faithful and Abstractive Dialogue Generation (2024.naacl-long)
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| Challenge: | Current-day large language models generate coherent, grammatical, and seemingly meaningful text, but are prone to hallucinating incorrect information. |
| Approach: | They propose to ‘subtract’ parameters of a model trained to hallucinate from a dialogue response generation model to ‘negate’ the contribution of such hallucinatedexamples from it. |
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CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning (2022.naacl-main)
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Xiangru Tang, Arjun Nair, Borui Wang, Bingyao Wang, Jai Desai, Aaron Wade, Haoran Li, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev
| Challenge: | Factual inconsistencies in generated summaries severely limit the practical applications of abstractive dialogue summarization. |
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Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information (2024.findings-naacl)
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| Challenge: | Prior studies have attempted to enhance faithfulness of abstractive summarization, yet hallucination remains a persistent challenge. |
| Approach: | They propose a decoding strategy that adjusts the generation probability of each token by comparing it with the token’s marginal probability within the domain of the source text. |
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Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports (2020.acl-main)
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| Challenge: | Existing abstractive summarization models do not guarantee factual correctness of summaries . |
| Approach: | They propose a framework where models evaluate factual correctness by fact-checking it against its reference using an information extraction module. |
| Outcome: | The proposed method significantly improves the factual correctness and overall quality of outputs over a competitive neural summarization system, producing radiology summaries that approach the quality of human-authored ones. |