| Challenge: | Existing methods for error tracing do not detect faithfulness errors in NLG datasets. |
| Approach: | They propose a framework to identify and remove low-quality training instances that lead to undesirable outputs. |
| Outcome: | The proposed method outperforms existing methods for detecting faithfulness errors in NLG datasets. |
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| Challenge: | Language models often generate fluent and convincing content but can lack consistency with the provided source, resulting in potential inaccuracies. |
| Approach: | They propose a new decoding method that augments the contrastive search framework with context-aware regularization terms to promote tokens that are semantically similar to the provided source while penalizing repetitiveness in the generated text. |
| Outcome: | The proposed method improves faithfulness across various language models while maintaining output diversity comparable to well-performing decoding algorithms. |
HALoGEN: Fantastic LLM Hallucinations and Where to Find Them (2025.acl-long)
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| Challenge: | generative large language models produce hallucinations that are not aligned with world knowledge or input context. |
| Approach: | They propose a hallucination benchmark framework that measures hallucinism in large language models . they evaluate 150,000 generations from 14 language models and find they are riddled with hallucinos . |
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On A Scale From 1 to 5: Quantifying Hallucination in Faithfulness Evaluation (2025.findings-naacl)
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| Challenge: | Hallucination is a popular topic in natural language generation (NLG). |
| Approach: | They propose to use large language models to evaluate faithfulness of guided NLGs by a rubric template and large language inference models to score the generation on quantifiable scales. |
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On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization (2024.eacl-short)
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| Challenge: | Text summarization and simplification are among the most widely used applications of NLP, but they are prone to hallucination due to training on unaligned data. |
| Approach: | They propose a loss-truncation approach to modify the standard log loss to adaptively remove noisy examples during training to improve model performance. |
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Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis (2022.findings-emnlp)
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| Challenge: | Existing learning metrics are limited to tasks where large human ratings are available. |
| Approach: | They propose a model-based natural language generation (NLG) evaluation metric that is highly correlated with human judgements without requiring human annotation. |
| Outcome: | The proposed metric outperforms all prior unsupervised metrics on multiple NLG tasks including translation, image captioning, and WebNLG text generation. |
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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Pre-trained Language Models Return Distinguishable Probability Distributions to Unfaithfully Hallucinated Texts (2024.findings-emnlp)
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| Challenge: | 88-98% of cases return distinguishable generation probability and uncertainty distributions to unfaithfully hallucinated texts, regardless of their size and structure. |
| Approach: | They examine 24 pre-trained language models on 6 data sets to examine their ability to distinguish unfaithfully hallucinated texts. |
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A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation (P19-1)
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| Challenge: | Recent neural language generation systems often hallucinate contents when trained on loosely corresponding pairs of the input structure and text. |
| Approach: | They propose to integrate a language understanding module for data refinement with self-training iterations to induce strong equivalence between the input data and the paired text. |
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Leveraging Entailment Judgements in Cross-Lingual Summarisation (2024.findings-acl)
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| Challenge: | Synthetically created cross-lingual summarisation datasets are prone to include document-summary pairs where the reference summary is unfaithful to the corresponding document. |
| Approach: | They propose to use off-the-shelf cross-lingual Natural Language Inference to evaluate faithfulness of reference and model generated summaries and use unlikelihood loss to teach a model about unfaithful summary sequences. |
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Critic-Driven Decoding for Mitigating Hallucinations in Data-to-text Generation (2023.emnlp-main)
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| Challenge: | Hallucination of text lacking grounding in input data is a problem in neural data-to-text generation. |
| Approach: | They propose to combine probabilistic output of a generator language model with the output of an “text critic” classifier which guides the generation by assessing the match between the input data and the generated text. |
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