Papers with factuality metric
Estimating Knowledge in Large Language Models Without Generating a Single Token (2024.emnlp-main)
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| Challenge: | Existing methods to evaluate knowledge in large language models require querying and evaluating the model's generated responses. |
| Approach: | They ask whether it is possible to estimate how knowledgeable a model is about a subject entity only from its internal computation. |
| Outcome: | The proposed model performs well with QA accuracy and FActScore . it can be leveraged to guide decisions on how to apply further training or augment queries with retrieval. |
X-FACTOR: A Cross-metric Evaluation of Factual Correctness in Abstractive Summarization (2022.emnlp-main)
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Subhajit Chaudhury, Sarathkrishna Swaminathan, Chulaka Gunasekara, Maxwell Crouse, Srinivas Ravishankar, Daiki Kimura, Keerthiram Murugesan, Ramón Fernandez Astudillo, Tahira Naseem, Pavan Kapanipathi, Alexander Gray
| Challenge: | Abstractive summarization models produce factually inconsistent summaries that are not supported by the original article. |
| Approach: | They propose a fact-aware filtering mechanism that improves the factuality of abstractive summarization models. |
| Outcome: | The proposed method improves the quality of training data and the factuality of generated summaries. |
Improving Factuality with Explicit Working Memory (2025.acl-long)
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Mingda Chen, Yang Li, Karthik Padthe, Rulin Shao, Alicia Yi Sun, Luke Zettlemoyer, Gargi Ghosh, Wen-tau Yih
| Challenge: | Large language models can generate factually inaccurate content, a problem known as hallucination. |
| Approach: | They propose an approach that integrates a working memory that receives feedback from external resources. |
| Outcome: | The proposed method outperforms baselines on four fact-seeking datasets and increases the factuality metric by 2 to 6 points absolute. |