Papers with document quality
Modelling Uncertainty in Collaborative Document Quality Assessment (D19-55)
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| Challenge: | Existing work on document quality assessment relies on predicting the quality of a document relative to a putative gold standard, without paying attention to the subjectivity of this task. |
| Approach: | They propose to use Gaussian processes and random forests to measure the uncertainty in document quality predictions. |
| Outcome: | The proposed methods can predict the quality of Wikipedia articles while providing an estimate of uncertainty when there is inconsistency in the quality labels from the contributors. |
HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | In-depth research on the specific capabilities needed by the RAG generation model is lacking, leading to inconsistent document quality and retrieval system imperfections. |
| Approach: | They propose that RAG models should possess three progressively hierarchical abilities: (1) Filtering: the ability to select relevant information; (2) Combination: the capability to combine semantic information across paragraphs; (3) RAG-specific reasoning: the capacity to further process external knowledge using internal knowledge. |
| Outcome: | Experiments show that the proposed method significantly improves the model’s open-book examination capability on datasets such as RGB, PopQA, MuSiQue, HotpotQA, and PubmedQA. |
Attention Weights as an Indicator: Analyzing and Improving Document Utilization in Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | In traditional RAG models, documents are grouped into categories based on their quality and order, and the quality of inputs is variable due to ineffective retrievers or misalignment between the retriever and generator. |
| Approach: | They propose to use attention weights to enhance document utilization from three perspectives: document ranking, placement, and filtering. |
| Outcome: | The proposed method outperforms baselines and improves document utilization effectiveness in a training-free manner. |