Summary of Mitigating Knowledge Conflicts in Language Model-driven Question Answering, by Han Cao et al.
Mitigating Knowledge Conflicts in Language Model-Driven Question Answering
by Han Cao, Zhaoyang Zhang, Xiangtian Li, Chufan Wu, Hansong Zhang, Wenqing Zhang
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the challenge of minimizing hallucinations in knowledge-driven seq-to-seq generation tasks, such as document-based question answering and document summarization. Recent studies have shown that when there is a misalignment between a model’s inherent knowledge and the ground truth answers in training data, the system may exhibit problematic behaviors during inference. The proposed strategy aims to build explicit connections between source inputs and generated outputs to reduce hallucination. Specifically, the paper targets a common hallucination pattern in question answering, examining how the correspondence between entities and their contexts during model training influences performance at inference time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at ways to improve machine learning models that generate text based on what they’ve learned from documents. Sometimes these models can create false information or ignore important context. The researchers propose a new approach to fix this problem by linking what the model knows to what it generates. They test this idea specifically with question-answering systems, which often make mistakes by creating false answers. |
Keywords
» Artificial intelligence » Hallucination » Inference » Machine learning » Question answering » Summarization
