Exploiting Positional Bias for Query-Agnostic Generative Content in Search (2024.findings-acl)
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| Challenge: | Recent studies show that neural ranking models outperform lexical models in text retrieval. |
| Approach: | They propose to exploit transformer attention mechanism to induce exploitable defects in search models through sensitivity to token position within a sequence. |
| Outcome: | The proposed model can generalise beyond a single query or topic without knowledge of topicality. |
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Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip Yu, Zhijiang Guo
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| Challenge: | Existing methods for listwise reranking exhibit intrinsic position bias . existing methods are constrained by an inherent trade-off between efficiency and flexibility . |
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| Challenge: | a new evaluation framework is used to assess the extent and impact of position bias in information retrieval. |
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Beyond [CLS] through Ranking by Generation (2020.emnlp-main)
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| Challenge: | Recent work on generative ranking models for Information Retrieval has focused on discriminative methods that learn a similarity function to compare questions and candidates answers. |
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