Papers with Pairwise Ranking Prompting
Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting (2024.findings-naacl)
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Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Le Yan, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, Michael Bendersky
| Challenge: | Existing methods to rank documents using large language models do not understand these challenging ranking formulations. |
| Approach: | They propose to use Pairwise Ranking Prompting to improve ranking performance . they propose to outperform fine-tuned baseline rankers on benchmark datasets . |
| Outcome: | The proposed technique outperforms supervised baselines on benchmark datasets and outperformed other LLM-based solutions by over 10% on average. |
PRP-Graph: Pairwise Ranking Prompting to LLMs with Graph Aggregation for Effective Text Re-ranking (2024.acl-long)
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| Challenge: | Existing methods for pairing ranking prompting only output the same label for comparison results of different confidence intervals without considering the uncertainty of pairwise comparison. |
| Approach: | They propose a pairwise ranking prompting approach that exploits the output probabilities of target labels to capture the degree of certainty of comparison results. |
| Outcome: | The proposed method shows strong robustness and acceptable efficiency on the BEIR benchmark. |