Papers with ranking performance
Consolidating Ranking and Relevance Predictions of Large Language Models through Post-Processing (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to generate relevance labels for large language models have not been successful in generating relevance labels. |
| Approach: | They propose a method to combine LLM relevance labels with ranking abilities . they take both LLM generated relevance labels and pairwise preferences . |
| Outcome: | The proposed method balances the ranking and labeling abilities of large language models . it takes both LLM generated relevance labels and pairwise preferences . |
MatRank: Text Re-ranking by Latent Preference Matrix (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for text ranking have improved performance, but there are still challenges. |
| Approach: | They propose a method that learns to re-rank the text retrieved for a given query by learning to predict the most relevant passage based on a latent preference matrix. |
| Outcome: | The proposed method outperforms all prior methods on datasets with extensive results. |
Similar Region Search using LLMs on Spatial Feature Space (2026.findings-eacl)
Copied to clipboard
| Challenge: | Existing similarity search methods fail to capture contextual richness of spatial data . existing methods fail in capturing regional characteristics, authors say . |
| Approach: | They propose a similar region search framework that ranks candidate regions based on their similarity to a query region using large language models. |
| Outcome: | The proposed similar region search framework outperforms state-of-the-art methods on real-world city datasets. |
Cocktail: A Comprehensive Information Retrieval Benchmark with LLM-Generated Documents Integration (2024.findings-acl)
Copied to clipboard
Sunhao Dai, Weihao Liu, Yuqi Zhou, Liang Pang, Rongju Ruan, Gang Wang, Zhenhua Dong, Jun Xu, Ji-Rong Wen
| Challenge: | Large Language Models (LLMs) have led to an influx of AI-generated content on the internet, transforming corpus of Information Retrieval (IR) systems from human-written to a coexistence with LLM-generated contents. |
| Approach: | They propose a benchmark named Cocktail that compares IR models with LLMs to find relevant documents and passages from a corpus. |
| Outcome: | The proposed benchmark aims to evaluate IR models in the mixed-sourced data landscape of the LLM era. |