Papers with ranking algorithms
Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers (2024.naacl-long)
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| Challenge: | Recent studies have shown that Large Language Models (LLMs) are more efficient in natural language understanding tasks. |
| Approach: | They evaluate large language models (LLMs) using a TREC Fair Ranking dataset . they assess fairness from both user and content perspectives . |
| Outcome: | The proposed model outperforms the existing models in the fair ranking task. |
Predicting Clickbait Strength in Online Social Media (2020.coling-main)
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| Challenge: | Clickbaits are sensational, provocative or controversial posts that entice readers to click on them. |
| Approach: | They propose to model clickbait strength prediction using transformers to predict clickbaiting intensity. |
| Outcome: | The proposed model outperforms existing methods on a benchmark dataset with 39K posts on 3K posts. |
AGRaME: Any-Granularity Ranking with Multi-Vector Embeddings (2024.emnlp-main)
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| Challenge: | Existing ranking algorithms restrict granularity to full passages or require a specific dense index for each desired level of granules. |
| Approach: | They propose a multi-vector ranking approach that leverages multi-vctor embeddings to rank at varying levels of granularity while maintaining encoding at a single (coarser) level of grail. |
| Outcome: | The proposed method surpasses prompt-driven citation generation by incorporating proposition-level ranking to post-hoc citation addition. |
Learning Robust Models for e-Commerce Product Search (2020.acl-main)
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| Challenge: | Existing models that understand search intent are difficult to learn due to lack of labeled datasets. |
| Approach: | They develop a deep, end-to-end model that learns to effectively classify mismatches . they introduce a latent variable into the cross-entropy loss that alternates between real and generated samples . |
| Outcome: | The proposed model achieves a relative gain of over 26% in F-score and 17% in Area Under PR curve on live search traffic in multiple countries. |
Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat (2025.acl-long)
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| Challenge: | Evaluating large language models (LLMs) is a complex task. Pairwise ranking has emerged as state-of-the-art method to evaluate human preferences. |
| Approach: | They propose to use pairwise ranking to evaluate human preferences . they propose to evaluate the robustness of ranking algorithms in LLMs . |
| Outcome: | The proposed methods are based on the principles of effective ranking and the robustness of several ranking algorithms in the context of LLMs. |