Papers with ranking process
Make Large Language Model a Better Ranker (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) demonstrate robust capabilities across various fields . current list-wise approaches fail in ranking tasks due to misalignment between ranking objectives and next-token prediction . |
| Approach: | They propose a large language model framework with Aligned Listwise Ranking Objectives (ALRO) this framework provides explicit feedback in a listwise manner by introducing soft lambda loss . |
| Outcome: | The proposed model outperforms existing recommendation methods and embedding-based recommendations without additional computational burdens. |
URG: A Unified Ranking and Generation Method for Ensembling Language Models (2024.findings-acl)
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| Challenge: | Existing approaches to rank and generate large language models have limited performance due to time-intensive nature of ranking process and lack of error propagation. |
| Approach: | They propose a framework that jointly ranks the outputs of Large Language Models and generates fine-grained fusion results. |
| Outcome: | The proposed framework achieves state-of-the-art (SOTA) performance on ranking and generation tasks. |
Natural Language Video Localization with Learnable Moment Proposals (2021.emnlp-main)
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| Challenge: | Existing methods for video moment localization have poor performance due to predefined rules. |
| Approach: | They propose a model with a fixed set of learnable moment proposals with 'border-aware loss' they propose to localize the video moment corresponding to the query by locating the start and end timestamps in an untrimmed video. |
| Outcome: | The proposed model outperforms state-of-the-art models on two challenging benchmarks. |
Modularized Transfomer-based Ranking Framework (2020.emnlp-main)
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| Challenge: | Recent innovations in Transformer-based ranking models have advanced the state-of-the-art in information retrieval. |
| Approach: | They propose to modularize a Transformer ranker into separate modules for text representation and interaction. |
| Outcome: | The proposed model is faster than previous models and is easier to interpret and understand. |
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs (2025.findings-acl)
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| Challenge: | Neural ranking models produce the final document scores, but they are often treated as transient information and only the relative orderings are preserved to produce a ranking. |
| Approach: | They propose to exploit large language models (LLMs) to provide relevance and uncertainty signals for these neural text rankers to produce scale-calibrated scores through Monte Carlo sampling of natural language explanations (NLEs). |
| Outcome: | The proposed approach outperforms previous calibration methods and LLM-based methods for ranking, calibration, and query performance prediction tasks. |