Papers with ranking tasks
Sliding Windows Are Not the End: Exploring Full Ranking with Long-Context Large Language Models (2025.acl-long)
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| Challenge: | Existing methods for listwise passage ranking use sliding window approach, which is inefficient as it requires repetitive and serialized processing. |
| Approach: | They propose a listwise label construction approach and importance-aware learning objective for full ranking. |
| Outcome: | The proposed method outperforms existing methods in listwise ranking tasks. |
Deep Metric Learning to Hierarchically Rank - An Application in Product Retrieval (2023.emnlp-industry)
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Kee Kiat Koo, Ashutosh Joshi, Nishaanth Reddy, Karim Bouyarmane, Ismail Tutar, Vaclav Petricek, Changhe Yuan
| Challenge: | e-commerce search engines use customer behavior signals to augment lexical matching and improve search relevance. |
| Approach: | They propose a method to identify duplicate and near-duplicate products across stores . they use Hierarchical Ranked Multi Similarity Loss to learn hierarchical metric space . |
| Outcome: | The proposed model outperforms baselines in terms of catalog coverage and precision of the mappings. |
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. |
Unsupervised Contrast-Consistent Ranking with Language Models (2024.eacl-long)
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| Challenge: | Language models contain ranking-based knowledge and are powerful solvers of in-context ranking tasks. |
| Approach: | They propose to use a model to elicit language models' ranking knowledge without supervision by using a pairwise, pointwise and listwise prompting method. |
| Outcome: | The proposed method is inspired by an unsupervised probing method called Contrast-Consistent Search (CCS). |
MAPS: Motivation-Aware Personalized Search via LLM-Driven Consultation Alignment (2025.acl-long)
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| Challenge: | Existing personalized product search methods assume that users’ query fully captures their real motivation, but in practice, user's queries do not always articulate the requirements. |
| Approach: | They propose a Motivation-Aware Personalized Search method that embeds queries and consultations into a unified semantic space via LLMs and utilizes a Mixture of Attention Experts (MoAE) to prioritize critical semantics. |
| Outcome: | Extensive experiments on real and synthetic data show that the proposed method outperforms existing methods in retrieval and ranking tasks. |
K-order Ranking Preference Optimization for Large Language Models (2025.findings-acl)
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| Challenge: | Existing list-wise methods focus on optimizing list ranking consistency for LLMs to improve ranking abilities. |
| Approach: | They propose to extend the Plackett-Luce model to accommodate top-K ranking by extending the DPO’s Plact-Lucer model to dynamically determine appropriate K for different samples. |
| Outcome: | The proposed model can be extended to accommodate top-K ranking and improve training efficiency. |
Contextual Metric Meta-Evaluation by Measuring Local Metric Accuracy (2025.findings-naacl)
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| Challenge: | Existing approaches to metric meta-evaluation focus on general statements about absolute and relative quality of metrics across arbitrary system outputs, but in practice, metrics are applied in highly contextual settings. |
| Approach: | They propose a method for contextual metric meta-evaluation by comparing local metric accuracy. |
| Outcome: | The proposed method compares the local metric accuracy of evaluation metrics across translation, speech recognition, and ranking tasks. |
DUQGen: Effective Unsupervised Domain Adaptation of Neural Rankers by Diversifying Synthetic Query Generation (2024.naacl-long)
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| Challenge: | State-of-the-art rankers pre-trained on large task-specific training data such as MS-MARCO exhibit strong performance on various ranking tasks without domain adaptation, also called zero-shot. |
| Approach: | They propose a method to generate unsupervised domain adaptation for ranking using large-scale task-specific training data such as MS-MARCO and Wikipedia retrieval. |
| Outcome: | The proposed method outperforms all zero-shot baselines and significantly outperfies the SOTA baselines on 16 out of 18 datasets, for an average of 4% relative improvement across all datasets. |
The Niki and Julie Corpus: Collaborative Multimodal Dialogues between Humans, Robots, and Virtual Agents (L18-1)
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Ron Artstein, Jill Boberg, Alesia Gainer, Jonathan Gratch, Emmanuel Johnson, Anton Leuski, Gale Lucas, David Traum
| Challenge: | Niki and Julie corpus contains more than 600 dialogues between humans and robots . corpus includes audio and video recordings, results of ranking tasks, questionnaire responses . |
| Approach: | the corpus contains more than 600 dialogues between human participants and a robot . the dialogues are part of a collaborative item-ranking task designed to measure influence . |
| Outcome: | the corpus contains more than 600 dialogues between human participants and a robot or virtual agent . the dialogues contain conversational errors by the robot, which simulates typical of modern automated agents . |
Similarity = Value? Consultation Value-Assessment and Alignment for Personalized Search (2025.emnlp-main)
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| Challenge: | Existing methods rely on semantic similarity to align historical consultations with current queries due to the absence of ‘value’ labels, but this lacks exploration of needs in user consultations. |
| Approach: | They propose a consultation value assessment framework that evaluates historical consultations from three novel perspectives: (1) Scenario Scope Value, (2) Posterior Action Value, and (3) Time Decay Value. |
| Outcome: | The proposed model outperforms baselines on public and commercial datasets on both retrieval and ranking tasks. |
Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation (2024.findings-emnlp)
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| Challenge: | Recent work leverages the power of pretrained language models to rank news items . pointwise approaches fail to capture comparative information between items that is more effective for ranking tasks. |
| Approach: | They propose a framework for PLM-based news recommendation that integrates pointwise relevance prediction and pairwise comparisons in a scalable manner. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on the MIND and Adressa news recommendation datasets. |
Batched Self-Consistency Improves LLM Relevance Assessment and Ranking (2025.emnlp-main)
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| Challenge: | Existing work has focused on a one-by-one pointwise (PW) scoring strategy where each LLM call judges one passage at a time against the query. |
| Approach: | They propose to use batched PW methods to evaluate multiple passages per LLM call to improve efficiency and judgment quality by enabling content from multiple passage to be seen jointly. |
| Outcome: | The proposed methods improve efficiency and judgment quality by enabling content from multiple passages to be seen jointly. |