Papers with ranking problem
CoAlign: Uncertainty Calibration of LLM for Geospatial Repartition (2025.acl-industry)
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| Challenge: | Existing methods to optimize geospatial repartition rely on manual adjustments by experts or algorithmic optimization using limited offline operational metrics. |
| Approach: | They propose a framework that calibrates LLM uncertainty to enable robust geospatial repartition by integrating historical data with LLM-generated candidates. |
| Outcome: | The proposed framework calibrates LLM uncertainty to enable robust geospatial repartition by integrating historical data with LLM-generated candidates. |
Dialogue Response Ranking Training with Large-Scale Human Feedback Data (2020.emnlp-main)
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| Challenge: | Existing open-domain dialog models can minimize the perplexity of target human responses . however, some human responses are more engaging than others, spawning more followup interactions . |
| Approach: | They train open-domain dialog models to minimize perplexity of target human responses . they use social media feedback data to train models to predict engaging dialog turns . |
| Outcome: | The proposed model outperforms existing models on 133M human feedback pairs . it also outperformed the conventional dialog perplexity baseline model . |
Boosting Self-Consistency with Ranking (2026.acl-srw)
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Maria Marina, Daniil Moskovskiy, Sergey Pletenev, Mikhail Salnikov, Alexander Panchenko, Viktor Moskvoretskii
| Challenge: | Existing approaches to improve performance of large language models include self-consistency, RISC, extended reasoning, and iterative self-correction. |
| Approach: | They propose a test-time scaling technique that uses multiple features to score candidate answers in self-consistency as a ranking problem. |
| Outcome: | The proposed method achieves better accuracy-efficiency trade-off than standard self-consistency and strong baselines on three datasets. |
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking (2021.emnlp-main)
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| Challenge: | Existing methods require training millions of architectures to estimate the accuracy of the search results. |
| Approach: | They propose a performance ranking method (RankNAS) that uses pairwise ranking and search space pruning to enlarge the search space. |
| Outcome: | The proposed method significantly accelerates NAS through pairwise ranking and search space pruning. |
FLIN: A Flexible Natural Language Interface for Web Navigation (2021.naacl-main)
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| Challenge: | Existing semantic parsing and slot-filling techniques cannot adapt to many different websites without being constantly re-trained. |
| Approach: | They propose a natural language interface for web navigation that maps user commands to concept-level actions rather than low-level UI actions. |
| Outcome: | The proposed interface can adapt to new websites in a given domain. |
Choosing Transfer Languages for Cross-Lingual Learning (P19-1)
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Yu-Hsiang Lin, Chian-Yu Chen, Jean Lee, Zirui Li, Yuyan Zhang, Mengzhou Xia, Shruti Rijhwani, Junxian He, Zhisong Zhang, Xuezhe Ma, Antonios Anastasopoulos, Patrick Littell, Graham Neubig
| Challenge: | Cross-lingual transfer is a useful tool for improving performance of natural language processing (NLP) on low-resource languages. |
| Approach: | They propose to use cross-lingual transfer to improve accuracy of low-resource languages . they build models that consider features to perform prediction on such languages based on ranking problem . |
| Outcome: | The proposed model predicts good transfer languages much better than baselines considering single features in isolation. |
Generating Hashtags for Short-form Videos with Guided Signals (2023.acl-long)
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Tiezheng Yu, Hanchao Yu, Davis Liang, Yuning Mao, Shaoliang Nie, Po-Yao Huang, Madian Khabsa, Pascale Fung, Yi-Chia Wang
| Challenge: | Short-form video hashtag recommendation (SVHR) is a classification or ranking problem that selects hashtags from a set of limited candidates. |
| Approach: | They propose a short-form video hashtag recommendation task that better represents how hashtags are created naturally by retrieving relevant hashtags from a large-scale hashtag pool as extra guidance signals. |
| Outcome: | The proposed model outperforms strong classification baselines on two short-form video datasets and the guidance signals boost the performance by 8.11 and 2.17 absolute ROUGE-1 scores on average. |
SELFOOD: Self-Supervised Out-Of-Distribution Detection via Learning to Rank (2023.findings-emnlp)
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| Challenge: | Existing methods for supervised OOD detection require expensive manual annotation of in-distribution and OOD samples. |
| Approach: | They propose a self-supervised OOD detection method that requires only in-distribution samples as supervision. |
| Outcome: | Experiments with multiple classifiers on coarse- and fine-grained datasets show the proposed method performs well in both coarse-and fine-grid settings. |
Interpreting Topic Models in Byte-Pair Encoding Space (2025.coling-main)
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| Challenge: | Byte-pair encoding (BPE) is a popular method of tokenizing valid words onto a token space V b with a predetermined fixed size, and handling out-of-vocabulary words, breaking words into smaller tokens. |
| Approach: | They propose to interpret the recovery of valid words from these tokens as a ranking problem and apply existing evaluation measures to topic sets. |
| Outcome: | The proposed model interprets the recovery of valid words from these tokens as a ranking problem and applies existing evaluation measures. |
A Prioritization Model for Suicidality Risk Assessment (2020.acl-main)
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| Challenge: | Existing methods for predicting suicide have failed for fifty years . however, with the advent of machine learning, the problem is gaining momentum . |
| Approach: | They propose a method that jointly ranks individuals and their social media posts to improve suicide risk assessment. |
| Outcome: | The proposed approach outperforms existing methods in a case study using expert-annotated test collection. |
Aggregating Multiple Heuristic Signals as Supervision for Unsupervised Automated Essay Scoring (2023.acl-long)
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| Challenge: | Automated Essay Scoring (AES) aims to evaluate the quality score of input essays without human intervention. |
| Approach: | They propose an unsupervised approach to evaluate the quality of input essays . they use multiple heuristic quality signals as pseudo-groundtruths to train a neural AES model . |
| Outcome: | The proposed approach achieves state-of-the-art performance on eight prompts of ASPA dataset compared with previous unsupervised methods . |
Stop Hardening Everything: A Training-Free Neuron-Level Defense for Neural Ranking Models (2026.acl-long)
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| Challenge: | Existing defenses for neural ranking models are data-centric and require retraining and adversarial data generation. |
| Approach: | They propose a model-centric defense that addresses vulnerability at its architectural source without costly retraining or adversarial data generation. |
| Outcome: | The proposed approach outperforms state-of-the-art models on MS MARCO and TREC 19 while maintaining strong performance on clean data. |
RankPrompt: Step-by-Step Comparisons Make Language Models Better Reasoners (2024.lrec-main)
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| Challenge: | Existing solutions to reasoning tasks require extensive human annotations or fail in scenarios with inconsistent responses. |
| Approach: | They propose a new method that enables LLMs to self-rank their responses without additional resources. |
| Outcome: | The proposed method improves reasoning performance of ChatGPT and GPT-4 with 13% improvement over existing methods. |
Explanation Quality Assessment as Ranking with Listwise Rewards (2026.findings-acl)
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| Challenge: | a new approach to explanation quality assessment is to rank explanations by relative quality . standard reward objectives do not preserve graded distinctions well enough for policy optimization . |
| Approach: | They reformulate explanation quality assessment as a ranking problem instead of a generation problem . they train listwise and pairwise ranking models to preserve ordinal structure . |
| Outcome: | The proposed model outperforms regression on score separation and performance on listwise and pairwise models. |