Papers with Ranking
Sentence Suggestion of Japanese Functional Expressions for Chinese-speaking Learners (P18-4)
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| Challenge: | a large number of Chinese characters are commonly used both in Chinese and Japanese. |
| Approach: | They propose a computer-assisted learning system for Chinese-speaking learners of Japanese as a second language (JSL) they use a free Japanese morphological analyzer MeCab to learn Japanese functional expressions with suggestion of appropriate example sentences. |
| Outcome: | The proposed system automatically recognizes Japanese functional expressions using a free Japanese morphological analyzer and is retrained on a new conditional random field model. |
StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoning (2022.emnlp-main)
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| Challenge: | Existing automatic story evaluation methods place a premium on story lexical level coherence, deviating from human preference. |
| Approach: | They propose a novel Story Evaluation method that mimics human preference when judging a story . the model is based on a well-annotated dataset and a longformer-encoder-decoder . |
| Outcome: | The proposed method is applicable to machine-generated and human-written stories. |
Guided Profile Generation Improves Personalization with Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing approaches to personalization with LLMs rely on sparse and complex personal contexts, resulting in incomplete interpretation. |
| Approach: | They propose a general method to generate personal profiles in natural language that extracts important, distinctive features from the personal context into concise, descriptive sentences. |
| Outcome: | The proposed method improves personalization ability across different tasks, for example, it increases 37% accuracy in predicting personal preference compared to directly feeding the LLMs with raw personal context. |
Beyond Sampling: Self-Sorting for Long-Context Ranking (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) remain unstable on long-context ranking. |
| Approach: | They propose a method that fuses explicit within-list positions with implicit cross-list preferences to score entities and return a top-k set. |
| Outcome: | Experimental results show that large language models remain unstable on long-context ranking . |
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. |
Extended Parallel Corpus for Amharic-English Machine Translation (2022.lrec-1)
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| Challenge: | Existing approaches to automate the complex task of translation are tedious and expensive. |
| Approach: | They describe acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. |
| Outcome: | The proposed corpus outperforms statistical machine translation models by six to seven BLEU points . the results show that the subword models outperformed word-based models by three to four BLUE points compared with the word-base models . |
PiCSAR: Probabilistic Confidence Selection and Ranking for Reasoning Chains (2026.findings-acl)
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Joshua Ong Jun Leang, Zheng Zhao, Aryo Pradipta Gema, Sohee Yang, Wai-Chung Kwan, Xuanli He, Wenda Li, Pasquale Minervini, Eleonora Giunchiglia, Shay B Cohen
| Challenge: | Recent studies show that large reasoning models (LLMs) achieve strong performance on complex reasoning tasks. |
| Approach: | They propose a method that scores each candidate generation using the joint log-likelihood of the reasoning and final answer. |
| Outcome: | The proposed method outperforms baselines with 2x fewer samples in 20 out of 25 comparisons. |