Papers by Puxuan Yu
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. |
Language Concept Erasure for Language-invariant Dense Retrieval (2024.emnlp-main)
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| Challenge: | Multilingual models aim for language-invariant representations but still encode language identity. |
| Approach: | They propose a multi-task learning framework that induces language invariance in multilingual retrieval by reducing language-specific signals in the embedding space. |
| Outcome: | The proposed learning framework improves language-invariant dense retrieval over baselines on English retrieval data and general multilingual corpora. |
Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers (2026.findings-acl)
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Qingcheng Zeng, Yuheng Lu, Zeqi Zhou, Heli Qi, Puxuan Yu, Fuheng Zhao, Hitomi Yanaka, Weihao Xuan, Naoto Yokoya
| Challenge: | a new study examines the performance of code-switching IR in monolingual contexts . code-witching is a pervasive linguistic phenomenon in global communication . |
| Approach: | They propose a benchmark to evaluate code-switching IR in monolingual contexts . they propose CS-MTEB, which measures performance declines of up to 27% . |
| Outcome: | The proposed benchmark shows that code-switching performance is degraded by 27% . the proposed benchmark is based on a dataset of mixed-language queries . |