Papers by Fengyu Cai
MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers (2025.emnlp-main)
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| Challenge: | Different retrievers offer distinct, often complementary signals, but they are not optimal for all queries. |
| Approach: | They propose a zero-shot, weighted combination of heterogeneous retrievers . they validate this intuition by incorporating specialized non-oracle human information sources . |
| Outcome: | Experiments show that a mixture of heterogeneous retrievers outperforms each retriever and larger models by +10.8% and +3.9% on average. |
CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval (2026.acl-long)
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Jiahui Geng, Fengyu Cai, Shaobo Cui, Qing Li, Liangwei Chen, Chenyang Lyu, Haonan Li, Derui Zhu, Alexander Pretschner, Heinz Koeppl, Fakhri Karray
| Challenge: | Existing benchmarks focus on functional relevance while neglecting code quality. |
| Approach: | They propose a multilingual benchmark to evaluate quality-aware code retrieval . they include fine-grained quality annotations over 42,725 queries and 134,907 code snippets . |
| Outcome: | The proposed benchmarks show that state-of-the-art models fail to separate buggy or insecure code from robust counterparts. |
GeoHard: Towards Measuring Class-wise Hardness through Modelling Class Semantics (2024.findings-acl)
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| Challenge: | Recent advances in measuring hardness-wise properties of data guide language models in sample selection within low-resource scenarios. |
| Approach: | They propose to use class-wise hardness to measure class-specific properties of data in the semantic embedding space by modeling class geometry in the . semantic embeddining space. |
| Outcome: | The proposed method surpasses instance-level metrics by over 59 percent on Pearson‘s correlation on measuring class-wise hardness. |
A Survey of Confidence Estimation and Calibration in Large Language Models (2024.naacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities across a wide range of tasks in various domains, but they can be unreliable due to factual errors in their generations. |
| Approach: | They summarize recent advances in LLM confidence estimation and calibration and outline their main lessons learned. |
| Outcome: | The proposed methods can be used to assess the reliability of models and to calibrate them across tasks. |
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems (2021.emnlp-main)
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| Challenge: | Large-scale pre-trained language models have shown promising results for few-shot learning in task-oriented dialog (ToD) systems. |
| Approach: | They propose a self-training approach that iteratively labels the most confident unlabeled data to train a stronger Student model. |
| Outcome: | The proposed approach improves state-of-the-art pre-trained models in few-shot learning scenarios for task-oriented dialog (ToD) systems when only a small number of labeled data are available. |
MixGR: Enhancing Retriever Generalization for Scientific Domain through Complementary Granularity (2024.emnlp-main)
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| Challenge: | Recent studies show the importance of document retrieval in the scientific domain. |
| Approach: | They propose a zero-shot approach to measure query-document similarity using atomic components in queries and documents to combine them into a united score. |
| Outcome: | The proposed approach outperforms previous document retrieval methods by 24.7%, 9.8%, and 6.9% on nDCG@5 with unsupervised, supervised, and LLM-based retrievers. |