Papers by Heinz Koeppl
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. |
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. |
ECOLA: Enhancing Temporal Knowledge Embeddings with Contextualized Language Representations (2023.findings-acl)
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Zhen Han, Ruotong Liao, Jindong Gu, Yao Zhang, Zifeng Ding, Yujia Gu, Heinz Koeppl, Hinrich Schütze, Volker Tresp
| Challenge: | Existing enhancement approaches cannot be applied to temporal knowledge graphs (tKGs) existing enhancement approaches assume knowledge embedding is time-independent, whereas entity embedded in tKG models evolves . |
| Approach: | They propose to use textual data to enhance temporal knowledge embedding by Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations (ECOLA) to evaluate ECOLA, they introduce three new datasets for training and evaluation. |
| Outcome: | The proposed model significantly improves Hits@1 on the link prediction task. |