Papers by Jonas Golde
Question Decomposition for Retrieval-Augmented Generation (2025.acl-srw)
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| Challenge: | Retrieval-augmented generation (RAG) is effective for question answering tasks . multi-hop questions, such as "Which company among NVIDIA, Apple, and Google made the biggest profit in 2023?" challenge RAG because relevant facts are often distributed across multiple documents . |
| Approach: | They propose a pipeline that incorporates question decomposition to ground large language models in verifiable external sources. |
| Outcome: | The proposed approach improves retrieval and answer accuracy over standard RAG . multi-hop questions often require multiple documents to support the model . |
Familarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Data (2025.naacl-long)
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| Challenge: | Current research relies on large synthetic datasets to train zero-shot named entity recognition models. |
| Approach: | They propose a metric that captures the semantic similarity between entity types in training and evaluation to estimate label shift. |
| Outcome: | The proposed metric captures semantic similarity between entity types in training and evaluation, and their frequency in training data to provide an estimate of label shift. |
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition (2024.eacl-long)
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| Challenge: | Few-shot named entity recognition (NER) uses only a few annotated examples to identify named entities within text. |
| Approach: | They propose to leverage natural language descriptions of each entity type to perform few-shot named entity recognition. |
| Outcome: | The proposed model learns to interpret verbalized descriptions of entities using natural language descriptions of their types and their verbalizations. |
Hierarchical Text Classification with LLM-Refined Taxonomies (2026.eacl-long)
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| Challenge: | Hierarchical text classification (HTC) relies on taxonomies that organize labels into structured hierarchies, but many real-world taxonomies introduce ambiguities, such as identical leaf names under similar parent nodes, which prevent language models from learning clear decision boundaries. |
| Approach: | They propose a framework that uses large language models to transform entire taxonomies through operations such as renaming, merging, splitting, and reordering to better match the semantics encoded by LMs. |
| Outcome: | The proposed framework outperforms human-curated taxonomies in three HTC benchmarks and shows that it aligns better with the model's actual confusion patterns. |
FiNERweb: Datasets and Artifacts for Scalable Multilingual Named Entity Recognition (2026.findings-eacl)
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| Challenge: | Named entity recognition (NER) is the task of identifying tokens that belong to a predefined set of classes such as "person" or "location" |
| Approach: | They propose a dataset-creation pipeline that scales the teacher-student paradigm to 91 languages and 25 scripts. |
| Outcome: | The proposed model achieves comparable or improved performance in English, Thai, and Swahili despite being trained on 19x less data than strong baselines. |
PECC: Problem Extraction and Coding Challenges (2024.lrec-main)
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| Challenge: | Existing benchmarks evaluate tasks in isolation, yet the extent to which LLMs can understand prose-style tasks, identify the underlying problems, and then generate appropriate code solutions remains mostly unexplored. |
| Approach: | They propose a benchmark derived from Advent Of Code challenges and Project Euler, which requires LLMs to interpret narrative-embedded problems, extract requirements, and generate executable code. |
| Outcome: | The proposed benchmarks show that LLMs can understand prose-style tasks, identify underlying problems, and generate appropriate code solutions in a variety of tasks. |
Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMs (2023.emnlp-demo)
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| Challenge: | Recent research addresses the bottleneck of producing labeled training data for NLP tasks. |
| Approach: | They propose a method that generates labeled data that can be used to train a downstream NLP model. |
| Outcome: | The proposed model enables an LLM to generate labeled data that can be used to train a downstream NLP model. |