Papers by Craig Macdonald
SPARKLE: A Structured and Plug-and-play Agentic Retrieval Policy for Adaptive RAG Models (2026.acl-long)
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| Challenge: | Existing methods for integrating external knowledge rely on frozen large language models without explicit supervision or require costly LLM finetuning. |
| Approach: | They propose a structured and plug-and-play agentic retrieval policy with an additional proxy model to control the retrieval process. |
| Outcome: | Experiments on three in-domain and four out-of-domain QA benchmarks show that SPARKLE outperforms state-of the-art adaptive RAG models, achieving average improvements of 9.17% and 2.85%, respectively. |
Effective Contrastive Weighting for Dense Query Expansion (2023.acl-long)
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| Challenge: | Verbatim queries that do not adequately express the user's search intent are often lexical inadequacies. |
| Approach: | They propose a contrastive weighting model that learns to select the most useful expansion embeddings for semantic search. |
| Outcome: | The proposed model outperforms existing methods while maintaining its efficiency. |
monoQA: Multi-Task Learning of Reranking and Answer Extraction for Open-Retrieval Conversational Question Answering (2022.emnlp-main)
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| Challenge: | Existing approaches to the Conversational Question Answering task have used multi-task learning to solve the task. |
| Approach: | They propose to use multi-task learning to improve the ORConvQA task by sharing the reranker and reader’s learned structure in a generative model. |
| Outcome: | The proposed model outperforms baseline models on the OR-QuAC and OR-CoQA datasets and significantly outperformed existing strong baseline models. |