Papers by Craig MacDonald
TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation (2024.findings-emnlp)
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| Challenge: | Existing retrievers are not perfect and often include irrelevant documents in the retrieved set. |
| Approach: | They propose to construct knowledge-grounded reasoning chains from retrieved documents to integrate supporting evidence into RAG models. |
| Outcome: | The proposed model achieves an average performance improvement of 14.03% on three multi-hop QA datasets. |
REANO: Optimising Retrieval-Augmented Reader Models through Knowledge Graph Generation (2024.acl-long)
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| Challenge: | Existing knowledge graphs suffer from incompleteness and lack information critical for answering given questions. |
| Approach: | They propose to enhance the open domain question answering model with a knowledge graph generation module that generates KGs from the passages and an answer predictor. |
| Outcome: | The proposed model improves the exact match score by 2.7% on the EntityQuestion dataset, with an average improvement of 1.8% across all the datasets. |
KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation (2025.acl-long)
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| Challenge: | Iterative retrieval-augmented generation models are difficult to use for multihop question answering (QA) . their retrieval processes can be disrupted by irrelevant documents or factually inaccurate chain-of-thoughts . |
| Approach: | They propose a knowledge-driven iterative retriever model that decomposes documents into knowledge triples and performs iterativ retrieval with these triples to enable a factually reliable retrieval process. |
| Outcome: | The proposed model outperforms existing iRAG models with an average improvement of 9.40% in R@3 and 5.14% in F1 on multi-hop QA datasets. |
Multi-Task Learning of Query Generation and Classification for Generative Conversational Question Rewriting (2023.findings-emnlp)
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| Challenge: | Existing approaches to identifying ambiguous questions as part of a conversation have not addressed this challenge. |
| Approach: | They propose a multi-task learning approach that uses a text generation model for question rewriting and classification. |
| Outcome: | The proposed approach outperforms single-task learning baselines on three LIF test sets. |