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Craig Macdonald

7 accepted papers

2025

KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation

ACL 2025long

Iterative retrieval-augmented generation (iRAG) models offer an effective approach for multihop question answering (QA). However, their retrieval processes face two key challenges: (1) they can be disrupted by irrelevant documents or factually inaccurate chain-of-thoughts; (2) their retrievers are n…

2024

REANO: Optimising Retrieval-Augmented Reader Models through Knowledge Graph Generation

ACL 2024long

Open domain question answering (ODQA) aims to answer questions with knowledge from an external corpus. Fusion-in-Decoder (FiD) is an effective retrieval-augmented reader model to address this task. Given that FiD independently encodes passages, which overlooks the semantic relationships between pass…

2024

TRACE the Evidence: Constructing Knowledge-Grounded Reasoning Chains for Retrieval-Augmented Generation

EMNLP 2024finding

Retrieval-augmented generation (RAG) offers an effective approach for addressing question answering (QA) tasks. However, the imperfections of the retrievers in RAG models often result in the retrieval of irrelevant information, which could introduce noise and degrade the performance, especially when…

2024

gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling (Extended Abstract)

IJCAI 2024poster

Sequential recommendation models predict the next item in a sequence of user-item interactions, akin to how language models predict the next tokens. These models often adapt language model architectures, treating item IDs as if they were token IDs. However, the number of potential items in recommend…

Cited by 40SourcePDFScholar
2023

Effective Contrastive Weighting for Dense Query Expansion

ACL 2023long

Verbatim queries submitted to search engines often do not sufficiently describe the user’s search intent. Pseudo-relevance feedback (PRF) techniques, which modify a query’srepresentation using the top-ranked documents, have been shown to overcome such inadequacies and improve retrieval effectiveness…

2023

Multi-Task Learning of Query Generation and Classification for Generative Conversational Question Rewriting

EMNLP 2023long findings

In conversational search settings, users ask questions and receive answers as part of a conversation. The ambiguity in the questions is a common challenge, which can be effectively addressed by leveraging contextual information from the conversation history. In this context, determining topic contin…

Cited by 0SourceScholar
2022

monoQA: Multi-Task Learning of Reranking and Answer Extraction for Open-Retrieval Conversational Question Answering

EMNLP 2022main

To address the Conversational Question Answering (ORConvQA) task, previous work has considered an effective three-stage architecture, consisting of a retriever, a reranker, and a reader to extract the answers. In order to effectively answer the users’ questions, a number of existing approaches have…

Cited by 8SourcePDFScholar