PhD Studentship: Reliable and Generalisable Knowledge Editing in Large Language Models

About the Project Primary Supervisor - Dr Farhana Ferdousi Liza Emerging techniques in model editing, also discussed in the literature as knowledge editing and memory editing, offer promising approaches for correcting erroneous or harmful information, incorporating specialised knowledge, and improving the usability, security, and accountability of large language models (LLMs). The overall aim of this PhD project is to develop and evaluate methods for reliable, localised, and generalisable knowledge updating in l...

Opportunity details

About the Project Primary Supervisor - Dr Farhana Ferdousi Liza Emerging techniques in model editing, also discussed in the literature as knowledge editing and memory editing, offer promising approaches for correcting erroneous or harmful information, incorporating specialised knowledge, and improving the usability, security, and accountability of large language models (LLMs). The overall aim of this PhD project is to develop and evaluate methods for reliable, localised, and generalisable knowledge updating in large language models. Using accessible open-weight language models suited to available computational resources, including NVIDIA RTX 6000 Ada 48GB GPUs and UEA high-performance computing (HPC) facilities, you will adapt established editing methods, such as ROME and MEMIT, and compare them with retrieval-augmented generation (RAG), external memory mechanisms, and conventional fine-tuning. Experiments will investigate how models can incorporate new information or knowledge, correct inaccurate information, and remove outdated or unwanted information while minimising unintended changes to existing capabilities. Evaluation will use established factual model-editing benchmarks, such as CounterFact and zsRE, supplemented where appropriate by a domain-specific dataset. Performance will be assessed in terms of editing efficacy (whether the target knowledge is successfully updated), generalisation (whether updates remain effective across paraphrases and related queries), locality (whether unrelated knowledge and capabilities are preserved), and retention (whether updates persist following subsequent edits or conflicting information). Further evaluations will examine factual consistency, general reasoning, and robustness to adversarial prompts. Where feasible, the projec...

Eligibility

The standard minimum entry requirement is 2:1 in Computer Science. Mode of Study Full-time Start Date 1 October 2027 Additional Funding Information This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition fees and an annual stipend for 3 years. The current UEA / institutional PhD stipend rate is £21,805 and will be subject to any agreed inflationary uplift for 2027/28.

Requirements

The standard minimum entry requirement is 2:1 in Computer Science. Mode of Study Full-time Start Date 1 October 2027 Additional Funding Information This PhD project is in a competition for a Faculty of Science funded studentship. Funding is available to UK applicants and comprises ‘home’ tuition fees and an annual stipend for 3 years. The current UEA / institutional PhD stipend rate is £21,805 and will be subject to any agreed inflationary uplift for 2027/28.

Funding and benefits

Funding eligibility: UK Students. Funding amount: £21,805.

How to apply

The project will contribute practical methods and empirical evidence for developing reliable, trustworthy, and accountable LLMs for knowledge-intensive applications.

Open the official application information

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