Research output per year
Research output per year
Search results
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2024
Analysing the residual stream of language models under knowledge conflicts
Zhao, Y., Du, X., Hong, G., Gema, A. P., Devoto, A., Wang, H., He, X., Wong, K-F. & Minervini, P., 9 Oct 2024, (Accepted/In press) Proceedings of the 38th Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation (NeurIPS), Vol. 37. p. 1-12 12 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Edinburgh Clinical NLP at MEDIQA-CORR 2024: Guiding large language models with hints
Gema, A. P., Lee, C., Minervini, P., Daines, L., Simpson, T. I. & Alex, B., 28 May 2024, ArXiv.Research output: Working paper › Preprint
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Evaluating the adversarial robustness of retrieval-based in-context learning for large kanguage models
Yu, S. C. L., He, J., Minervini, P. & Pan, J. Z., 24 May 2024, ArXiv.Research output: Working paper › Preprint
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The hallucinations leaderboard: An open effort to measure hallucinations in large language models
Hong, G., Gema, A. P., Saxena, R., Du, X., Nie, P., Zhao, Y., Perez-Beltrachini, L., Ryabinin, M., He, X. & Minervini, P., 8 Apr 2024, ArXiv.Research output: Working paper › Preprint
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Edinburgh Clinical NLP at SemEval-2024 Task 2: Fine-tune your model unless you have access to GPT-4
Gema, A. P., Hong, G., Minervini, P., Daines, L. & Alex, B., 30 Mar 2024, ArXiv.Research output: Working paper › Preprint
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2023
AsyLex: A Dataset for Legal Language Processing of Refugee Claims
Barale, C., Klaisoongnoen, M., Minervini, P., Rovatsos, M. & Bhuta, N., 7 Dec 2023, Proceedings of the Natural Legal Language Processing Workshop (NLLP 23). Association for Computational Linguistics (ACL), p. 244–257 14 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open AccessFile -
Parameter-efficient fine-tuning of LLaMA for the clinical domain
Gema, A. P., Minervini, P., Daines, L., Hope, T. & Alex, B., 6 Jul 2023.Research output: Working paper › Preprint
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Knowledge graph embeddings in the biomedical domain: Are they useful? A look at link prediction, rule learning, and downstream polypharmacy tasks
Gema, A. P., Grabarczyk, D., Wulf, W. D., Borole, P., Alfaro, J. A., Minervini, P., Vergari, A. & Rajan, A., 31 May 2023, ArXiv.Research output: Working paper › Preprint
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