All Outputs (2)

Context driven multi-query resolution using LLM-RAG to support the revision of explainability needs. (2025)
Presentation / Conference Contribution
JAYAWARDENA, L., LIRET, A., WIRATUNGA, N., NKISI-ORJI, I. and FLEISCH, B. [2025]. Context driven multi-query resolution using LLM-RAG to support the revision of explainability needs. In Proceedings of the 33rd International conference on case-based reasoning (ICCBR 2025), 30 June - 3 July 2025, Biarritz, France. Lecture notes in computer science, [volume to be confirmed]. Cham: Springer [online], (accepted).

The revision step in the Case-Based Reasoning (CBR) cycle ensures that cases are adaptable and that updates can be integrated meaningfully based on evaluation metrics. However, the effectiveness of this step heavily depends on how new knowledge is ac... Read More about Context driven multi-query resolution using LLM-RAG to support the revision of explainability needs..

AlignLLM: alignment-based evaluation using ensemble of LLMs-as-judges for Q &A. (2025)
Presentation / Conference Contribution
ABEYRATNE, R., WIRATUNGA, N., MARTIN, K., NKISI-ORJ, I. and JAYAWARDENA, L. [2025]. AlignLLM: alignment-based evaluation using ensemble of LLMs-as judges for Q&A. In Case-based reasoning research and development: proceedings of the 33rd International conference on case-based reasoning 2025 (ICCBR 2025), 30 June - 3 July 2025, Biarritz, France. Lecture notes in computer science (LNCS), TBC. Cham: Springer [online], (forthcoming).

Evaluating responses generated by large language models (LLMs) is challenging in the absence of ground-truth knowledge, particularly in specialised domains such as law. Increasingly, LLMs themselves are used to evaluate the responses they generate; h... Read More about AlignLLM: alignment-based evaluation using ensemble of LLMs-as-judges for Q &A..