Faithfulness metric fusion: Improving the evaluation of LLM trustworthiness across domains
This work addresses the need for more accurate faithfulness evaluation in LLMs, which is incremental as it improves existing metrics rather than introducing a new paradigm.
The authors tackled the problem of evaluating faithfulness in Large Language Models (LLMs) by fusing elementary metrics using a tree-based model, resulting in a fused metric that correlates more strongly with human judgments across domains.
We present a methodology for improving the accuracy of faithfulness evaluation in Large Language Models (LLMs). The proposed methodology is based on the combination of elementary faithfulness metrics into a combined (fused) metric, for the purpose of improving the faithfulness of LLM outputs. The proposed strategy for metric fusion deploys a tree-based model to identify the importance of each metric, which is driven by the integration of human judgements evaluating the faithfulness of LLM responses. This fused metric is demonstrated to correlate more strongly with human judgements across all tested domains for faithfulness. Improving the ability to evaluate the faithfulness of LLMs, allows for greater confidence to be placed within models, allowing for their implementation in a greater diversity of scenarios. Additionally, we homogenise a collection of datasets across question answering and dialogue-based domains and implement human judgements and LLM responses within this dataset, allowing for the reproduction and trialling of faithfulness evaluation across domains.