CSS: Contrastive Semantic Similarity for Uncertainty Quantification of LLMs

• URL: http://arxiv.org/abs/2406.03158v1
• Date: Wed, 5 Jun 2024 11:35:44 GMT
• Title: CSS: Contrastive Semantic Similarity for Uncertainty Quantification of LLMs
• Authors: Shuang Ao, Stefan Rueger, Advaith Siddharthan,
• Abstract summary: We propose Contrastive Semantic Similarity, a module to obtain similarity features for measuring uncertainty for text pairs. We conduct extensive experiments with three large language models (LLMs) on several benchmark question-answering datasets. Results show that our proposed method performs better in estimating reliable responses of LLMs than comparable baselines.
• Score: 1.515687944002438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the impressive capability of large language models (LLMs), knowing when to trust their generations remains an open challenge. The recent literature on uncertainty quantification of natural language generation (NLG) utilises a conventional natural language inference (NLI) classifier to measure the semantic dispersion of LLMs responses. These studies employ logits of NLI classifier for semantic clustering to estimate uncertainty. However, logits represent the probability of the predicted class and barely contain feature information for potential clustering. Alternatively, CLIP (Contrastive Language-Image Pre-training) performs impressively in extracting image-text pair features and measuring their similarity. To extend its usability, we propose Contrastive Semantic Similarity, the CLIP-based feature extraction module to obtain similarity features for measuring uncertainty for text pairs. We apply this method to selective NLG, which detects and rejects unreliable generations for better trustworthiness of LLMs. We conduct extensive experiments with three LLMs on several benchmark question-answering datasets with comprehensive evaluation metrics. Results show that our proposed method performs better in estimating reliable responses of LLMs than comparable baselines. Results show that our proposed method performs better in estimating reliable responses of LLMs than comparable baselines. The code are available at \url{https://github.com/AoShuang92/css_uq_llms}.

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