When to Speak, When to Abstain: Contrastive Decoding with Abstention
Hyuhng Joon Kim, Youna Kim, Sang-goo Lee, Taeuk Kim
2024-12-18
Summary
This paper talks about a new method called Contrastive Decoding with Abstention (CDA), which helps large language models (LLMs) decide when to provide answers and when to refrain from responding if they lack relevant knowledge.
What's the problem?
Large language models are great at answering questions, but they sometimes make mistakes when they don't have enough information. This can lead to unreliable answers, especially in important situations where accuracy is crucial. The issue is that these models often try to answer even when they shouldn't, which can cause problems like providing incorrect or misleading information.
What's the solution?
CDA addresses this problem by allowing LLMs to evaluate their own knowledge before responding. If the model has enough relevant information, it generates an answer; if not, it chooses to abstain from answering. This method helps improve the reliability of the responses by ensuring that the model only speaks when it has something useful to say. The researchers tested CDA with several LLMs on different question-answering tasks and found that it works well in both generating accurate answers and knowing when to stay silent.
Why it matters?
This research is important because it enhances the trustworthiness of AI systems. By improving how LLMs handle situations where they lack knowledge, CDA can help prevent misinformation and maintain user confidence in AI applications, especially in critical areas like healthcare, law, and education.
Abstract
Large Language Models (LLMs) demonstrate exceptional performance across diverse tasks by leveraging both pre-trained knowledge (i.e., parametric knowledge) and external knowledge (i.e., contextual knowledge). While substantial efforts have been made to leverage both forms of knowledge, scenarios in which the model lacks any relevant knowledge remain underexplored. Such limitations can result in issues like hallucination, causing reduced reliability and potential risks in high-stakes applications. To address such limitations, this paper extends the task scope to encompass cases where the user's request cannot be fulfilled due to the lack of relevant knowledge. To this end, we introduce Contrastive Decoding with Abstention (CDA), a training-free decoding method that empowers LLMs to generate responses when relevant knowledge is available and to abstain otherwise. CDA evaluates the relevance of each knowledge for a given query, adaptively determining which knowledge to prioritize or which to completely ignore. Extensive experiments with four LLMs on three question-answering datasets demonstrate that CDA can effectively perform accurate generation and abstention simultaneously. These findings highlight CDA's potential to broaden the applicability of LLMs, enhancing reliability and preserving user trust.