Know Your Limits: A Survey of Abstention in Large Language Models

Original Paper: https://arxiv.org/abs/2407.18418

By: Bingbing Wen, Jihan Yao, Shangbin Feng, Chenjun Xu, Yulia Tsvetkov, Bill Howe, Lucy Lu Wang

Abstract:

Abstention, the refusal of large language models (LLMs) to provide an answer, is increasingly recognized for its potential to mitigate hallucinations and enhance safety in building LLM systems. In this survey, we introduce a framework to examine abstention behavior from three perspectives: the query, the model, and human values. We review the literature on abstention methods (categorized based on the development stages of LLMs), benchmarks, and evaluation metrics, and discuss the merits and limitations of prior work. We further identify and motivate areas for future research, such as encouraging the study of abstention as a meta-capability across tasks and customizing abstention abilities based on context. In doing so, we aim to broaden the scope and impact of abstention methodologies in AI systems.

Summary Notes

Figure: Our proposed framework for abstention in language models. Starting with input query 𝐱, the query can be gauged for answerability a⁢(𝐱) and alignment with human values h⁢(𝐱). The model then generates a potential response 𝐲 based on the input 𝐱. If query conditions are not met, the model’s confidence in the response c⁢(𝐱,𝐲) is too low, or if the response’s alignment with human values h⁢(𝐱,𝐲) is too low, the system should abstain.

Introduction

Large Language Models (LLMs) like GPT-4 and ChatGPT have revolutionized natural language processing (NLP) by excelling in tasks such as question-answering, summarization, and dialogue generation.

Despite these advancements, these models often generate "hallucinated" or incorrect information, overconfident responses, or even harmful outputs.

To address this, the concept of abstention—where a model refuses to answer when uncertain—has emerged as a critical safety mechanism.

This blog post delves into a recent survey paper that provides a comprehensive framework for understanding and improving abstention in LLMs.

We'll explore the methodologies, findings, and implications of this research, and discuss potential applications and future directions.

Framework for Abstention

The survey introduces a framework that examines abstention from three perspectives: the query, the model, and human values.

This multi-faceted approach helps in organizing existing research and identifying areas for future improvements.

1. Query Perspective: This focuses on whether the input query is answerable based on its clarity, relevance, and completeness.
2. Model Perspective: This evaluates the model's confidence in its response, considering its design, training, and inherent biases.
3. Human Values Perspective: This assesses whether the query and its potential responses align with ethical standards and societal norms.

Methodologies for Encouraging Abstention

The survey categorizes abstention methodologies based on when they are applied in the LLM lifecycle: pretraining, alignment, and inference.

Pretraining Stage

Pretraining methods to promote abstention are relatively rare. One notable approach involves data augmentation to include unanswerable queries, which helps models learn when to abstain.

• Example: Training a model with a dataset that includes random or empty documents to teach it to recognize unanswerable queries.

Alignment Stage

Alignment methods involve fine-tuning the model to improve its abstention capabilities. This can be achieved through instruction tuning, where the model is trained on datasets that include explicit examples of when it should refuse to answer.

• Example: R-tuning, a technique where models are fine-tuned on refusal-aware datasets, has shown promising results in improving abstention.

Inference Stage

Inference methods are applied during the model's operation and include techniques like query processing, probing the model's inner state, uncertainty estimation, and consistency-based approaches.

• Example: Using Negative Log-Likelihood (NLL) to estimate the uncertainty of a model's response and abstaining when the uncertainty is high.

Main Findings

The survey provides several key insights:

1. Abstention Improves Safety and Reliability: Encouraging models to abstain in uncertain situations can significantly enhance their safety and reliability.
2. Task-Specific vs. General Abstention: While current methods are often task-specific, there is a need for developing abstention as a meta-capability that transcends specific tasks.
3. Challenges in Human Value Alignment: Aligning model responses with human values is complex and requires careful consideration of ethical implications.

Evaluation Benchmarks and Metrics

The survey also reviews various datasets and metrics used to evaluate abstention:

• Datasets: These include benchmarks like SQuAD2 and Natural Questions, which contain unanswerable questions to test a model's abstention capabilities.
• Metrics: Metrics such as Abstention Accuracy, Precision, and Recall are used to quantify a model's performance in terms of correctly abstaining when necessary.

Implications and Applications

The ability to abstain can have far-reaching implications across various applications:

1. Chatbots and Virtual Assistants: Enhancing the safety and reliability of conversational agents in customer service and personal assistants.
2. Medical and Legal Domains: Preventing the generation of incorrect or harmful information in critical fields where accuracy is paramount.
3. Content Moderation: Improving the ability of models to avoid generating or amplifying toxic and harmful content.

Conclusion

Abstention in LLMs is a promising area of research that addresses the critical need for safety and reliability in AI systems.

By understanding and improving abstention capabilities, we can develop more robust and trustworthy AI applications.

As the survey highlights, future research should focus on generalizing abstention across tasks and domains, refining evaluation methods, and aligning abstention mechanisms with human values.

By doing so, we can ensure that LLMs not only perform well but also act responsibly and ethically in diverse real-world scenarios.

Quote from the Research Paper:

"Abstention includes a spectrum of behaviors ranging from partial to full abstention; for example, expressing uncertainty, providing conflicting conclusions, or refusing to respond due to potential harm are all forms of abstention."

As we continue to push the boundaries of what LLMs can achieve, understanding their limits and knowing when to hold back will be crucial in building AI systems that we can trust and rely on.