arXiv:2410.14582v1 Announce Type: new Abstract: Large language models (LLMs) could be valuable personal AI agents across various domains, provided they can precisely follow user instructions. However, recent studies have shown significant limitations in LLMs’ instruction-following capabilities, raising concerns about their reliability in high-stakes applications. Accurately estimating LLMs’ uncertainty in adhering to instructions is critical to mitigating deployment risks. We present, to our knowledge, the first systematic evaluation of the uncertainty estimation abilities of LLMs in the context of instruction-following. Our study identifies key challenges with existing instruction-following benchmarks, where multiple factors are entangled with uncertainty stems from instruction-following, complicating the isolation and comparison across methods and models. To address these issues, we introduce a controlled evaluation setup with two benchmark versions of data, enabling a comprehensive comparison of uncertainty estimation methods under various conditions. Our findings show that existing uncertainty methods struggle, particularly when models make subtle errors in instruction following. While internal model states provide some improvement, they remain inadequate in more complex scenarios. The insights from our controlled evaluation setups provide a crucial understanding of LLMs’ limitations and potential for uncertainty estimation in instruction-following tasks, paving the way for more trustworthy AI agents.
The article titled “Large Language Models’ Uncertainty Estimation in Instruction-Following: A Systematic Evaluation” addresses the growing importance of large language models (LLMs) as personal AI agents in various domains. However, recent studies have highlighted significant limitations in LLMs’ ability to accurately follow user instructions, raising concerns about their reliability in high-stakes applications. To mitigate deployment risks, accurately estimating LLMs’ uncertainty in adhering to instructions is crucial. This article presents the first systematic evaluation of LLMs’ uncertainty estimation abilities specifically in the context of instruction-following. The study identifies challenges with existing instruction-following benchmarks, where multiple factors are intertwined with uncertainty, making it difficult to isolate and compare different methods and models. To address these issues, the authors introduce a controlled evaluation setup with two benchmark versions of data, enabling a comprehensive comparison of uncertainty estimation methods under various conditions. The findings reveal that existing uncertainty methods struggle, particularly when models make subtle errors in instruction following. Although internal model states offer some improvement, they are insufficient in more complex scenarios. The insights gained from the controlled evaluation setups provide a crucial understanding of LLMs’ limitations and potential for uncertainty estimation in instruction-following tasks, ultimately paving the way for the development of more trustworthy AI agents.
Exploring the Uncertainty of Large Language Models in Instruction-Following
Large language models (LLMs) have the potential to be valuable personal AI agents across various domains, but their ability to precisely follow user instructions has been called into question. Recent studies have highlighted significant limitations in LLMs’ instruction-following capabilities, raising concerns about their reliability in high-stakes applications. To mitigate these risks, accurately estimating LLMs’ uncertainty in adhering to instructions becomes crucial.
In what is believed to be the first systematic evaluation of uncertainty estimation abilities of LLMs in instruction-following, a study has revealed key challenges with existing benchmarks. These benchmarks consider multiple factors that are intertwined with uncertainty stemming from instruction-following, making it complex to isolate and compare different methods and models.
To address these challenges, a controlled evaluation setup with two benchmark versions of data has been introduced. This setup allows for a comprehensive comparison of uncertainty estimation methods under various conditions. The aim is to understand the limitations and potential of LLMs in estimating uncertainty in instruction-following tasks, thereby paving the way for more trustworthy AI agents.
The findings of the study indicate that existing uncertainty methods struggle, especially when models make subtle errors in following instructions. While internal model states show some improvement, they are still inadequate in more complex scenarios. This suggests that there is a need for further advancements in uncertainty estimation methods for LLMs in order to enhance their reliability in instruction-following tasks.
By gaining a crucial understanding of the limitations and potential of LLMs in uncertainty estimation for instruction-following, researchers can work towards developing more robust AI agents. These agents would be capable of accurately estimating their uncertainty and adhering to instructions even in high-stakes applications.
The paper titled “Large language models’ uncertainty estimation in instruction-following tasks” addresses an important concern regarding the reliability of large language models (LLMs) in adhering to user instructions. LLMs have the potential to be valuable personal AI agents in various domains, but recent studies have revealed significant limitations in their instruction-following capabilities. This raises concerns about their reliability in high-stakes applications.
One crucial aspect of mitigating deployment risks is accurately estimating LLMs’ uncertainty in adhering to instructions. This paper presents the first systematic evaluation of uncertainty estimation abilities of LLMs specifically in the context of instruction-following. The authors highlight the challenges associated with existing instruction-following benchmarks, where multiple factors are intertwined with uncertainty stemming from instruction-following. This complexity makes it difficult to isolate and compare different methods and models.
To address these challenges, the authors introduce a controlled evaluation setup with two benchmark versions of data, enabling a comprehensive comparison of uncertainty estimation methods under various conditions. This controlled setup allows for a more accurate assessment of the effectiveness of different uncertainty estimation methods.
The findings of this study indicate that existing uncertainty estimation methods face difficulties, particularly when LLMs make subtle errors in instruction following. While internal model states offer some improvement in uncertainty estimation, they are still inadequate in more complex scenarios.
The insights gained from the controlled evaluation setups in this paper provide a crucial understanding of the limitations of LLMs and their potential for uncertainty estimation in instruction-following tasks. This understanding is essential for developing more trustworthy AI agents.
Moving forward, it will be important to build upon these findings and explore new approaches to improve LLMs’ instruction-following capabilities and their ability to estimate uncertainty. This could involve leveraging external knowledge sources, incorporating contextual information, or developing specialized architectures that are more robust in handling instruction-following tasks.
Overall, this paper contributes to the field by shedding light on the challenges and limitations of LLMs in instruction-following and provides a foundation for future research to develop more reliable and trustworthy AI agents.
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