Understanding Language Models and Tools: A Comprehensive Survey

Khái niệm cốt lõi

Tools are essential for enhancing language models' performance in various tasks, providing external programs to extend their capabilities.

Tóm tắt

• Language models struggle with complex tasks and lack access to external information.
• Tools facilitate LM task-solving by extending or aiding in various functions.
• Different categories of tools exist, such as knowledge access, computation activities, interaction with the world, non-textual modalities, and specialized LMs.
• The basic tool use paradigm involves LM generating text tokens and tool calls to complete tasks.
• Inference-time prompting and training-time learning methods are used for LM tool integration.
• Various scenarios benefit from tools, while some tasks may not require them.
• Advanced approaches include multi-tool selection, complex tool usage, and tool creation when unavailable.
• Evaluation metrics include task completion, tool selection accuracy, and tool reusability.
• Missing aspects in evaluation include efficiency of tool integration, quality of tools, reliability of unstable tools, reproducible testing, and safe usage considerations.

Thống kê

Many works provide tool information through a prompt and expect LMs to acquire abilities to use these tools from input contexts.
Inference-time prompting Leveraging the ability of LMs to learn in-context (Brown et al., 2020), many works provide tool information through a prompt and expect LMs to acquire abilities to use these tools from input contexts.
Beyond learning tools from test-time contexts, LMs can learn from examples that use these tools during training.

Trích dẫn

"Tools have substantially enhanced their performance for tasks that require complex skills." - Abstract
"Some works introduce application-specific software as tools." - Content
"Inspired by the tools used by humans...some works introduce application-specific software as tools." - Content

Thông tin chi tiết chính được chắt lọc từ

What Are Tools Anyway? A Survey from the Language Model Perspective

by Zhiruo Wang,... lúc arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15452.pdf

Yêu cầu sâu hơn

How do different types of tools impact the performance of language models?

Different types of tools can have varying impacts on the performance of language models. Perception tools help in collecting information from the environment, which can enhance an LM's ability to provide accurate responses based on real-time data. Action tools allow LMs to interact with and manipulate their surroundings, enabling them to perform tasks that require physical actions or changes in state. Computation tools are essential for tackling complex computational tasks that may be challenging for LMs alone, such as mathematical calculations or translations between languages.
The integration of these different types of tools can significantly improve an LM's performance by extending its capabilities beyond text generation tasks. For example, a calculator tool can assist with numerical computations, while a search engine tool can provide access to up-to-date information not present in the training data. By leveraging external programs through these tools, LMs can overcome limitations in their training data and handle more diverse and complex tasks effectively.

How do different types of ethical considerations surrounding the use of external tools in language models?

The use of external tools in language models raises several ethical considerations that need to be addressed:

Data Privacy: External tools may access sensitive user data during interactions, raising concerns about privacy breaches if not handled securely.

Bias and Fairness: Tools integrated into LMs should be carefully vetted to ensure they do not perpetuate biases present in their datasets or algorithms.

Transparency: Users should be informed when external tools are utilized by LMs so they understand how their data is being processed and used.

Accountability: Clear guidelines must be established regarding responsibility for decisions made by LMs using external tool inputs.

Security: Ensuring that integrated external APIs are secure from potential cyber threats is crucial to protect both user data and system integrity.

Consent: Users should have control over whether their interactions with an LM involve the use of external tools and understand the implications thereof.

How can we ensure the reliability and security of external tools integrated into language models?

Ensuring reliability and security when integrating external tools into language models involves implementing robust measures at various stages:

Vetting Process: Conduct thorough assessments before integrating any new tool into an LM ecosystem; verify its functionality, accuracy, security protocols, compliance with regulations like GDPR etc.,and potential biases.

2 .Secure Communication: Implement secure communication channels between the LM and external APIs using encryption methods like HTTPS.
3 .Access Control: Limit access permissions granted to each tool within the LM system based on principles like least privilege.
4 .Regular Audits: Perform regular audits on both internal systems hosting these integrations as well as third-party services providing them.
5 .Data Handling Protocols: Establish clear protocols for handling sensitive user information accessed through these integrations following best practices around storage retention limits,data anonymization techniques etc.,
6 .**Emergency Response Plan: Develop contingency plans outlining steps take incase breach occurs involving one or more integrated API services
By adhering strictlytothese practices ,wecan mitigate risks associatedwithexternaltoolsintegrationandensuretheLMoperatesreliablyandsafelyforallusers involved