Eliciting Problem Specifications via Large Language Models
This work addresses the problem of automating problem formulation for cognitive systems researchers, though it is incremental as it builds on existing LLM and cognitive system capabilities.
The paper tackles the challenge of translating natural language problem definitions into semi-formal specifications for cognitive systems by using large language models (LLMs) to map problem classes into specifications, enabling existing reasoning systems to solve problem instances, with preliminary results suggesting potential speed-up in cognitive systems research.
Cognitive systems generally require a human to translate a problem definition into some specification that the cognitive system can use to attempt to solve the problem or perform the task. In this paper, we illustrate that large language models (LLMs) can be utilized to map a problem class, defined in natural language, into a semi-formal specification that can then be utilized by an existing reasoning and learning system to solve instances from the problem class. We present the design of LLM-enabled cognitive task analyst agent(s). Implemented with LLM agents, this system produces a definition of problem spaces for tasks specified in natural language. LLM prompts are derived from the definition of problem spaces in the AI literature and general problem-solving strategies (Polya's How to Solve It). A cognitive system can then use the problem-space specification, applying domain-general problem solving strategies ("weak methods" such as search), to solve multiple instances of problems from the problem class. This result, while preliminary, suggests the potential for speeding cognitive systems research via disintermediation of problem formulation while also retaining core capabilities of cognitive systems, such as robust inference and online learning.