2024
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Prompting LLMs is a challenging art where different ways of expressing the same idea can lead to drastically different responses. Not prompting in the right way gives suboptimal performance and has led to the budding space of prompt engineering and optimization techniques. A standard example here is that appending the string "let's think step by step" to the prompt may significantly improve the quality on few-shot classification tasks. In this session, we'll first cover how prompt optimization can be expressed as a combinatorial optimization problem (over the token sequence space) and overview the standard methods here. (A warning for this audience [the Dagstuhl seminar on ML and combinatorial optimization]: standard combinatorial solvers or approaches are often not used, and instead approaches are specialized to LLMs.) Beyond this, prompt optimization problems are often not solved a single time in isolation, but are repeatedly solved for every new task or piece of information we want to extract from the language model. So, we'll conclude with an overview of learned optimization, or amortization, to share the information between the repeatedly-solved optimization problems.
