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Prompting LLM is presented with example input-output pairs, and asked to generate instructions that could have caused a model following the instructions to generate the outputs, given the inputs. Each of the generated instructions is used to prompt the target LLM, followed by each of the inputs.
A language model may answer a query by first querying a database of documents using the query. The document retrieval can be via a vector database, summary index, tree index, or keyword table index. [8] Following document retrieval, the LLM generates an output that incorporates information from both the query and the retrieved documents. [9]
An example of such a task is responding to the user's input '354 * 139 = ', provided that the LLM has not already encountered a continuation of this calculation in its training corpus. [ dubious – discuss ] In such cases, the LLM needs to resort to running program code that calculates the result, which can then be included in its response.
The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>. User: <prompt>. Assistant:
A generative LLM can be prompted in a zero-shot fashion by just asking it to translate a text into another language without giving any further examples in the prompt. Or one can include one or several example translations in the prompt before asking to translate the text in question. This is then called one-shot or few-shot learning, respectively.
As stated above, LLM outputs should not be used verbatim to expand an article. Asking an LLM for feedback on an existing article. Such feedback should never be taken at face value. Just because an LLM says something, does not make it true. But such feedback may be helpful if you apply your own judgment to each suggestion.
However it comes at a cost: due to encoder-only architecture lacking a decoder, BERT can't be prompted and can't generate text, while bidirectional models in general do not work effectively without the right side, thus being difficult to prompt. As an illustrative example, if one wishes to use BERT to continue a sentence fragment "Today, I went ...
One such experiment conducted in 2019 tested Google’s BERT LLM using the argument reasoning comprehension task. BERT was prompted to choose between 2 statements, and find the one most consistent with an argument. Below is an example of one of these prompts: [20] [28]