In-Context (ICL) LLM-based Information Retrieval (IR) Task
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A In-Context (ICL) LLM-based Information Retrieval (IR) Task is a LLM-based NLP task that uses the LLM input context to specify the information retrieval task.
- Context:
- It can range from being a Zero-Shot IR Task to being a Few-Shot IR Task settings where the model uses minimal prior examples during inference.
- It can employ prompt engineering to improve the specificity and relevance of the information retrieval.
- It can range from being a single document information retrieval task to a multi-document information retrieval task.
- It can be enhanced by fine-tuning on specific domains to improve retrieval accuracy in specialized areas such as legal or medical texts.
- It can require handling large volumes of data and managing performance issues related to information overload and context size.
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- Example(s):
- an In-Context Needle-in-a-Haystack Retrieval Task that ...
- an In-Context Legal Document Retrieval that uses specific legal terms to extract relevant case law information.
- an In-Context Medical Information Retrieval where specific medical conditions or treatments are retrieved from medical literature.
- an In-Context Customer Support Retrieval that pulls specific product information or troubleshooting help based on customer queries.
- an In-Context Historical Event Retrieval where significant historical facts are retrieved based on a contextual inquiry into historical texts.
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- Counter-Example(s):
- See: Domain-Specific Information Retrieval.