LLM In-Context Recall Measure
A LLM In-Context Recall Measure is a LLM ICL performance measure for LLM in-context tasks that is an information recall measure.
- Context:
- It can (typically) employ Benchmarking Tests to assess various LLMs on their ability to retrieve embedded information from extensive textual content accurately.
- It can range from being a Simple Information Retrieval Task to handling complex Real-World Applications where the context and the embedded information vary significantly.
- It can be supported by an LLM Evaluation System, which helps to standardize and automate the measurement process across different models and scenarios.
- It can involve varying levels of complexity and constraints, adapting to the specific needs and goals of the information retrieval tasks it is designed to measure.
- It can also serve as a fundamental metric for developers refining the retrieval capabilities of LLMs in response to empirical performance data.
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- Example(s):
- LLM In-Context Needle-in-a-Haystack Recall Measure, focusing on how precisely and completely the model identifies and uses the 'needle' (specific information) within the 'haystack' (larger block of text).
- Context-Dependent Information Retrieval Test, evaluating LLMs' performance in retrieving relevant information based on varying contextual cues.
- Multi-Hop Reasoning Assessment, measuring LLMs' capacity to perform multi-step reasoning by combining information from different parts of the input text.
- Structured Query Resolution Test, which tests how well a model can interpret and answer queries that require understanding and manipulation of structured data within a textual context.
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- Counter-Example(s):
- Incidental Bilingualism Evaluation, assessing LLMs' ability to translate languages based on incidental bilingual texts in training data.
- LLM In-Context Precision Measures, which focus more on the accuracy of the information retrieved rather than its comprehensiveness.
- LLM In-Context Hallucination Measures, which evaluate how often an LLM generates incorrect or fabricated information under various contexts.
- Binary Decision Tasks, where the model’s task is to make binary choices, not retrieve information.
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- See: Large Language Model, Performance Metric, Precision and Recall Metrics, Information Retrieval, Benchmarking Tests.