In question-answering scenarios, humans can assess whether the available information is sufficient and seek additional information if necessary, rather than providing a forced answer.

FL further makes efforts to adapt real-world scenarios, i.e., adapting

Input reduction reduces the length of the input context before feeding to downstream LLMs.

Understanding how and why data distillation methods work is vital not only for improving these methods but also for revealing fundamental characteristics of "good" training data.