Work

Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models

Year: 2024

Type: conference-paper

Abstract: Memory is one of the most essential cognitive functions serving as a repository of world knowledge and episodes of activities.In recent years, large-scale pre-trained language models have shown remarkable memorizing ability.On the contrary, vanilla neural networks without pre-training have been long observed suffering from the catastrophic forgetting problem.To investigate such a retentive-forgetful contradiction and understand the memorizing dynamic mechanism of language models, we conduct thorough experiments by controlling the target knowledge types, the learning strategies and the learning schedules.We find that: 1) Vanilla language models without pre-training are forgetful; 2) Pre-training leads to retentive language models; 3) Knowledge relevance and diversification significantly influence the memory formation.These conclusions are useful for understanding the abilities of pre-trained language models and shed light on designing and evaluating new learning and inference algorithms of language models.

Source: Language Resources and Evaluation Conference (LREC)

DOI: 10.63317/4h8nkuscdqqa