SeqMem-Eval: New Framework for Evaluating LLM Memory Evolution
A new diagnostic evaluation framework called SeqMem-Eval has been developed by researchers to assess memory capabilities in large language models (LLMs) during sequential tasks. Unlike conventional metrics that depend on overall scores like final accuracy or total performance, SeqMem-Eval emphasizes the evolution, generalization, consolidation, and retention of memory states over time. This framework is intended for a test-time environment where memory is external, mediated by prompts, and updated independently of model parameters. It evaluates online utility, hold-out generalization, backward transfer, and forgetting, taking cues from continual learning. The goal is to identify significant failure modes such as forgetting and negative transfer that might be hidden by aggregate metrics. This research is documented in a paper on arXiv under ID 2605.15384.
Key facts
- SeqMem-Eval is a diagnostic evaluation framework for LLM memory.
- It addresses limitations of aggregate metrics like final hold-out accuracy.
- The framework targets test-time settings with external, prompt-mediated memory.
- Memory is updated without modifying model parameters.
- It measures online utility, hold-out generalization, backward transfer, and forgetting.
- Inspiration is drawn from continual learning.
- The paper is available on arXiv with ID 2605.15384.
- The work aims to uncover failure modes like forgetting and negative transfer.
Entities
Institutions
- arXiv