Forgetting Curve: A Reliable Method for Evaluating Memorization Capability for Long-context Models
- URL: http://arxiv.org/abs/2410.04727v1
- Date: Mon, 7 Oct 2024 03:38:27 GMT
- Title: Forgetting Curve: A Reliable Method for Evaluating Memorization Capability for Long-context Models
- Authors: Xinyu Liu, Runsong Zhao, Pengcheng Huang, Chunyang Xiao, Bei Li, Jingang Wang, Tong Xiao, Jingbo Zhu,
- Abstract summary: We propose a new method called forgetting curve to measure the memorization capability of long-context models.
We show that forgetting curve has the advantage of being robust to the tested corpus and the experimental settings.
Our measurement provides empirical evidence for the effectiveness of transformer extension techniques while raises questions for the effective length of RNN/SSM based models.
- Score: 58.6172667880028
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Numerous recent works target to extend effective context length for language models and various methods, tasks and benchmarks exist to measure model's effective memorization length. However, through thorough investigations, we find limitations for currently existing evaluations on model's memorization capability. We provide an extensive survey for limitations in this work and propose a new method called forgetting curve to measure the memorization capability of long-context models. We show that forgetting curve has the advantage of being robust to the tested corpus and the experimental settings, of not relying on prompts and can be applied to any model size. We apply our forgetting curve to a large variety of models involving both transformer and RNN/SSM based architectures. Our measurement provides empirical evidence for the effectiveness of transformer extension techniques while raises questions for the effective length of RNN/SSM based models. We also examine the difference between our measurement and existing benchmarks as well as popular metrics for various models. Our code and results can be found at https://github.com/1azybug/ForgettingCurve.
Related papers
- EmbedLLM: Learning Compact Representations of Large Language Models [28.49433308281983]
We propose EmbedLLM, a framework designed to learn compact vector representations of Large Language Models.
We introduce an encoder-decoder approach for learning such embeddings, along with a systematic framework to evaluate their effectiveness.
Empirical results show that EmbedLLM outperforms prior methods in model routing both in accuracy and latency.
arXiv Detail & Related papers (2024-10-03T05:43:24Z) - The Languini Kitchen: Enabling Language Modelling Research at Different
Scales of Compute [66.84421705029624]
We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours.
We pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length.
This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput.
arXiv Detail & Related papers (2023-09-20T10:31:17Z) - TRAK: Attributing Model Behavior at Scale [79.56020040993947]
We present TRAK (Tracing with Randomly-trained After Kernel), a data attribution method that is both effective and computationally tractable for large-scale, differenti models.
arXiv Detail & Related papers (2023-03-24T17:56:22Z) - Revisiting Neural Scaling Laws in Language and Vision [43.57394336742374]
We argue for a more rigorous methodology based on the extrapolation loss, instead of reporting the best-fitting parameters.
We present a recipe for estimating scaling law parameters reliably from learning curves.
We demonstrate that it extrapolates more accurately than previous methods in a wide range of architecture families across several domains.
arXiv Detail & Related papers (2022-09-13T09:41:51Z) - Efficient Nearest Neighbor Language Models [114.40866461741795]
Non-parametric neural language models (NLMs) learn predictive distributions of text utilizing an external datastore.
We show how to achieve up to a 6x speed-up in inference speed while retaining comparable performance.
arXiv Detail & Related papers (2021-09-09T12:32:28Z) - ALT-MAS: A Data-Efficient Framework for Active Testing of Machine
Learning Algorithms [58.684954492439424]
We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data.
The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
arXiv Detail & Related papers (2021-04-11T12:14:04Z) - Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples.
We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries.
We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
arXiv Detail & Related papers (2020-02-27T10:22:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.