LOLAMEME: Logic, Language, Memory, Mechanistic Framework
Jay Desai, Xiaobo Guo, Srinivasan H. Sengamedu
TL;DR
LOLAMEME is a new framework that extends mechanistic analysis of large language models by incorporating logic, memory, and language nuances, enabling better understanding and comparison of different architectures.
Contribution
The paper introduces LOLAMEME, a comprehensive framework for mechanistic analysis of language models, including two instantiations and a hybrid architecture T HEX for improved performance.
Findings
T HEX outperforms GPT-2 and Hyena on select tasks.
LOLAMEME enables detailed comparison of different language model architectures.
Framework incorporates logic, memory, and language nuances for deeper mechanistic insights.
Abstract
The performance of Large Language Models has achieved superhuman breadth with unprecedented depth. At the same time, the language models are mostly black box models and the underlying mechanisms for performance have been evaluated using synthetic or mechanistic schemes. We extend current mechanistic schemes to incorporate Logic, memory, and nuances of Language such as latent structure. The proposed framework is called LOLAMEME and we provide two instantiations of LOLAMEME: LoLa and MeMe languages. We then consider two generative language model architectures: transformer-based GPT-2 and convolution-based Hyena. We propose the hybrid architecture T HEX and use LOLAMEME framework is used to compare three architectures. T HEX outperforms GPT-2 and Hyena on select tasks.
Peer Reviews
Decision·Submitted to ICLR 2024
This work proposes a new hybrid architecture based on transformer-based GPT-2 and convolution-based Hyena. Experiments demonstrate the superiority of this architecture.
• The motivation and problem formulation of this work is unclear. And the novelty and contribution of this paper are somewhat limited. The proposed new architectures are simply constructed by replacing certain layers of the Hyena model with the GPT-2 layer. Although some experiments demonstrate better performance on the proposed two test datasets, there may be a lack of validation experiments on other existing datasets. Additionally, providing some interesting findings or interpretations about t
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The only changes done to the transformer architecture is to replace a single layer by a layer from the hyena model. The variations include only replacing a different layer of the transformer with the same hyena layer. Lots of experiments are done to compare the performance of variants and measure the impact on the quality under different input lengths, on some synthetic datasets, etc. But I don't see any insight that could be won from these experiments.
1. The new framework LOLAMEME similar to natural language is impressive and interesting. 2. This work builds multiple datasets with several billion tokens based on the LOLAMEME framework, which would contribute to future research. 3. This work performs comprehensive experiments over these datasets and a related benchmark dataset to show the effectiveness of the new framework.
1. The motivation for the model design is not clearly discussed in this work. I am confused about the differences among T HEX, GPT-2, and Hyena. 2. The structure of this paper is not clear enough, which is very hard to follow. 3. I would suggest that an illustration figure be provided to clearly show the main idea of the LOLAMEME framework, which will make this work easier to understand. 4. Some sentences should be revised and the format should be unified. For instance, in the Abstract, "We ext
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Taxonomy
TopicsMachine Learning and Data Classification · Topic Modeling · Generative Adversarial Networks and Image Synthesis
MethodsAttention Is All You Need · Cosine Annealing · Layer Normalization · Weight Decay · Linear Warmup With Cosine Annealing · Linear Layer · Byte Pair Encoding · Adam · Refunds@Expedia|||How do I get a full refund from Expedia? · Attention Dropout