causallm is not suitable for contextual learning

causallm is not suitable for contextual learning

Originally published by Satoru Morimoto  No data is not smart  2023-08-16 23:42  published in Guangdong

overview

The background of this paper is that in context learning, the Transformer-based prefixLM model outperforms the causalLM model using the autoregressive attention mechanism.

In the past methods, the causalLM model was mainly used, which used an autoregressive attention mechanism to limit the mutual attention between context samples. Due to this limitation, the capabilities of the model are limited. Therefore, it is natural to propose the prefixLM model, which allows global attention among contextual samples. This approach is intuitively sound and has achieved good performance in empirical studies.

This paper uses the method of theoretical analysis to analyze the convergence behavior of prefixLM and causalLM under the construction of specific parameters. The research results show that although the convergence rates of the two language models are linear, the prefixLM model converges to the optimal solution of linear regression , while the convergence dynamics of the causalLM model follows the characteristics of the online gradient descent algorithm , even if the number of samples grows infinitely, it cannot Guaranteed optimality. To supplement the theoretical analysis, this paper verifies that the prefixLM model consistently underperforms the causalLM model in various settings by conducting experiments on synthetic and real tasks using different types of transformers.

Experiments are carried out on synthetic and real tasks, and the performance comparison verifies that the causalLM model has lower performance than the prefixLM model in all settings. These experimental results support their research goals.

 

Discussion on important issues

1. Why does using prefixLM instead of causalLM perform better in context learning? According to the empirical research in this paper, prefixLM is able to achieve full connection between contextual samples, while causalLM uses autoregressive attention to limit the connection between samples and future samples. By allowing full connections between contextual samples, prefixLM can better exploit contextual information and thus perform better in contextual learning.

2. What is the difference between prefixLM and causalLM convergence properties? Through theoretical analysis, the article finds that under a specific parameter configuration, both prefixLM and causalLM converge to their stable points at a linear rate. However, prefixLM will converge to the optimal solution of linear regression, while the convergence dynamics of causalLM follows the characteristics of the online gradient descent algorithm, even if the number of samples grows infinitely, it cannot guarantee to reach the optimal solution.

3. Why can the context learning ability of the model be achieved by using large-scale data pre-training? Large-scale data pre-training enables the Transformer model to learn richer semantic and grammatical rules from massive data, so that new tasks can be solved by ingesting a small number of labeled examples (prefixes) and calculating the prediction results of query examples in the inference stage. This capability, known as in-context learning (ICL), goes beyond traditional machine learning applications and endows models with the flexibility to tackle new tasks.

4. Why are auto-regressive masks not effective in restricting the entire sequence? Empirical studies have found that applying the autoregressive mask to the entire sequence will limit the model's ability to handle long sequences, and too strict autoregressive restrictions make it difficult for the model to fully utilize contextual information. To solve this problem, the researchers propose the prefixLM model, which allows full connections within prefix examples, so that the model can better utilize contextual information and improve performance.

5. Do the results of the empirical experiments mentioned in the article support the explanation of the theoretical framework? Yes, the article validates the performance of causalLM and prefixLM through experiments on synthetic and real tasks. Experimental results consistently show that causalLM underperforms prefixLM regardless of the setting . This is in line with the theoretical explanation proposed in the paper, proving the superiority of prefixLM in context learning.

Paper link: https://arxiv.org/abs/2308.06912.pdf