First is Better Than Last for Language Data Influence

TL;DR

This paper introduces TracIn-WE, a method that improves influence estimation for NLP models by operating on word embeddings instead of last-layer weights, reducing cancellation effects and enhancing interpretability.

Contribution

The paper proposes TracIn-WE, a novel influence measure that operates on embedding layers, addressing cancellation issues and improving influence detection in large language models.

Findings

TracIn-WE outperforms last-layer influence methods on deletion metrics.

It provides influence scores at the word level within training examples.

The method is effective across multiple NLP classification tasks.

Abstract

The ability to identify influential training examples enables us to debug training data and explain model behavior. Existing techniques to do so are based on the flow of training data influence through the model parameters. For large models in NLP applications, it is often computationally infeasible to study this flow through all model parameters, therefore techniques usually pick the last layer of weights. However, we observe that since the activation connected to the last layer of weights contains "shared logic", the data influenced calculated via the last layer weights prone to a ``cancellation effect'', where the data influence of different examples have large magnitude that contradicts each other. The cancellation effect lowers the discriminative power of the influence score, and deleting influential examples according to this measure often does not change the model's behavior by much. To mitigate this, we propose a technique called TracIn-WE that modifies a method called TracIn to operate on the word embedding layer instead of the last layer, where the cancellation effect is less severe. One potential concern is that influence based on the word embedding layer may not encode sufficient high level information. However, we find that gradients (unlike embeddings) do not suffer from this, possibly because they chain through higher layers. We show that TracIn-WE significantly outperforms other data influence methods applied on the last layer significantly on the case deletion evaluation on three language classification tasks for different models. In addition, TracIn-WE can produce scores not just at the level of the overall training input, but also at the level of words within the training input, a further aid in debugging.