Language Models Implement Simple Vector Arithmetic to Solve Relational Tasks

Temel Kavramlar

Language models sometimes use a simple vector arithmetic mechanism to solve relational tasks by leveraging regularities encoded in their hidden representations.

Özet

The paper presents evidence that large language models (LLMs) sometimes exploit a simple vector arithmetic mechanism to solve relational tasks during in-context learning. The key findings are:

1. The authors observe a distinct processing signature in the forward pass of LLMs, where the model first surfaces the argument to a function (e.g., the country name), then applies the function (e.g., retrieving the capital city) in a later layer. This signature is consistent across models and tasks.

2. By analyzing the feed-forward network (FFN) updates in GPT2-Medium, the authors show that the vector arithmetic mechanism is often implemented in mid-to-late layer FFNs. These FFN outputs can be isolated and applied to new contexts, demonstrating their modularity and ability to implement content-independent functions.

3. The authors find that this vector arithmetic mechanism is specific to tasks that require retrieving information from the model's pretraining memory, rather than from the local context. When the answer can be directly copied from the prompt, the FFN updates do not play a significant role.

The results contribute to the understanding of how LLMs solve tasks, suggesting that despite their complexity, they can sometimes rely on familiar and intuitive algorithms like vector arithmetic. This offers insights into the interpretability of LLMs and potential methods for detecting and preventing unwanted behaviors.

İstatistikler

"Poland:Warsaw::China:Beijing"
"Today I abandon. Yesterday I abandoned. Today I abolish. Yesterday I abolished."
"On the floor, I see a silver keychain, [...] and a blue cat toy. What color is the keychain? Silver"
"On the table, you see a brown sheet of paper, a red fidget spinner, a blue pair of sunglasses, a teal dog leash, and a gold cup. What color is the sheet of paper? Brown"

Alıntılar

"A primary criticism towards language mod-
els (LMs) is their inscrutability. This paper
presents evidence that, despite their size and
complexity, LMs sometimes exploit a simple
vector arithmetic style mechanism to solve
some relational tasks using regularities en-
coded in the hidden space of the model (e.g.,
Poland:Warsaw::China:Beijing)."
"We further show that this mechanism
is specific to tasks that require retrieval from
pretraining memory, rather than retrieval from
local context."

Önemli Bilgiler Şuradan Elde Edildi

Language Models Implement Simple Word2Vec-style Vector Arithmetic

by Jack Merullo... : arxiv.org 04-04-2024

https://arxiv.org/pdf/2305.16130.pdf

Daha Derin Sorular

How do the vector arithmetic mechanisms identified in this paper interact with other mechanisms that language models use to solve tasks, such as attention-based retrieval from local context

The vector arithmetic mechanisms identified in the paper interact with other mechanisms in language models, such as attention-based retrieval from local context, in a complementary manner. While attention mechanisms focus on capturing dependencies between words in a given context, the vector arithmetic mechanisms help in encoding relational information and solving tasks that require retrieval from pretraining memory. In the context of the study, the vector arithmetic mechanisms, implemented through FFN updates, play a crucial role in promoting the argument token to the function before arriving at the final answer token. This process of argument-function processing is distinct from the attention-based retrieval from local context, showcasing how different components in language models work together to solve tasks.

What are the limitations of the vector arithmetic mechanism, and under what conditions might language models resort to other strategies for solving relational tasks

The vector arithmetic mechanism, while effective in solving relational tasks by encoding regularities in the hidden space of the model, has certain limitations. One limitation is the dependency on the quality and quantity of training data. If the model has not been exposed to certain relationships or lacks sufficient data to learn those relationships, the vector arithmetic mechanism may not perform optimally. Additionally, the mechanism may struggle with many-to-many or many-to-one relations that are not easily represented through simple vector arithmetic. In such cases, language models may resort to other strategies, such as leveraging attention mechanisms for local context retrieval or incorporating external knowledge sources to enhance performance on complex relational tasks. Furthermore, the vector arithmetic mechanism may not generalize well to all types of tasks, requiring a more nuanced approach for diverse problem-solving scenarios.

Given the insights from this paper, how might we design language models that can more transparently and reliably apply vector arithmetic and other interpretable algorithms to solve a wider range of tasks

To design language models that can transparently and reliably apply vector arithmetic and other interpretable algorithms to solve a wider range of tasks, several strategies can be considered. Firstly, incorporating explicit mechanisms for vector arithmetic operations within the model architecture can enhance interpretability and facilitate the application of relational reasoning in various tasks. Additionally, training language models on diverse datasets that cover a wide range of relational tasks can improve their ability to encode and manipulate relational information effectively. Employing hybrid models that combine the strengths of vector arithmetic with attention mechanisms can offer a more robust approach to task-solving, leveraging the benefits of both mechanisms. Moreover, developing techniques for model introspection and explanation can aid in understanding how language models utilize different algorithms for task completion, leading to more transparent and reliable performance across tasks.