Demystifying Embedding Spaces using Large Language Models at ICLR 2024

Conceitos Básicos

Large language models can enhance the interpretability of embeddings, bridging the gap between rich data representations and expressive capabilities.

Resumo

Embeddings are crucial for representing complex information concisely, but interpreting them directly is challenging. This paper introduces ELM, a framework that leverages large language models to interact with embeddings, enabling querying and exploration. By training adapter layers to map domain embeddings into token-level embedding space, ELM facilitates interpretation of continuous domain embeddings using natural language. The study demonstrates the effectiveness of ELM on various tasks like enhancing concept activation vectors, communicating novel embedded entities, and decoding user preferences in recommender systems. The approach offers a dynamic way to navigate and understand complex embedding representations.

Estatísticas

Published as a conference paper at ICLR 2024
MovieLens 25M dataset contains 25 million ratings of 62,423 movies and 162,541 users.
Two types of embeddings: behavioral and semantic.
Training methodology includes two-stage training for fine-tuning pretrained LLMs.
Consistency metrics used: semantic consistency (SC) and behavioral consistency (BC).

Citações

"ELM generalizes well to embedding vectors in a test set."
"ELM aligns well with human-rater expectations."
"ELM is adept at handling the challenge of describing novel entities."

Principais Insights Extraídos De

Demystifying Embedding Spaces using Large Language Models

by Guy Tennenho... às arxiv.org 03-14-2024

https://arxiv.org/pdf/2310.04475.pdf

Perguntas Mais Profundas

How can ELM's approach be applied to other domains beyond movie datasets?

ELM's approach of leveraging large language models to interpret domain embeddings can be extended to various other domains beyond movie datasets. For instance, in e-commerce, it could help in interpreting user preferences based on behavioral embeddings derived from purchase history. This could enhance personalized recommendations and improve user experience. In healthcare, ELM could aid in understanding patient profiles encoded as embeddings, leading to better treatment recommendations and healthcare outcomes. Additionally, in finance, ELM could assist in analyzing market trends by interpreting complex financial data represented as embeddings.

What are the potential limitations or biases introduced by using large language models for interpreting embeddings?

While large language models like ELM offer significant advantages in interpreting embeddings, they also come with potential limitations and biases. One limitation is the black-box nature of these models, making it challenging to understand how exactly they arrive at their interpretations. Biases present in the training data can also get amplified through these models, leading to biased interpretations of the embedding spaces. Moreover, there might be a tendency for these models to overfit on specific tasks or datasets if not carefully regularized during training.

How might the findings from this study impact the development of future interpretability tools for machine learning models?

The findings from this study provide valuable insights into enhancing interpretability tools for machine learning models. By demonstrating how natural language interaction with embeddings can lead to more interpretable results, future tools may incorporate similar approaches for better model understanding. The use of semantic and behavioral consistency metrics introduced by ELM can inspire new evaluation techniques that focus on alignment between model outputs and underlying data representations. Overall, this study sets a precedent for developing more transparent and intuitive interpretability tools across various machine learning applications.