Paper: Demystifying Embedding Spaces using Large Language Models (ICLR-2024)

Reviewer comment (Experiments):
While the authors demonstrate the benefits of their framework with two examples (movie ratings - behavioral embeddings and movie descriptions - semantic embeddings), one of the vector spaces studied could have come from a completely different domain, perhaps derived from a different dataset, although this may fall into future work.

Author response:
Also, thank you for your comments regarding the embeddings. Notice that our behavioral embeddings are trained solely on user/movie ID and rating data (with no semantic information). Our user profiles task successfully demonstrates the ability of extracting semantic information from behavioral embeddings. Hope that addresses the concern regarding generalizability on embedding information from one domain to another. To emphasize this further, we are also adding experiments on Amazon public data, where the embeddings similarly come from a different source. We hope this will answer your concern.

Reviewer comment (Experiments):
Has the framework been tested on other embeddings that may contain less semantic information than the domain embeddings used here (similar to the example in Appendix C)?

Author response:
Also, thank you for your comments regarding the embeddings. Notice that our behavioral embeddings are trained solely on user/movie ID and rating data (with no semantic information). Our user profiles task successfully demonstrates the ability of extracting semantic information from behavioral embeddings. Hope that addresses the concern regarding generalizability on embedding information from one domain to another. To emphasize this further, we are also adding experiments on Amazon public data, where the embeddings similarly come from a different source. We hope this will answer your concern.

Reviewer comment (Experiments):
One of my greatest concerns is the practicality of the proposed framework, given that training an ELM requires manually constructing a batch of tasks, which need to be diverse enough to extract rich semantic information from the target domain $\mathcal{W}$ to support the interpretation of the embeddings.

Author response:
Thank you for your helpful comments. We are happy you found the problem of interpreting embeddings using language appealing, and our approach simple and effective. 

We understand your concern w.r.t. ELM, which requires the availability of tasks mapping the domain embeddings to text. While some problems would indeed require human intervention for the creation of such tasks (or possibly using the power of existing LLMs, as we’ve shown in the paper), many tasks readily associate enormous amounts of text with specific entities. Users and items in recommender systems, as an example, are abstract entities which are often described using behavioral embeddings, which are hard to interpret. Nevertheless, users also engage with text and items by commenting, reviewing, and conversing with other users about items (e.g., Youtube comments, Amazon reviews, MovieLens tags, etc.). 

To address your concern, we are adding experiments for the large public Amazon dataset [1], which consists of 9.35M items with textual descriptions, 20.9M users, 233.1M reviews, and 82.83M ratings. We are training ELM on 5-7 tasks over this dataset (starting with item descriptions, positive reviews, negative reviews, neutral reviews, convincing item summaries, and user profiles), and will update our paper with these new results by the end of the week, including human evaluation. We hope this will address most of your concerns.

Reviewer comment (Experiments):
I am not sure if ELM performs equally well in other more specialized domains, considering that the original training corpus of LLMs is likely to contain rich semantic information relevant to movies.

Author response:
To address your second question, we are adding new results for the Amazon product dataset (as discussed above).

Reviewer comment (Reproducibility):
What is the detailed form of the loss function $\mathcal{L}\_{\theta}$ used in the experiment? Did the authors use reinforcement learning from AI feedback to optimize $\mathcal{M}_{ELM}$?

Author response:
Regarding your questions:
1. We used supervised learning (i.e., next token cross-entropy loss) to train all our models in the paper. In Appendix G, we discuss training ELM using reinforcement learning from AI feedback (RLAIF). Specifically, we attempt to train ELM with an additional reward to encourage semantic and behavioral consistency. Our results, however, did not show significant improvement compared to the regular supervised learning baseline. We leave further experimentation using different RLAIF approaches for future work.

Reviewer comment (Experiments):
Since the output of instances in the training data is generated by LLMs, can ELM reasonably interpret embeddings in specialized domains outside the scope of LLMs?

Author response:
To address your second question, we are adding new results for the Amazon product dataset (as discussed above).

Reviewer comment (Experiments):
The evaluation can be improved. The training data are all synthesized from an LLM.

Author response:
To address your main concern, we are adding new tasks on real data from the public Amazon product dataset [1], which consists of 9.35M items with textual descriptions, 20.9M users, 233.1M reviews, and 82.83M ratings. We are training tasks involving item descriptions, positive reviews, negative reviews, neutral reviews, convincing item summaries, and user profiles. We will update the paper with these results by the end of the week, and also include human rater evaluations to improve the evaluation of our paper. We hope this will address your main concern.

Reviewer comment (Evaluation):
The evaluation can be improved. The 24 tasks are all original.

Author response:
[M2] We’ve added a short description of each task to the paper (see Table 3)

Reviewer comment (Experiments):
The evaluation can be improved. ELM is only evaluated on the MovieLens.

Author response:
To address your main concern, we are adding new tasks on real data from the public Amazon product dataset [1], which consists of 9.35M items with textual descriptions, 20.9M users, 233.1M reviews, and 82.83M ratings. We are training tasks involving item descriptions, positive reviews, negative reviews, neutral reviews, convincing item summaries, and user profiles. We will update the paper with these results by the end of the week, and also include human rater evaluations to improve the evaluation of our paper. We hope this will address your main concern.  
[Q1] As mentioned above, we will add experiments using the public Amazon dataset.

Reviewer comment (Presentation):
It would be great to explain $E_A$ in Figure 2 (the definition is on page 4).

Author response:
[M1] We’ve added an explanation for $E_A$ in Figure 2.

Reviewer comment (Presentation):
The 24 tasks are essential to understand this paper. I recommend adding a table to provide a high-level description of these 24 tasks in the main paper.

Author response:
[M2] We’ve added a short description of each task to the paper (see Table 3)

Reviewer comment (Writing):
The ELM method is quite intuitive, but the writing in section 2 is overly complex. It would be helpful to have a small glossary to explain all notations.

Author response:
[M3] We’ve added a small glossary to explain our notation, which we hope will improve clarity. (see Table 1)

Reviewer comment (Presentation):
It is hard to make sense of Table 1. Are these numbers good?

Author response:
[M4] For human rater evaluations, the numbers correspond to ratings between “agree” and “strongly agree”. For semantic and behavioral consistency metrics, we show in Figure 3 attempts of using GPT4 and PaLM2 text-only baselines, with significantly lower scores.

Reviewer comment (Experiments):
Have you tried ELM on other datasets and tasks other than movie reviews?

Author response:
[Q1] As mentioned above, we will add experiments using the public Amazon dataset.

Reviewer comment (Presentation):
Figure 1 is a bit hard to understand. For example, how do people generate the embedding for the animated version of Forrest Gump? Interpolating the embedding of Forrest Gump with an animated movie? Is the black text output from an LLM? Who writes the blue text (e.g., users, researchers)?

Author response:
[Q2] To generate a new movie (item) embedding, we found two potential options: \
Option 1: One could create an item embedding of a hypothetical movie for which the specific attribute is most salient (whereas the other attributes are average). This movie does not necessarily need to exist. \
Option 2: Moving in the direction of the “concept activation vector” with respect to a specific movie soft attribute (e.g., violent, funny), as we demonstrate with user profiles.

In the paper we demonstrate Option 1 for movie tasks and Option 2 for the user profile task.

Finally, regarding the text in Figure 1:
The black text is the generated output of ELM.
The blue and red text are the prompts used as input to ELM, where the red part is an embedding vector not text.
For instance if Forrest Gump’s movie embedding is [1.0, 1.0] and the direction of funny is [0, 1], then the prompt could be of the following sort: “List five positive characteristics of the movie [1.0, 1.1]”, where here, the vector [1.0, 1.1] is inputted through the adapters of ELM, and not as text.
We hope this clarifies your question.

Reviewer comment (Novelty):
The training data is generated by PaML 2-L, and ELM uses PaLM 2-XS to interpret the domain embeddings. Their similar architectures and training procedures may make the contribution limited.

Author response:
Thank you for your review and your helpful comments. To address your concerns, as you’ve proposed, we are adding new experiments for ELM using the public Amazon product dataset [1], which consists of 9.35M items with textual descriptions, 20.9M users, 233.1M reviews, and 82.83M ratings. Indeed, this dataset contains real data of item descriptions, ratings, and reviews, for which we train ELM on 5-7 tasks (starting with item descriptions, positive reviews, negative reviews, neutral reviews, convincing item summaries, and user profiles). We will update the paper with results for these tasks including human rater evaluations by the end of the week, to leave enough time for the discussion period. We hope this will address your major concerns.

We also note that, while our experiment procedures utilize both PaLM2 models for data generation and embedding interpretation, our method does not require the use of the same model architecture for these procedures. We hope our new experiments on the Amazon dataset will address your concern.

Reference for the Amazon Dataset: \
[1] McAuley, Julian, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. "Image-based recommendations on styles and substitutes." In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, pp. 43-52. 2015.

Reviewer comment (Experiments):
The authors argue that ELM is a general framework for different tasks, but the experiments only involve one model (MF) on one dataset (MovieLens). Therefore, the generalization of ELM to other models and datasets is not guaranteed.

Author response:
We've added new experiments for ELM using six tasks over Amazon dataset (see Appendix A in the updated version of the paper). We hope these experiments address your main concerns. We hope to use the time left in the discussion period to address any further concerns you may have.a

Reviewer comment (Experiments):
Can the training data be real other than generated? Some datasets contain descriptions and revives (such as Amazon datasets). Why not use these kinds of datasets?

Author response:
Thank you for your review and your helpful comments. To address your concerns, as you’ve proposed, we are adding new experiments for ELM using the public Amazon product dataset [1], which consists of 9.35M items with textual descriptions, 20.9M users, 233.1M reviews, and 82.83M ratings. Indeed, this dataset contains real data of item descriptions, ratings, and reviews, for which we train ELM on 5-7 tasks (starting with item descriptions, positive reviews, negative reviews, neutral reviews, convincing item summaries, and user profiles). We will update the paper with results for these tasks including human rater evaluations by the end of the week, to leave enough time for the discussion period. We hope this will address your major concerns.

We also note that, while our experiment procedures utilize both PaLM2 models for data generation and embedding interpretation, our method does not require the use of the same model architecture for these procedures. We hope our new experiments on the Amazon dataset will address your concern.

Reference for the Amazon Dataset: \
[1] McAuley, Julian, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. "Image-based recommendations on styles and substitutes." In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, pp. 43-52. 2015.

Reviewer comment (Experiments):
Can ELM perform well with other models and other datasets?

Author response:
We've added new experiments for ELM using six tasks over Amazon dataset (see Appendix A in the updated version of the paper). We hope these experiments address your main concerns. We hope to use the time left in the discussion period to address any further concerns you may have.a