Towards Automated Knowledge Integration From Human-Interpretable Representations

Authors: Kasia Kobalczyk, Mihaela van der Schaar
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: informed machine learning, knowledge integration, meta-learning, data efficiency, priors

Abstract:
A significant challenge in machine learning, particularly in noisy and low-data environments, lies in effectively incorporating inductive biases to enhance data efficiency and robustness. Despite the success of informed machine learning methods, designing algorithms with explicit inductive biases remains largely a manual process. In this work, we explore how prior knowledge represented in its native formats, e.g. in natural language, can be integrated into machine learning models in an automated manner. Inspired by the learning to learn principles of meta-learning, we consider the approach of learning to integrate knowledge via conditional meta-learning, a paradigm we refer to as informed meta-learning. We introduce and motivate theoretically the principles of informed meta-learning enabling automated and controllable inductive bias selection. To illustrate our claims, we implement an instantiation of informed meta-learning--the Informed Neural Process, and empirically demonstrate the potential benefits and limitations of informed meta-learning in improving data efficiency and generalisation.

Decision: accept (spotlight)
Reviewer scores: 6, 8, 8

Reviews and discussion:
<thread>
creation date:
1730646331556

thread_type:
official_review

<thread_object>
creator: reviewer_gplz

<content>
summary:
This paper studies a meta-learning setup in which a learner tries to adapt to a new environment given only a few samples. 

The paper's main idea is to endow the learner with additional domain knowledge about the specific task (\mathcal{K} in the paper).

The key research question is whether, in the era of LLMs, we can provide the learner with human-interpretable domain knowledge in the form of text, e.g. the human provides the learner with the information that the function "is monotonically decreasing". 

The paper first develops a formal framework for meta-learning with additional information (Section 3).

The paper then provides different empirical applications of the new paradigm using Neural Processes (Section 5).

The paper states that *"our work primarily focuses on putting forward the idea of informed meta-learning with the introduced
class of INPs serving mainly as an illustration."*

Overall, this is an interesting and well-written paper with limited empirical evaluation (which makes the currently submitted paper a marginal paper in my view).

I'm not very familiar with the meta-learning literature, and I decided to give this paper the benefit of the doubt with a score of 6.

strengths:
I find the research question quite interesting. It connects a classic topic in machine learning research—how to integrate human domain knowledge into the algorithm's inductive bias—with recent developments (LLMs). 

The paper is well-written, and the appendix documents the different experiments in detail. The paper illustrates key ideas and results with well-designed figures. 

The paper's theory seems sound (though I did not check the details), and the experiments are well-designed. 

Overall, this paper makes some interesting suggestions for how novel developments in LLMs can inform existing work in knowledge integration / meta-learning.

weaknesses:
The paper's main weakness is the empirical applications. Specifically, it does not provide a real-world application in which providing additional information in natural language improves upon other approaches (running somewhat contrary to the motivation as outlined, for example, in Figure 3).

The paper is aware of this and states that *"The aim of our work is not to present a new method that surpasses existing baselines on a benchmark dataset; rather, we propose a new viewpoint on meta-learning as a means of establishing an interface
between human domain knowledge and the hypothesis space of ML models."*

However, I'm uncertain how interested the ICLR audience will be in the methodological approach alone.

**Section 5.1:** The experiments in section 5.1 are all for the synthetic function y_i = ax_i + sin(b x_i) + c + epsilon_i. For this reason, the results in this section (while convincing) read like a case study, not a systematic assessment of the pro's and con's of the proposed method.

**Missing baselines:** I find the usefulness of the proposed method hard to gauge because the paper does not compare it with other existing methods. For example, the authors mention that Bayesian linear regression would be a natural method to use in the setup of Section 5.1. Then why does the paper not compare performance with Bayesian linear regression?

questions:
**Question 1:** Figure 3 contains nice examples, e.g. K = "This function is sharply decreasing." However, there seems to be no exmpirical application with K = natural language for a regression problem in the paper. Why not? Did the authors not try this, or did they try it, and it did not work? I would be quite interested to see if this can work well in practice. 

**Question 2:** Figure 4(b): I do not understand how the plot value for {a,b} can be in between {a} and {b}? should {a,b} not be strictly better than {a} and {b}?

**Question 3:** What exactly are the research contributions in Section 3? To me, this Section mostly reads like a summarization of different possible approaches, laying out the strategy the authors want to employ in the paper. 

**Question 4:** Concerning the general motivation, it is a bit strange to read some of this in 2024. For example, from the introduction: *"with the knowledge integration step often forming the core contribution of many ML papers"*. Do the authors actually believe that "the knowledge integration step" will be a core part of many ICLR'25 papers?

**References:** Some of the references need updating. e.g. 

"Chelsea Finn, Kelvin Xu, and Sergey Levine. Probabilistic Model-Agnostic Meta-Learning, 2019.
eprint: 1806.02817."

was published at neurips 2018.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730721210652

thread_type:
official_review

<thread_object>
creator: reviewer_66xm

<content>
summary:
The work proposes a viewpoint on how to integrate prior knowledge into the learning process via informed meta-learning. The idea is to incorporate knowledge that is conditioned on the generative function, but is not a unique representation of the function. The meta-learning algorithm can then be applied to incorporate this knowledge. Neural Processes are used for empirical studies of the approach, demonstrating the advantages of prior knowledge in three specific cases.

strengths:
The paper can potentially be applied to a lot of domains, where the expert knowledge and priors are presented.

The paper is overall clearly and well-written.

The proposed viewpoint is clearly illustrated based on a set of examples.

weaknesses:
One of the core ideas of the paper’s perspective is to use prior knowledge K to guide the learning system. To assess how applicable this viewpoint is to different problems and domains, more discussion is needed on how to construct K for different scenarios, as the examples provided in the paper are very specific and not necessarily realistic. More discussion is also needed on how the properties of K influence the viewpoint. For example, in 5.1.1, more knowledge corresponds to a stronger prior. Can this be formalized for a general viewpoint?

Could you please comment on whether it is possible to have a stronger statement in Theorem 1 with a strict inequality?

Minor:

Line 245: "mete-training" -> "meta-training"

Please name the axes in Figure 6.

questions:
For the MAML Example 1, how similarity between tasks is measured?

Could you please comment of how number of shorts or context points should be chosen while using the framework?
</content>
</thread_object>

</thread>
<thread>
creation date:
1731035137005

thread_type:
official_review

<thread_object>
creator: reviewer_eznr

<content>
summary:
The paper provides a theoretical framework around conditional meta-learning, oriented toward conditioning neural processes on natural language info about temporal processes.

strengths:
Nice concrete examples through the theory; good explanations and clear notation.

weaknesses:
Overall an interesting and well-written paper, but  seems like it's maybe missing references to conditional metalearning, and doesn't compare to the baselines it discusses early on. The theory seems like a lot of math to say things that are kind of vacuous, but such is math, and it's well explained with nice concrete examples. The biggest criticism I have is that the way K is given to the model, concretely, is very unclear, and in my experience (conditioning physics-informed neural nets, which come to think of it is another area that should be cited as relevant lit) this kind of 'technical detail' makes a huge practical difference.

questions:
Detailed comments by section:

- I would add "particularly" to the first sentence of the abs
 - almost very nice first fig, but caption could be improved (what are the different indices on Ks and how do they relate to K

2.  
- nice concrete examples of K in the problem setting
- not sure exactly how to phrase this, but it seems worth mentioning how strong the assumption of an underlying stochastic process is. e.g. in the nice explanatory figure, in the observable data space you have a sine and a line, and these are clearly different, but what we actually have in real life is often a bunch of high-D points that you could fit a sine or a line to with equal error and it doesn't mean that either of them is properly the "underlying" function. I'm not trying to say this assumption isn't fine to make, but I think it's worth emphasizing how abstracted it is from "real" data
  - similarly for the assumption of conditional independence of K and D that is necessary for the proof; it seems to me like it's a lot of math to say something pretty obvious; that if we add new necessarily true and relevant information that will improve things ... it would be nice to give an idea of how much the proof relies on those properties.

3. 
 - nice concrete examples but man the formatting is hard to read; please make it not all in italics!
 - the remarks section is a bit random and take up a lot of room relative to what they provide; suggest moving to appendix to make more room for easier to read formatting and extra concrete examples/explanations of experimental results and connections from theory to experiments.

4 
- the arrangement of the sections makes sense, but it does mean all the theory and acronyms about neural processes are really far from the empirical results; it would be nice to tie them together better with cross-references
- unclear how the aggregator operator works

5 
- definitely nice to have the empriical results but because of the synthetic setting it's hard to take anything from the results without the discussion being more concrete -- it would be nice to continue the concrete example pattern you had from the theory section (e.g. for the dist shift experiments, what was the K you provided?). 
 - why not use real weather forecasts?? introducing a LLM adds an unnecessary layer of complexity and potential for hallucinations 
- again unclear how the K is actually informing the NP -- are you encoding it with a pretrained LLM and appending that to the input of the neural process? adding? something else? these details are the technical core of the paper and it's important to make them clear.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732356790219

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Global response

comment:
We would like to express our gratitude to all the reviewers for their constructive feedback and insights on our submission.

We are encouraged by the reviewers' recognition of our work. The tackled research question was described as “interesting, as it connects a classic topic in machine learning research—how to integrate human domain knowledge into the algorithm's inductive bias—with recent developments (LLMs)" (**gpLZ**). The proposed approach was noted for its broad potential applicability, as it "can potentially be applied to a lot of domains, where the expert knowledge and priors are present." (**66xM**). The paper was also recognised for making "interesting suggestions for how novel developments in LLMs can inform existing work in knowledge integration / meta-learning." **(gpLZ**).

The reviewers also found the paper to be "overall clearly and well-written"(**66xM**), with "good explanations and clear notation." (**EZNr**) and highlighted the inclusion of "nice concrete examples through the theory" (**EZNr**) and that the "proposed viewpoint is clearly illustrated based on a set of examples" (**66xM**) and “experiments are well-designed” (**gpLZ**).

We thank the reviewers for their positive feedback and appreciate the opportunity to clarify and enhance our manuscript based on the comments received. Below we outline the key actions taken in response to the reviewers’ comments. Remaining queries have been addressed in individual responses under each review.

### Summary of key actions taken

In response to reviewer 66xM's questions, we have extended the theoretical discussion following Theorem 1. This now includes an analysis of the conditions under which the presented inequality becomes strict, as well as a formalisation of the intuition that more informative knowledge representations lead to improved predictions. These additions can be found in Appendix 1.3.3 of the updated manuscript. Additionally, we have addressed minor typos, made stylistic changes, and updated the references.

### Updated manuscript

Based on the helpful and detailed feedback of the reviewers we have updated our manuscript and uploaded the revised version. All changes made are highlighted with a blue text color. We hope that the proposed updates improve the paper’s clarity, ease of understanding, and that they address the reviewers’ questions and concerns. 

---

We are grateful for the reviewers' feedback, which helped us improve the presentation of our work. We are open to further discussions to clarify any aspects of our submission.

Kind regards,

The authors of submission #11605
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper explores how prior knowledge represented (e.g. in natural language) can be incorporated into machine learning models. Key idea centers around informed meta-learning (i.e. learning to learn).  This is a theoretically leaning paper and describes the principles of informed meta-learning and inductive bias selection. 

I recommend acceptance as all reviewers are positive about the approach - the biggest criticisms centers around clarity and empirical evaluation. The idea itself is novel and sufficient discussion is provided to analyze the approach.