Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance

Summary

The study introduced a specialized dataset and learning approach that aimed to enhance LLMs' use of generic facts in reasoning. The results indicate that this methodology not only improved their general reasoning skills but also significantly developed their abstract reasoning abilities, suggesting a shift from mere memorization to a deeper, more nuanced understanding and application of information. The paper is well composed and demonstrates good, consistent performance across several benchmarks.

Strengths

- the concept of AbsAcc is interesting and natural, as demonstrated by the experiments in Table 1, where the small difference between vanilla accuracy and AbsAcc for human subjects highlights that vanilla accuracy alone may not be an adequate metric for assessing abstract reasoning. - the paper is well-composed and straightforward to comprehend.

Weaknesses

- the experiments currently focus on earlier versions of LLMs, including LLaMA-2 and GPT-3.5-turbo-0125. It is advisable to also include more recent models like LLaMA-3 and GPT-4/4o to ensure the consistency of the performance evaluations. - some of the terminology used appears complex; for instance, in the memory learning section, the distinction between Knowledge and Reasoning examples (K-example and R-example) seems minimal. In the K-example, when a user poses a question, a supporting fact is cited while the R-example not. Additionally, I'm confused by the definition of abstract reasoning. It looks akin to tasks in commonsenseQA (https://aclanthology.org/N19-1421.pdf). The authors might need to elucidate the differences between their approach and that body of research.

Questions

- see the questions in weakness section

Rating

6

Confidence

4

Soundness

3

Presentation

4

Contribution

3

Limitations

- some of the terms mentioned in the paper maybe a little bit fancy and overestimated, please refer to the weakness section.

Summary

This paper explores the abstract reasoning abilities of LLMs by creating a specific evaluation metric and a dataset called AbsR, developed using GPT-4. It presents a method, Meaningful Learning (MeanLearn), to improves both general and abstract reasoning accuracies of LLMs by teaching them to apply generic facts in varied scenarios.

Strengths

- Proposing a dataset to study the potential of LLMs in abstract reasoning. - Proposing MeanLearn to improve the abstract reasoning capabilities of LLMs by teaching them to use generic facts for reasoning.

Weaknesses

- The paper is rushed, and not well organized. For instance, Some experimental results are presented in section 2, and others appear later in section 5 and after. - The methodology section is half a page. More details on the model is needed. For instance, in Eq. 3, how the first and second equations will be utilized in the model. - Incomplete related work section, specifically, insufficient related work on Abstract Reasoning. - Some experimental results are in section 2: Abstract reasoning study, some later in section 5: Experiments. In section 2, the paper jumps into some results and experiments too early and without proper background. - Experiments are limited. For example, it is unclear how the methods compare to [1] - [1] Xu, Yudong, et al. "LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations." Transactions on Machine Learning Research.

Questions

- How the methods introduced in the paper can be compare to [1]? - The paper states that "Our findings reveal a substantial discrepancy between their general reasoning and abstract reasoning performances". However, based on [2], LLMs "know" more than they can "say". Please justify how your statement remains valid. - Considering that MeanLearn does not have many advantages over LLaMA-3, how do you justified the use of MeanLearn? - [1] Xu, Yudong, et al. "LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations." Transactions on Machine Learning Research. - [2] Li, Kenneth, et al. "Inference-time intervention: Eliciting truthful answers from a language model." Advances in Neural Information Processing Systems 36 (2024).

Rating

3

Confidence

5

Soundness

2

Presentation

1

Contribution

2

Limitations

- Lack of sufficient experiments. - Limited related work, and background sections. - A thorough writing revision would be beneficial.

Summary

This paper introduces a novel framework aimed at enhancing the abstract reasoning capabilities of large language models (LLMs) through a method called "Meaningful Learning." It specifically targets the challenge LLMs face in abstract reasoning despite their robust general reasoning abilities. The authors identify a notable gap in performance between general and abstract reasoning tasks and propose a structured approach to narrow this gap by using AbsR, a tailored dataset that includes generic facts coupled with guided explanations to foster deeper learning and understanding. Key contributions include: 1. Introduction of the Meaningful Learning framework for improving abstract reasoning in LLMs. 2. Development of the AbsR dataset for training. 3. New metrics and Empirical evaluation across different settings

Strengths

1. Novel approach to improving abstract reasoning in LLMs through generic fact guidance. 2. Comprehensive evaluation across multiple settings demonstrating reasonable performance improvements.

Weaknesses

1. Limited scale: The experiments are conducted on relatively small models (7B-13B parameters) compared to state-of-the-art LLMs. Without such it's hard to judge such method's applicability in real world and more complex tasks as mentioned in 5.1. 2. Human evaluation scale: The human evaluation of the AbsR dataset is conducted on a relatively small sample (200 instances) (Appendix E). 3. Lack of comparison to more recent reasoning techniques: The paper doesn't compare MeanLearn to recent advances in LLM reasoning capabilities. 4. Weaknesses in Evaluation Metrics: It's not convincing that Perplexity-based evaluation for classification tasks is good and the use of AbsAcc needs more clarification.

Questions

1. How did you determine that perplexity-based evaluation was the best approach for classification tasks, given its limitations mentioned in Appendix A? Were other methods considered? 2. Have you considered comparing MeanLearn to more recent reasoning techniques, such as tree-of-thoughts or other advanced prompting methods 3. The paper mentions that "MeanLearn can make LLMs implicitly learn generic facts and solve problems under the guidance of generic facts" (Section 4). Can you provide more concrete examples or analysis of how this implicit learning occurs? 4. How do you envision MeanLearn scaling to larger models (100B+ parameters), and what challenges or benefits do you anticipate in applying this method to more advanced LLMs? 5. how do you ensure that the AbsAcc metric is truly capturing abstract reasoning abilities rather than other factors like memorization or pattern matching?

Rating

5

Confidence

3

Soundness

2

Presentation

3

Contribution

2

Limitations

Yes

Summary

This paper addresses the challenge that LLMs face in abstract reasoning, where they often struggle to apply general facts to new situations despite their impressive performance in other reasoning tasks. To tackle this issue, the authors introduce an abstract reasoning dataset called AbsR, which incorporates generic facts and guided explanations to teach LLMs how to leverage such facts for reasoning. They also propose a learning paradigm named Meaningful Learning (MeanLearn) that simulates the human process of implicit knowledge acquisition, enabling LLMs to implicitly learn and apply generic facts without explicit input. Through experiments on various out-of-distribution reasoning and language understanding benchmarks, the paper demonstrates that MeanLearn improves the general and abstract reasoning capabilities of LLMs, moving beyond simple memorization towards a more nuanced understanding and application of knowledge.

Strengths

1. Useful Resource: This paper introduces an abstract reasoning dataset (AbsR) that provides generic facts and guided explanations for reasoning tasks. The dataset and code will be publicly available, facilitating reproducibility and further community research. 2. Empirical Evidence: Comprehensive experimental results and ablation studies that validate the effectiveness of the proposed method. Improvements are observed in both general and abstract reasoning performance of LLMs across various benchmarks. 3. Broad Applicability: The approach's effectiveness is shown on multiple LLMs of varying sizes, indicating broad applicability.

Weaknesses

1. Evaluation on Advanced LLMs: The observation that the benefits of MeanLearn seem to diminish with better-trained LLMs, like LLaMA3, raises a crucial question about its broader applicability. While the method shows promise with smaller models, it's essential to assess its performance on more powerful LLMs like Mistral, Phi3, and Qwen 2. This would provide a clearer picture of its potential contribution in the context of rapidly advancing language models. 2. Data Augmentation and Training Dynamics: The use of GPT4 for constructing the AbsR dataset, with annotation quality comparable to human annotators, opens up interesting possibilities for data augmentation. Exploring the impact of increasing the training data size, potentially using more cost-effective models like GPT4o or open-source LLMs, could reveal the potential for further performance gains. Additionally, analyzing the training dynamics of MeanLearn by varying the proportion of training data (e.g., 20%, 50%, 80%) would provide valuable insights into its saturation point and the diminishing returns of additional data. 3. Expanding Reasoning Benchmarks: The paper's focus on commonsense reasoning benchmarks (Com. and ARC) should be complemented by evaluation on other reasoning domains, such as arithmetic reasoning. Including benchmarks like GSM8K or MATH would provide a more comprehensive understanding of MeanLearn's capabilities across different reasoning tasks. 4. Presentation Clarity and Improvements: The paper's presentation could benefit from several improvements. The introduction should explicitly connect abstract reasoning with other types of reasoning, providing a broader context for the research. The pilot investigation in the introduction is quite confusing as it does not introduce the datasets, metrics, and setup used. Confusing figures, like Figure 2, should be explained in detail to ensure clarity and understanding. 5. Updated Related Work: The related work section should be updated to include recent efforts in tuning-based methods, ensuring a comprehensive overview of the current landscape in abstract reasoning research.

Questions

See above,

Rating

6

Confidence

3

Soundness

3

Presentation

2

Contribution

2

Limitations

The authors adequately addressed the limitations.

Follow-up Rebuttal

### Dear Reviewer zthW: We appreciate your efforts in the review process. As the rebuttal deadline approaches, we kindly ask if our responses have sufficiently addressed your concerns. If you require further clarification, we are prepared to provide additional information. Sincerely, Authors

Follow-up Rebuttal

### Dear Reviewer Fwhs: We appreciate your thoughtful review. As the rebuttal deadline approaches, we kindly ask if our responses have sufficiently addressed your concerns. If you require further clarification, we are prepared to provide additional information. Sincerely, Authors