Enhancing LLM's Cognition via Structurization

Summary

The paper presents a method to improve the cognitive capabilities of large language models (LLMs) by organizing input context into a hierarchical structure. The method is called context structurization, and it involves transforming unordered contextual sentences into hierarchically structured elements to mimick human cognitive processes. The paper shows the effectiveness of the approach through evaluations on various NLP tasks, such as context-based question-answering, hallucination evaluation, and passage-level dense retrieval, using different model architectures and sizes. The study shows performance improvements and introduces StruXGPT-7B, a distilled model, to perform structurization efficiently.

Strengths

- The study covers a wide range of tasks, providing a comprehensive assessment of the method's effectiveness. - I like the idea of distilling the structurization capability into a smaller model (StruXGPT-7B). - Even though the approch lack a bit of novelty it seems to work.

Weaknesses

- The performance improvements are heavily dependent on the quality of the structurization process, so a poor structurization could lead to suboptimal model performance or even confusion. - Even though there is a performances improvement the process of structurizing context might introduce additional computational overhead. - The approach lack a bit of novelty, it doesn't introduce fundamentally new concepts and resembles methodologies from previous studies. - The study relies on metrics such as ROUGE-L and human evaluation, that are useful but they might not fully capture the more complex improvements, such as a cognitive-like processing that structurization aims to achieve.

Questions

- How does the proposed three-layer structure handle texts with more complex relationships or multiple topics? - What are the computational costs associated with context structurization?

Rating

6

Confidence

5

Soundness

3

Presentation

3

Contribution

2

Limitations

- The study heavily relies on specific models like LLaMA2 and Qwen, so i wonder about the generalizability of the results to other LLMs. - The structurization may be less effective working with extremely long contexts or when the context cannot be easily divided into clear hierarchical segments.

Summary

This paper proposes a new technique for prompting Large Language Models (LLMs) called StruXGPT. The basic idea is to transform the original prompt into a more structured description of the request which contains three levels of information: Scope, Aspects, and Descriptions. While the **Scope** provides an outline summary of the request, the **Aspects** present an itemized and well-ordered list of topics that are associated with their respective **Descriptions** which provide the details. In the paper, the prompt transformation is achieved by either using another large model or by a smaller model obtained by knowledge distillation. The experiments show that this type of structure can help LLMs find the right information needed to answer the requests, improving performance in question-answering tasks and reducing model hallucinations.

Strengths

The proposed technique is useful and easy to implement. As hallucination is currently one of the main challenges faced by real-world systems that employ LLMs, any technique that reduces it should be considered.

Weaknesses

In the abstract and introduction of the paper, there are claims about how human cognition works that are poorly backed up. This is an area of debate and I believe this discussion could be avoided without any harm to the main point of the paper. The proposal assumes that the prompt transformation process is an easier task for an LLM than directly addressing the request. Although this seems to be the case for question-answering problems considered, there is no discussion about cases in which this may not be adequate. I believe more experiments with other types of datasets would help us to better understand that. Finally, as there are other papers investigating similar approaches, the novelty of the approach is not a strong point.

Questions

The legend of Table 1 mentions that the prefix Struct- indicates the data fed into LLMs is structured by StruXGPT-7B. However, I don't see this prefix in the table.

Rating

5

Confidence

4

Soundness

3

Presentation

3

Contribution

2

Limitations

Yes, the limitations were well addressed in the paper.

Summary

The paper presents a novel approach to improve the cognitive capabilities of large language models (LLMs) without inferring the model by structuring contextual information hierarchically. The authors propose transforming plain, sequential text into a structured format, enabling LLMs to process and understand complex information more effectively. This method is empirically tested across various NLP tasks, demonstrating consistent performance gains. Additionally, the paper introduces StruXGPT, a distilled 7B model that efficiently executes the structurization process, enhancing the results on context-based QA benchmark, Hallucination Evaluation and Passage-level dense retrieval.

Strengths

1. While structurization of text is not a novel concept itself, the development of StruXGPT, a smaller and more efficient model for structurization, is a significant contribution and it demonstrates the practical applicability of the proposed method. 2. Evaluation and experiments are strong parts of paper, authors provide many detailed experiments with proper comparisons. The demonstrate stable performance gains using proposed method. 3. The paper thoroughly describes the structurization process, including the use of linguistic markers and transformation templates, ensuring clarity and reproducibility. 4. Authors made the research deeper by analyzing attention mechanism on structured texts.

Weaknesses

1. Lack of parameter-efficiency analysis: authors provide 7B model for structurization process, but it is not clear whether smaller or bigger models would significantly hurt or benefit the approach. 2. While the proposed approach shows stable improvements, it is still a lot of additional parameters, which is a concern for scalability to large amount of texts.

Questions

1. Did you experiment with smaller/bigger models for structurization? If yes, what were the results? 2. Are there specific tasks in which such structurization could be worse? 3. Did I understand correctly that the data for training StruXGPT was gather by quering bigger model? Did you analyze the training set in details and do you think the proposed approach would benefit from training data gather or annotated by humans? I am personally worried here about the hallucinations that could be inherited from the bigger model. 4. Do you plan to publish your StruXGPT model? Also in Table 1 you write "The prefix Struct- indicates..." but there is not such prefix in the table. Did you mean "+StruXGPT (ours)" here?

Rating

8

Confidence

3

Soundness

3

Presentation

4

Contribution

3

Limitations

1. Paper investigates only inference stage 2. Introducing StructXPGT is still additional computation cost, however authors provide the analysis of extra cost in appendix

Summary

This paper introduces the concept of context structurization to enhance the comprehension capabilities of large language models (LLMs) for long texts. The authors propose summarizing the input text into a three-layer structure of Scope-Aspect-Description using LLMs, and then inputting this three-layer structure as an enhanced version into the LLM. Additionally, the authors propose to optimize a 7B model by distilling the structuring capabilities of giant commercial LLMs to reduce computational costs. The effectiveness of the method is validated through experiments on multiple NLP tasks. The experiments also demonstrate that the fine-tuned 7B model can inherit most of the structuring capabilities of the giant commercial LLMs.

Strengths

The paper is well-written and well-structured, making it easy to understand. The proposed method is technically sound. The effectiveness of context structurization has been validated across multiple datasets in Context-based Question-Answering, Context-based Summarization, and Passage-level Dense Retrieval, showing improvements on a number datasets.

Weaknesses

The experiments are not sufficiently comprehensive. Firstly, many tables only compare the scenarios with and without StruXGPT (except for Table A2 and A5), without comparing against other advanced prompt engineering methods. Therefore, it is hard to determine whether StruXGPT enhances the cognitive abilities of LLMs more effectively than other methods. Secondly, the authors did not validate the effectiveness of StruXGPT on state-of-the-art LLMs such as GPT-4, so it is unclear whether GPT-4 would still perform well without context structurization. There are also some related summarization-based augmentation methods that could be discussed or compared [1]. [1] Cheng, Daixuan, Shaohan Huang, and Furu Wei. "Adapting Large Language Models via Reading Comprehension." In The Twelfth International Conference on Learning Representations.

Questions

How does the number of in-context examples affect the results of structurization, and why was the number set to 2?

Rating

5

Confidence

4

Soundness

3

Presentation

3

Contribution

2

Limitations

Yes.

Thank you for your reply.

Your explanations are very clear, and I appreciate that you plan to add the experiments and discussions about W1 in the final version. Because of this, I’ve decided to raise my rating to 6.

Response for the rebuttal

I appreciate the authors' responses to my questions, which addressed part of my concerns. However, merely comparing chain-of-thought (CoT) and only comparing AdaptLLM on a single dataset is not sufficiently convincing for me. In AdaptLLM paper, there are other datasets where AdaptLLM performs significantly better than the baseline. How does the authors' method perform on these datasets?