SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning

Summary

The paper titled "SELF-GUIDE: Better Task-specific Instruction Following via Self-Synthetic Finetuning" presents a novel approach to enhance the performance of large language models (LLMs) on specific tasks without relying on external training signals or additional data. The authors propose a multi-stage mechanism called SELF-GUIDE, which synthesizes task-specific input-output pairs using the LLM itself and then employs these pairs to finetune the model. The paper reports significant improvements in performance on classification and generation tasks as measured by the Natural Instructions V2 benchmark, with absolute improvements of approximately 15% and 18%, respectively.

Rating

6

Confidence

4

Ethics flag

1

Reasons to accept

1. **Innovative Methodology**: The SELF-GUIDE framework introduces a unique self-synthetic finetuning approach that allows LLMs to become more proficient in executing task-specific instructions, which is a significant advancement in the field of AI and language model training. 2. **Empirical Findings**: The paper provides robust empirical evidence demonstrating the effectiveness of SELF-GUIDE, with substantial performance improvements over the baseline model, which adds credibility to the proposed method. 3. **Addressing Data Scarcity**: The SELF-GUIDE mechanism adeptly tackles the challenge of data scarcity for underrepresented tasks, offering a scalable solution that does not depend on the availability of abundant annotated datasets. 4. **Comprehensive Evaluation**: The authors have conducted a thorough evaluation of SELF-GUIDE across a range of tasks from the Natural Instructions V2 benchmark, which showcases the method's versatility and generalizability. 5. **Quality of Writing**: The paper is well-structured, with a clear explanation of the problem, the proposed solution, and the results. The authors have also provided a detailed analysis and discussion of the results.

Reasons to reject

1. **Limited Language Focus**: The experiments and models are focused solely on English NLP tasks, which may limit the applicability of the findings to other languages and could potentially exacerbate existing language inequalities. 2. **Scalability with Larger Models**: The paper has only demonstrated SELF-GUIDE's effectiveness on a 7B-parameter model. It is unclear how the approach scales with larger models, which is a significant consideration given the trend towards increasingly larger LLMs. 3. **Potential for Misuse**: The paper acknowledges the risk of SELF-GUIDE being used for malicious purposes due to its ability to improve instruction following. This ethical concern should be addressed more thoroughly, possibly with proposed mitigation strategies. 4. **Lack of Comparative Analysis**: The paper could benefit from a more comprehensive comparison with other contemporary methods or baselines in the field to better situate the novelty and advantage of SELF-GUIDE.

Questions to authors

1. **Cross-Linguistic Evaluation**: Could the authors comment on the potential of SELF-GUIDE to be effective across different languages, and what are the challenges and considerations for such an extension? 2. **Scalability Analysis**: How does SELF-GUIDE perform with larger models, and what are the expected computational and scalability challenges? 3. **Ethical Safeguards**: What safeguards are proposed to mitigate the potential misuse of SELF-GUIDE, especially concerning the creation of task-specific models for harmful purposes? 4. **Comparison with State-of-the-Art**: Can the authors provide additional comparisons with other leading methods to further establish the superiority of SELF-GUIDE?

Summary

SELF-GUIDE works by first employing the target model to generate a synthetic dataset for a given task. The model is then finetuned on this “self-generated” data. The proposed method is similar to Self-Instruct work, which is mainly for general domains. And SELF-GUIDE is more on domain specific tasks and can effectively self-generate hundreds of examples for a given instruction.

Rating

5

Confidence

4

Ethics flag

1

Reasons to accept

1. The paper is well rewritten 2. The proposed method is simple and effective

Reasons to reject

1. The data generation and filtering strategy may vary across tasks. The evaluation tasks need to be more comprehensive. It would be better to test more general tasks, such as MMLU, BBH, etc. 2. Considering the smaller scale of the experiment, it would be better to report the variance. 3. Need some ablation study on Rule-based Filters. 4. The proposed method is still very similar to self-instruction, although in a different setting. And similar settings have also been explored before, such as "It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners". The authors need a better data-generation method than the proposed one in the paper.

Summary

The paper introduces a novel multi-stage mechanism, SELF-GUIDE, which uses self-synthesized data for finetuning language models on task-specific instructions without external data or teacher models. This is a significant advancement in making LLMs more efficient and autonomous.

Rating

7

Confidence

4

Ethics flag

1

Reasons to accept

The empirical evaluation shows substantial improvements in task performance (15% for classification tasks and 18% for generation tasks) when using the SELF-GUIDE approach, compared to traditional prompting and finetuning methods. This indicates the method's effectiveness.

Reasons to reject

While I do not have a strong reason to outright reject this paper, there might be a need to delve deeper into its connection with other relevant works in the field, particularly concerning data curation and enhancement techniques for language models. For instance, it could be beneficial to discuss and compare this paper's approach with methodologies outlined in reference [1] "Automated Data Curation for Robust Language Model Fine-Tuning." Such a comparison could elucidate how the SELF-GUIDE method aligns with, diverges from, or improves upon existing strategies for generating and utilizing synthetic data to train language models more effectively.

Summary

The paper proposes Self-guide, a multi-stage strategy to synthesize task-specific input-output pairs from a student LLM, and finetune the student model itself. This approach addresses the challenges of cost and scalability issues of using external powerful LLMs for generating task-specific data. Empirical evaluations on multiple individual tasks from Natural-Instructions-v2 demonstrate performance improvements for classification and generation tasks, surpassing ICL and few-shot finetuning.

Rating

4

Confidence

4

Ethics flag

1

Reasons to accept

Pros: 1. Customizing LLMs as task specialists is an important direction that has received relatively less attention. 2. Using self-generated synthetic data is well-motivated, and this paradigm has been explored and justified as reasonable in many scenarios.

Reasons to reject

Cons: 1. This paper posits a scenario in which there is a lack of sufficient annotated, task-specific data. However, it cannot ensure that the tasks used in the experiments are entirely new to Vicuna or even Llama. It is likely that numerous in-distribution training instances were included during pre-training, such as those found in crawled web data. This situation has already been acknowledged in some evaluation benchmarks. Given that complete decontamination of the dataset is infeasible, it is not possible to confirm whether the proposed pipeline enhances task-specific knowledge or capabilities within the current experiment design. 2. More baselines of task-specific data synthesis should be discussed and compared, e.g., ProGen[1] and Zerogen[2]. 3. The contribution of rule-based filters to overall quality improvements is not demonstrated, and their generalization when handling more diverse tasks raises concerns. [1] ProGen: Progressive zero-shot dataset generation via in-context feedback [2] Zerogen: Efficient zero-shot learning via dataset generation

Questions to authors

See the pros and cons above. Additional: 1. What is the prompt for the ICL baseline, and whether this prompt is optimized for the aligned model (i.e. vicuna in the experiments)?