Paper: #InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models (ICLR-2024)

Reviewer comment (Experiments):
The method’s reliance on a strong tagging model (in this case, ChatGPT) is not explored, despite a very brief mention of training a distilled tagger model at the end of the work. It would be interesting and useful to see how well this method works with openly available models, or over a variety of different quality models (e.g. how does using GPT-4 as a tagger compare? How about Vicuna? etc.).

Author response:
We thank reviewer NPyy for proposing this valuable analysis. We ran a quick analysis and found 92% of the 6K SFT samples we used to train TagLM (selected based on ChatGPT tags) is still collected based on local tagger’s tags via the complexity first diverse sampling. This overlap and the F1 on the validation set we reported in the manuscript provide evidence of the consistency between ChatGPT and local tagging. We will train a local tagger-based TagLM and report the end-to-end performance in our camera-ready version. We will release the ChatGPT-tagged data pool and local tagger to promote the development of more precise, cheap, and open-source tag annotators.

Reviewer comment (Evaluation):
The tag-based analysis is somewhat restrictive, as discussed in section 4.3. It would be interesting to take the semantics of the tags themselves into account somehow, since (a) some tags may be closer to each other and so overlap in terms of diversity, and (b) certain tags may express queries that are naturally more complex than others (e.g. ‘solve’ vs ‘inquiry’ tags).

Author response:
We agree with the two limitations pointed out by reviewer NPyy, especially the second limitation. We have constrained the minimum semantic distance between tags in the normalization described in Section 3 to avoid limitation (a) due to synonym tags. We consider involving the semantic distance between tags in the diversity metric a valuable future work. Limitation (b) is mainly due to the simplicity of our complexity definition.

Reviewer comment (Evaluation):
Does the average number of tags per instance correlate well with human intuitions of complexity?

Author response:
Yes, we will add a case study to present the correlation between human intuitions of complexity and our metrics. We notice queries with more tags contain multiple intentions or more complex constraints.

Reviewer comment (Experiments):
How well does your distilled tagger perform if used as a tagger for data selection? Did you test with different models?

Author response:
Please kindly refer to the response of W1.

Reviewer comment (Evaluation):
How does TagLM perform over other benchmarks (e.g. MMLU, HumanEval)? Are there any particular capabilities or skills it seems to underperform in compared to baselines?

Author response:
Please kindly refer to the general response. As for the second question, we have some insights from detailed MT-Bench results in Table 8. We notice TagLM only shows marginal improvements on mathematic questions, which is somehow reasonable as existing works show that enhancing reasoning abilities may require tremendous supervision.

Reviewer comment (Experiments):
The primary mechanism of the proposed method is dependent on the automated, open-ended tagging capability of GPT-4; therefore, there is a risk that the analysis in this paper might be influenced by any inherent biases present within GPT-4.

Author response:
We fully agree with the conjecture that ChatGPT may contain a certain inherent bias that influences the tagging results. We also consider it one of our main challenges and develop a normalization procedure to mitigate this issue. For example, ChatGPT sometimes provides associated tags, so we conduct association analysis to combine taggers that always appear together. Besides, this concern is more related to ChatGPT than the methodology of InsTag, as InsTag can easily adopt stronger and unbiased automatic annotators in the future.

Reviewer comment (Evaluation):
Further examination of data instances categorized by the proposed metrics would be advantageous. Specifically, exploring the semantic or syntactic traits defining IFT datasets identified as diverse and complex by these metrics would be informative.

Author response:
We sincerely appreciate the reviewer pointing out this idea, and this is a great starting point to further dive deep into our metrics.

Reviewer comment (Theory):
The presented work is largely empirical, which may raise concerns within the community regarding the foundational grounding of its results.

Author response:
We agree with the reviewer jYjU that this work mainly empirically shows the effectiveness of diversity and complexity of instructions. We also consider a more foundational interpretation as our valuable future works.

Reviewer comment (Experiments):
The proposed method appears to be sufficiently versatile to extend beyond instruction fine-tuning applications. Could you provide additional examples where the efficacy of this method could be demonstrated?

Author response:
Yes, InsTag can be extended to other scenarios as it generally proposes an automatic tagging method for text. We are tagging intentions and semantics of instructions in this work, but it can also be used in annotating information extraction samples where tags are the expected information, classification tasks where tags are the categories, and so on. However, it is more challenging to tag open-set instructions, and how intention tags reveal the diversity and complexity of the instruction tuning dataset is currently significantly understudied. So, we discuss InsTag in the context of instruction tuning in this manuscript.

Reviewer comment (Novelty):
Selecting more diverse and complex samples is an existing idea, and this article is more similar to the implementation and application of this idea.

Author response:
We agree with reviewer 32G5 that “diverse and complex samples will benefit instruction tuning” is a widely accepted insight nowadays as methods for diversifying and complexing SFT data emerge. However, we would like to emphasize that the motivation of InsTag is not only to provide solid empirical evidence for this claim but also to investigate reasonable and easy-to-use metrics for diversity and complexity. For example, we try to answer a research question such as both WizardLM and shareGPT may contain “diverse and complex” instructions but which is more “diverse and complex” and how it leads to the final performance. InsTag provides straightforward metrics using open-set tags to quantify these observations, and we show the correlations between our metrics and final performance.

Reviewer comment (Theory):
The definition of complexity seems a little strange. Is there any explanation from other papers? If not, I hope the authors can give a more detailed explanation for it.

Author response:
We thank the reviewer for pointing out the vague of our definition of instruction complexity based on tags. Tags initially inspire this definition in the recommendation system. More tags imply the query is multi-facet, in other words, complex in instruction tuning as it reveals more intentions and constraints. We adopt it for its simplicity and effectiveness. Besides, the analysis in Section 3 and Section 4.3 also shows its effectiveness as a trustworthy metric.

Reviewer comment (Experiments):
The author is recommended to verify the performance of the proposed method on more benchmarks.

Author response:
Please kindly refer to the general response.

Reviewer comment (Evaluation):
Evaluation is a bit weak, MT-Bench scores are the only metric used across entire paper – other datasets such as AlpacaEval and human evaluation could further strengthen the claims in the paper.

Author response:
We thank reviewer jmr3 for recommending AlpacaEval and human evaluation. We have added more evaluation to strengthen our claims. Please kindly refer to the General Response.

Reviewer comment (Experiments):
While I appreciate that the authors distill a local tagger in Section 5, it is unknown how much SFT performance would be sacrificed by using the tags from this local tagger. The strong results on Table 3 is from ChatGPT tagger if I understand correctly, and it may be too expensive to use ChatGPT in practice when we have a large data pool to measure.

Author response:
Yes, the result in Table 3 is from the ChatGPT tagger and our corresponding normalization procedure. And we also agree with the reviewer that annotating tags with ChatGPT is very expensive. To address reviewer jmr3’s concern, we ran a quick analysis and found 92% of the 6K SFT samples we used to train TagLM (selected based on ChatGPT tags) are still collected based on local tagger’s tags via the complexity first diverse sampling. This overlap and the F1 on the validation set we reported in the manuscript provide evidence of the consistency between ChatGPT and local tagging. We will train a local tagger-based TagLM and report the end-to-end performance in our camera-ready version. We will release the ChatGPT-tagged data pool and local tagger to promote the development of more precise and cheap tag annotators.