Summary
The paper presents AMPLIFY, an approach that uses post-hoc explanations from a proxy model to improve the prompting performance of large language models. For a given dataset, the approach assumes access to a set of labeled validation data that is used for crafting a prompt. First, the approach selects k examples that are misclassified by LLMs and exhibit high misclassification confidence scores. Next, post-hoc explanation techniques are used to find the most important input features of these selected examples, which are later used to construct template-based rationales. The rationales are used in the final prompts following the chain-of-thought paradigm.
The paper conducts experiments on 7 datasets from Big-Bench-Hard. Experimental results suggest good improvements over the few-shot answer-only prompting (AO) baseline and the few-shot CoT baseline. The paper also includes analyses suggesting the importance of example selection strategies as well as the impacts of using different proxy models and explanation techniques.
Strengths
The paper studies an interesting problem that focuses on using post-hoc explanations for improving LLMs’ performance, which is relatively new.
The experiments cover 7 datasets and show performance improvements compared to few-shot baselines.
The paper provides some useful analyses, especially the analysis of the example selection strategies.
The paper is well-written and easy to follow.
Weaknesses
1: The major comparison is unfair in some way
The proposed approach uses a validation set (the size of the validation set is also not mentioned in the paper, nor in the appendix) and selects examples from the validation set. By contrast, standard prompting and chain-of-thought prompting typically only use few-shot examples. So part of the improvements might also be attributed to the use of more data in addition to the prompting method proposed in this paper (see weakness 2). In addition, the paper does not compare against approaches that can also use the validation set instead of just a few-shot examples.
2: However, the experiments do not clearly suggest the effectiveness of using post-hoc explanations
The proposed approach uses the LLM itself + smaller proxy models to find more informative examples to be used in the prompts (the selection strategy). This active selection strategy does not rely on using post-hoc explanations and can be applied to answer-only prompting or chain-of-thought prompting as well. It would be good to provide AO and CoT performance using the selected examples as well to give a better understanding of the effectiveness of using better examples versus the effectiveness of using post-hoc explanations in prompts.
The paper does provide analysis of the impacts of using different selection strategies. As shown in Table 3, using random examples, the performance of AMPLIFY is only 59.3 on GPT3 and 62.0 on GPT-3.5; at the same time, according to Table 1, CoT performance is 58.0 on GPT3 and 62.9 on GPT-3.5. IIUC, this suggests CoT and AMPLIFY is comparable; and selecting better examples contribute the majority of the performance improvements.
While it seems that most of the improvements come from the selection strategy, the paper does not compare against other approaches that involve active annotating examples for in-context learning (e.g., Su et al. (2022); Diao et al. (2023)). Also, this proposed selective annotation strategy does not distinguish itself from existing work, which uses similar confidence-based ways to actively annotate examples (Su et al., 2022; Diao et al., 2023).
Selective Annotation Makes Language Models Better Few-Shot Learners (Su et al., 2022)
Active Prompting with Chain-of-Thought for Large Language Models (Diao et al., 2023)
3: The paper also misses some important details on the approach and the baseline
Regarding the proposed approach:
* When getting the post-hoc explanations. Does it explain the ground truth label or the predicted label? (it is not clearly explained in line 172)
* How does the method get the initial predictions, in order to determine the misclassified examples? Is it obtained using few-shot AO or few-shot CoT?
* It seems no information regarding the size of the validation set is provided.
Regarding the baselines:
* It seems the CoT baselines and AO baselines use few-shot prompting. How is the shot selected? Are they randomly selected? How many shots are used?
* How are the CoTs for the CoT baseline written? Based on line 129, the CoTs are taken from Wei et al. (2022), but the original CoT paper does not include prompts for these datasets. Are they from Suzgun et al. (2022)?
In addition, it would be also helpful if the appendix can include some prompt examples.
4: The paper uses a subset from big-bench-hard that mainly focuses on understanding linguistic concepts.
On these datasets, CoT typically leads to no improvements or minor improvements, compared to AO. While it is understandable that the paper chooses to focus on understanding linguistics concepts, it would still be beneficial to benchmark the effectiveness of this approach on a broader range of multi-step reasoning tasks (e.g., GSM) where CoT shows substantial improvements over AO. And on these tasks, using rationales consisting of just top-k words may be less effective.
Rating
5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
Confidence
4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Limitations
The paper discusses the limitations in appendix.