Summary Explaining Large Language Models with Self-Explanations arxiv.org
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Self-explanations from large language models are compared to traditional methods for sentiment analysis, revealing similarities in faithfulness but differences in agreement metrics, highlighting the cost-effectiveness and interpretability challenges of self-explanations, while acknowledging the need for further research.
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Key Points
- Large language models like ChatGPT can generate self-explanations along with their responses, providing insights into their predictions.
- Researchers investigated the quality of self-explanations generated by ChatGPT and compared them to traditional explanation methods such as occlusion and LIME saliency maps.
- ChatGPT's self-explanations performed on par with traditional methods in terms of faithfulness, but there were notable differences according to various agreement metrics.
- Self-explanations generated by ChatGPT were much cheaper to produce as they were generated along with the predictions.
- Current evaluation methods have limitations in assessing the effectiveness of self-explanations, and further research is needed in this area.
- ChatGPT's self-explanations challenge current model interpretability practices and suggest the need for rethinking the interpretability pipeline for large language models.
- The study used ChatGPT for experiments on sentiment analysis and evaluated the accuracy of models with self-explanation generation compared to models without any explanation generation.
- The study highlights the need for future research in evaluating other large language models and other types of explanations, while ensuring that these explanations are used responsibly.
Summaries
38 word summary
Self-explanations from large language models (LLMs) like ChatGPT are compared to traditional methods for sentiment analysis. Results show similarities in faithfulness but differences in agreement metrics. Self-explanations are cost-effective and challenge interpretability practices, but more research is required.
54 word summary
This study compares self-explanations generated by large language models (LLMs), like ChatGPT, to traditional explanation methods for sentiment analysis. Results show that self-explanations perform similarly to traditional methods in terms of faithfulness, but with differences in agreement metrics. Self-explanations are cheaper to produce and challenge current model interpretability practices, but further research is needed.
117 word summary
This study examines the quality of self-explanations generated by large language models (LLMs), specifically ChatGPT, in the context of sentiment analysis. The researchers compare these self-explanations to traditional explanation methods like occlusion and LIME saliency maps. Experiments using ChatGPT on the Stanford Sentiment Treebank dataset reveal that self-explanations perform similarly to traditional methods in terms of faithfulness, but with differences in agreement metrics. Self-explanations are cheaper to produce as they are generated along with predictions. The study challenges current model interpretability practices and suggests a need for rethinking the interpretability pipeline for LLMs with human-like reasoning abilities. Although self-explanations can be a cost-effective alternative, limitations in current evaluation methods and the need for further research are highlighted.
376 word summary
This study examines the quality of self-explanations generated by large language models (LLMs), specifically ChatGPT, in the context of sentiment analysis. The researchers compare these self-explanations to traditional explanation methods like occlusion and LIME saliency maps.
The experiments focus on sentiment analysis using ChatGPT and explore different ways to elicit self-explanations. The researchers evaluate the faithfulness of these self-explanations using metrics. They find that ChatGPT's self-explanations perform similarly to traditional methods in terms of faithfulness, but there are differences according to agreement metrics. Importantly, self-explanations are cheaper to produce as they are generated along with predictions.
The study identifies interesting characteristics of ChatGPT's self-explanations, challenging current model interpretability practices and suggesting a need for rethinking the interpretability pipeline for LLMs with human-like reasoning abilities. The researchers also provide an overview of related work in interpretability research, including feature attribution explanations, evaluations of these explanations, and LLM-generated self-explanations.
Experiments are conducted using ChatGPT on the Stanford Sentiment Treebank dataset. Self-explanations generated by ChatGPT highlight words with strong sentiment values.
The researchers evaluate the accuracy of ChatGPT models in predicting sentiment and find that models with self-explanation generation have lower accuracy compared to models without explanations. This suggests a trade-off between accuracy and interpretability. They also compare the performance of self-explanations to occlusion and LIME methods using various evaluation metrics. Results show that occlusion performs best on one metric, while LIME and self-explanations perform similarly on other metrics.
Overall, this study provides insights into the quality of self-explanations generated by ChatGPT. The findings suggest that self-explanations can be a cost-effective alternative to traditional explanation methods. However, there are limitations in current evaluation methods and a need for further research to better understand and utilize these self-explanations. The study also highlights the need to reconsider current interpretability practices in the era of LLMs with human-like reasoning abilities.
In conclusion, this study rigorously assesses LLMs' ability to self-generate feature attribution explanations. While no explanation shows a distinct advantage in terms of faithfulness, there is disagreement among explanations according to agreement metrics. The roundedness and insensitivity of the model's prediction values and the level of saliency values in self-explanations are also highlighted. Further research is needed to develop better ways of eliciting self-explanations and rethinking the evaluation practice for LLM-generated explanations.
595 word summary
Large language models (LLMs) like ChatGPT have been successful in various natural language processing tasks, including sentiment analysis. In this study, researchers investigate the quality of self-explanations generated by ChatGPT and compare them to traditional explanation methods like occlusion and LIME saliency maps.
The experiments were conducted using ChatGPT and focused on sentiment analysis. The researchers explored different ways to elicit self-explanations and evaluated their faithfulness using metrics. They found that ChatGPT's self-explanations performed similarly to traditional methods in terms of faithfulness, but there were differences according to agreement metrics. Importantly, self-explanations were cheaper to produce as they were generated along with predictions.
The study identified interesting characteristics of ChatGPT's self-explanations, challenging current model interpretability practices and suggesting a need for rethinking the interpretability pipeline for LLMs with human-like reasoning abilities. The researchers also discussed related work in interpretability research, providing an overview of feature attribution explanations, evaluations of these explanations, and LLM-generated self-explanations.
The researchers used ChatGPT for their experiments and described the prompting strategy employed. They compared self-explanations to occlusion and LIME methods, explaining the evaluation metrics used to assess faithfulness and agreement.
Experiments were conducted on the Stanford Sentiment Treebank dataset, randomly selecting 100 sentences from the test set. The self-explanations generated by ChatGPT highlighted words with strong sentiment values.
The researchers evaluated the accuracy of ChatGPT models in predicting sentiment and found that models with self-explanation generation had lower accuracy compared to models without explanations. This suggests a trade-off between accuracy and interpretability. They also compared the performance of self-explanations to occlusion and LIME methods using various evaluation metrics. Results showed that occlusion performed best on one metric, while LIME and self-explanations performed similarly on other metrics.
Overall, this study provides insights into the quality of self-explanations generated by ChatGPT. The findings suggest that self-explanations can be a cost-effective alternative to traditional explanation methods. However, there are limitations in current evaluation methods and a need for further research to better understand and utilize these self-explanations. The study also highlights the need to reconsider current interpretability practices in the era of LLMs with human-like reasoning abilities.
The study constructs prompts to generate two types of self-explanations using ChatGPT: full feature attribution explanations and top-k explanations. These explanations are compared to occlusion saliency and LIME using faithfulness and agreement metrics.
Results reveal that no explanation shows a distinct advantage in terms of faithfulness, but there is disagreement among the explanations according to agreement metrics. This suggests a need for further research to uncover a significantly better explanation.
The researchers note that ChatGPT's model prediction values and word attribution values are well-rounded, lacking fine-grained variations. This may explain the similarity in faithfulness metric values across explanations and suggests the evaluation metrics used may not be powerful enough to distinguish between good and bad explanations.
Differences between explanation generation and evaluation for ChatGPT compared to other models are also highlighted, such as the level of saliency values in self-explanations. The roundedness and insensitivity of the model's prediction values are also noted, as the removal of a few words does not significantly change predictions.
In conclusion, this study rigorously assesses LLMs' ability to self-generate feature attribution explanations. While no explanation shows a distinct advantage in terms of faithfulness, there is disagreement among explanations according to agreement metrics. The roundedness and insensitivity of the model's prediction values and the level of saliency values in self-explanations are also highlighted. Further research is needed to develop better ways of eliciting self-explanations and rethinking the evaluation practice for LLM-generated explanations.
The researchers suggest future research directions, including evaluating other L
1014 word summary
Large language models (LLMs) like ChatGPT have shown impressive performance on various natural language processing tasks, including sentiment analysis. These models are designed to generate self-explanations along with their responses, providing insights into how they arrive at their predictions. In this study, the researchers investigate the quality of these self-explanations and compare them to traditional explanation methods such as occlusion and LIME saliency maps.
The researchers conducted experiments using OpenAI's ChatGPT model and focused on sentiment analysis as the task. They explored different ways to elicit self-explanations and evaluated their faithfulness using a set of metrics. They also compared the self-explanations to occlusion and LIME methods. The results showed that ChatGPT's self-explanations performed on par with traditional methods in terms of faithfulness, but there were notable differences between them according to various agreement metrics. Importantly, the self-explanations were much cheaper to produce as they were generated along with the predictions.
The study identified several interesting characteristics of the self-explanations generated by ChatGPT. These findings challenge current model interpretability practices and suggest the need for rethinking the interpretability pipeline for LLMs with human-like reasoning abilities. The researchers highlighted the limitations of current evaluation methods in assessing the effectiveness of these explanations and called for further research in this area.
The researchers also discussed related work in the field of interpretability research, focusing on feature attribution explanations, evaluations of feature attribution explanations, and LLM-generated self-explanations. They provided an overview of these areas and highlighted the works directly relevant to their study.
In terms of methodology, the researchers used auto-regressive LLMs, specifically ChatGPT, for their experiments. They described the prompting strategy they employed and explained the two traditional interpretability methods they used for comparison: occlusion and LIME. They also discussed the evaluation metrics they used to assess the faithfulness and agreement of the self-explanations.
The experiments were conducted on the Stanford Sentiment Treebank dataset, which consists of movie reviews with associated sentiment labels. The researchers randomly selected 100 sentences from the test set for their investigations. They visualized the explanations generated by ChatGPT for two example sentences and found that the self-explanations highlighted words with strong sentiment values.
The researchers evaluated the accuracy of the ChatGPT models in predicting sentiment and found that the models with self-explanation generation had lower accuracy compared to the model without any explanation generation. This suggests a trade-off between accuracy and interpretability. They also compared the performance of the self-explanations to occlusion and LIME methods using various evaluation metrics. The results showed that occlusion performed best on one metric, while LIME and self-explanations performed similarly on other metrics.
Overall, the study provides insights into the quality of self-explanations generated by large language models like ChatGPT. The findings suggest that these self-explanations can be a cost-effective alternative to traditional explanation methods. However, there are limitations in current evaluation methods and a need for further research to better understand and use these self-explanations. The study also highlights the need to reconsider current interpretability practices in the era of LLMs with human-like reasoning abilities.
This document presents a study on the use of large language models (LLMs) to generate self-explanations, specifically focusing on the ChatGPT model and its ability to explain its own predictions. The researchers construct prompts that generate two types of self-explanations: full feature attribution explanations that assign importance scores to each word, and top-k explanations that highlight the most important words. These explanations are compared to traditional explanation techniques such as occlusion saliency and LIME (Local Interpretable Model-agnostic Explanations) using faithfulness and agreement metrics.
The results of the study reveal several key findings. First, none of the explanations, whether self-generated or not, show a distinct advantage over the others in terms of faithfulness. However, there is high disagreement among the different explanations according to the agreement metrics. This suggests that there may be an explanation that is significantly better than the current ones, and further research is needed to uncover it.
Another important finding is that both the model prediction values and word attribution values in ChatGPT are highly well-rounded, taking values such as 0.25, 0.67, 0.75, etc. This lack of fine-grained variations in the explanation and prediction values may explain the similarity in faithfulness metric values across different explanations. It also suggests that the evaluation metrics used in this study may not have sufficient power to distinguish between good and bad explanations.
The researchers also note some differences between explanation generation and evaluation for ChatGPT compared to other models. One difference is the level of saliency values in the self-explanations. Instead of arbitrary values, ChatGPT tends to produce well-rounded decimal numbers such as 0.5 and 0.75. This behavior may be attributed to ChatGPT's attempt to mimic human reasoning, as humans typically do not provide very fine-grained saliency values.
The study also highlights the roundedness and insensitivity of the model's prediction values. The removal of a few words often does not significantly change the model's prediction, suggesting that the model may infer the missing words or align itself with human thinking, which tends to ignore minor typographical errors. This behavior has implications for the evaluation metrics, particularly those based on word removal, as the model's prediction does not show significant changes.
In conclusion, this study provides a rigorous assessment of LLMs' capability to self-generate feature attribution explanations. While no explanation shows a distinct advantage over the others in terms of faithfulness, there is high disagreement among the explanations according to the agreement metrics. The study also highlights the roundedness and insensitivity of the model's prediction values and the level of saliency values in the self-explanations. Further research is needed to develop better ways of eliciting self-explanations and to rethink the evaluation practice for LLM-generated explanations.
The researchers suggest future directions for research, including evaluating other LLMs and other types of explanations such as counterfactual explanations and concept-based explanations. They also emphasize the need to ensure that these explanations are beneficial and not used for harmful purposes, such as manipulation or hiding fairness issues. Overall, this study contributes to the understanding of LLM-generated explanations and raises important considerations for their development and evaluation.