Saturday 08 March 2025
The relationship between language and thought has long been a topic of debate among scholars and linguists. Some argue that language shapes our thoughts, while others believe that our thoughts shape our language. A recent paper attempts to shed light on this age-old question by examining the role of chain-of-thought (CoT) prompting in sentiment analysis.
The researchers designed an experiment where they trained a large language model to perform sentiment analysis tasks using CoT prompts. These prompts are essentially a series of sentences that guide the model through a specific thought process, allowing it to reason and make connections between different pieces of information.
In this case, the team used CoT prompts to analyze customer reviews of laptops. They created three different types of prompts: CoT-v1, which simply stated the sentiment polarity of each aspect being reviewed; CoT-v2, which listed the aspects in sequence with their corresponding sentiment polarities; and CoT-v3, which used arrow symbols to indicate the direction of the sentiment shift.
The results were fascinating. The researchers found that while CoT prompting did improve the model’s ability to perform sentiment analysis, it was not as significant as they had expected. In fact, the differences between the three types of prompts were relatively small, suggesting that the language model was able to adapt and learn from each type of prompt.
The team also experimented with perturbing the input text by shuffling the words or reversing the sentiment polarity of the aspects being reviewed. These manipulations had a significant impact on the model’s performance, indicating that it was relying heavily on the specific wording and structure of the prompts rather than the underlying thought process.
This finding has important implications for our understanding of language and thought. It suggests that while language can influence our thoughts and perceptions, it is not as dominant a force as some might believe. Instead, our brains are capable of adapting to different linguistic structures and navigating complex thought processes on their own.
The paper also highlights the importance of considering the role of demonstration information in sentiment analysis. The researchers found that when they modified the demonstrations by reversing the sentiment polarity of the aspects being reviewed, the model’s performance suffered significantly. This suggests that the language model was relying heavily on the specific examples provided and not necessarily understanding the underlying thought process.
Overall, this study provides valuable insights into the relationship between language and thought. While CoT prompting did improve the model’s ability to perform sentiment analysis, it was not as significant as expected.
Cite this article: “Languages Influence on Thought: A Study of Chain-of-Thought Prompts in Sentiment Analysis”, The Science Archive, 2025.
Language, Thought, Chain-Of-Thought, Prompting, Sentiment Analysis, Customer Reviews, Laptops, Language Model, Perturbing, Demonstrations
