Whether a vector represents a single token or an entire sentence depends on the specific application and the type of embedding being used.
Token-level embeddings represent individual tokens, such as words, subwords or punctuation marks. These embeddings are typically used for tasks like part-of-speech tagging and named entity recognition.
Sentence-level embeddings represent an entire sentence as a single vector. These embeddings are typically used for tasks like sentiment analysis and text classification.
In some cases, it is possible to use both token-level and sentence-level embeddings in the same application. For example, a machine translation system might use token-level embeddings to translate individual words and sentence-level embeddings to ensure that the translation is grammatically correct and makes sense as a whole.
The choice of whether to use token-level or sentence-level embeddings depends on the specific task at hand. If the task requires understanding the relationships between tokens, then token-level embeddings are a good choice. If the task requires understanding the meaning of the sentence as a whole, then sentence-level embeddings are a better choice.
Here is a table summarizing the key differences between token-level embeddings and sentence-level embeddings:
| Feature | Token-Level Embeddings | Sentence-Level Embeddings |
| Granularity | Individual tokens | Entire sentence |
| Strength | Fine-grained information, flexibility, explainability | Compact representation, captures global context, less sensitive to noise |
| Weakness | High dimensionality, loss of global context, sensitive to noise | Loss of fine-grained information, less flexible, difficult to interpret |
| Suitable for tasks | Sentiment analysis, named entity recognition, question answering, topic modeling | Sentiment analysis, topic modeling |
| Computational cost | High | Low |
| Example method | Word2Vec, GloVe | SentenceBERT, Universal Sentence Encoder (USE) |
It is important to note that there is no one-size-fits-all answer to the question of whether a vector represents a single token or an entire sentence. The best choice will depend on the specific task at hand and the type of data being used.
GPT4, despite operating primarily at the token level, is still capable of comparing sentences or understanding the relationship between them. This capability stems from its underlying transformer architecture, which enables the model to understand context and relationships in text.
Try out this prompt on your own (on ChatGPT) and you will see how skillfully it answers:
Compare these two sentences and tell me if they mean the same:
1: I ran fast so I can get home by bus
2: I ran fast to catch the bus to get home
Response from ChatGPT using GPT4:
The two sentences convey similar ideas but have slight differences in their meaning and emphasis:
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- “I ran fast so I can get home by bus”: This sentence suggests that the primary goal is getting home, and the method chosen is by bus. The running fast part is presented as a means to ensure that the speaker can catch the bus to get home. The focus is more on the desire to get home, with catching the bus being a necessary step in the process.
- “I ran fast to catch the bus to get home”: This sentence places more emphasis on the action of catching the bus. The running fast is directly linked to the act of catching the bus, which is then linked to the goal of getting home. Here, the catching of the bus is given more prominence as a separate goal, which then leads to getting home.
In summary, both sentences describe a scenario where someone runs quickly to catch a bus for the purpose of getting home. However, the first sentence emphasizes the ultimate goal of getting home, using the bus as a means, while the second sentence gives more weight to the action of catching the bus as a step towards getting home.
See Also: Vector Space, Word level embedding, Word2vec