In the first lab, there are two options for choosing configGeneration for T5 transformer.
In both of the configs, model is able to generate answers within the specified word limit and still the answer makes sense, how does the model takes care of this i.e., how model ensures word limit threshold and still comes up with reasonable prompt ?
Because T5 is pre-trained on large datasets (including short summaries and sentences), the model’s extensive knowledge allows it to condense information, understand prompts, and generate meaningful responses effectively even when constrained by fewer tokens, such as max_new_tokens=10. T5 uses tokenization to break words into sub-word units, efficiently compressing information into fewer tokens and conveying the core message or meaning of the prompt. Additionally, the model generates text by predicting one token at a time, choosing the most probable token at each step based on the context of the input and previously generated tokens, and prioritizing more meaningful tokens within the limited number of tokens to produce coherent output. Finally, the attention mechanism helps the model focus on the most relevant parts of the input context, which allows the model to produce concise and coherent responses that respect the token limit while still addressing the prompt meaningfully.
By experimenting with different settings, like max_new_tokens, do_sample, and temperature, you’ll see how they affect the model’s ability to generate summaries or answers. For example, when max_new_tokens=10, the model is constrained to a shorter output, which may truncate a summary, whereas increasing the temperature with do_sample=True introduces more diversity in the responses.
Hi @nadtriana , thanks for helping but as I assume the model predict function might be something like:
predict while eos token generated or max_token_reached:
where the second condition seems to be a brutal cut-off then how model is able to summarise it short, as in decoder concept I don’t see a parameter that takes in max_token count as input to influence prompt generated.
The model can generate meaningful responses within a token limit (without an explicit prompt indicating the maximum token count) due to various underlying factors in its design and training. During the generation process, the model selects tokens based on a probability distribution of what comes next. When constrained by max_new_tokens, it will typically generate the most relevant information first, making the output meaningful within the limit. Although the token limit acts as a strict cut-off during generation, the model’s training and design of its probabilistic token selection ensure that it generates the most meaningful and concise output it can, even without being told to summarize within a token count during decoding.
Decoding strategies like top-k sampling, top-p (nucleus) sampling, and beam search influence the generated output’s structure. These strategies affect how diverse or focused the next token prediction is, which in turn helps the model generate more concise summaries when needed. For example:
do_sample=True and temperature are adjusted, the model’s randomness in token selection changes, and it often generates more focused and meaningful summaries, even within a constrained token limit.