How many tokens to generate in response to a prompt?

[GasimV]

The decision-making process for how many tokens to generate in response to a prompt, especially in models like ChatGPT, involves several key components designed to ensure the responses are coherent, contextually appropriate, and of a sensible length. Here’s how this process generally works:

1. Stopping Criteria

ChatGPT and similar models use specific stopping criteria to determine when to end the generation of tokens. These criteria include:

• Maximum Length: A predefined maximum length limit (number of tokens) is often set based on practical considerations such as computational efficiency and typical user needs. The model will stop generating tokens once this limit is reached.

• End-of-Text Token: GPT models can be trained to recognize an end-of-text token (<|endoftext|> in GPT models). If the model generates this token, it considers the response complete and stops generating further tokens.

• Semantic Completion: The model is trained to predict the probability of each token given the context. If the probabilities of subsequent tokens fall below a certain threshold, suggesting that additional tokens wouldn’t contribute significantly to completing the thought, the model may stop generating.

2. Decoding Strategies

The model uses specific decoding strategies that influence the length and quality of the responses:

• Greedy Decoding: Always chooses the most likely next token. This method is fast but may not always result in the most interesting or varied responses.

• Beam Search: Considers multiple possible next tokens (the "beam width") and keeps track of a set of the most likely sequence combinations. This can lead to more coherent and contextually appropriate responses but may generate longer outputs as it explores more possibilities before concluding.

• Top-k Sampling: Randomly picks from the top k most likely next tokens. This introduces randomness, allowing for more diverse responses.

• Top-p (Nucleus) Sampling: Chooses from a dynamic number of top tokens, ensuring the cumulative probability exceeds a threshold p. This method balances diversity and relevance in the generated text.

3. Contextual and Semantic Awareness

Advanced models like ChatGPT are trained on vast amounts of text data, which enables them to develop a nuanced understanding of language structure and context. This training helps the model learn when a response should naturally end, such as completing a sentence, answering a question, or concluding a paragraph.

4. Interactive Adjustments

In interactive settings, models can be adjusted based on user feedback or specific parameters set by the application using the model. For example, a chat application may set shorter response lengths for quick interactions or longer lengths for detailed explanations.

Example in Practical Implementation

When using ChatGPT or similar models via an API or software library, you often have parameters to control these aspects:

• You can set max_length for the maximum response length.
• You can choose a decoding strategy, like setting num_beams for beam search or top_k and top_p for sampling strategies.

These mechanisms collectively ensure that the model generates responses that are well-formed and appropriate to the given prompt, stopping when a logical endpoint has been reached. Adjusting these parameters allows developers and users to tailor the model’s performance to specific needs or interaction styles.

[GasimV]

How we can adapt the next token prediction task to generate text sequences of arbitrary length. We start with a prompt like “Transformers are the” and use the model to predict the next token. Once we have determined the next token, we append it to the prompt and then use the new input sequence to generate another token. We do this until we have reached a special end-of-sequence token or a predefined maximum length. Since the output sequence is conditioned on the choice of input prompt, this type of text generation is often called conditional text generation.

Decoding Methods

The process of selecting which token to add at each step involves a decoding method. Here’s how it works:

1. Logit Output:

   • At each step, the model produces a logit (a raw prediction score) for every token in the vocabulary.

2. Softmax Function:

   • These logits are transformed into a probability distribution using the softmax function. This equation means that the probability of the next token w_i given the previous tokens y_<t and the model parameters O is obtained by applying the softmax to the logit z_{t, i}.

3. Choosing the Most Likely Sequence:

   • The goal is to find the sequence of tokens that maximizes the overall probability. However, evaluating every possible sequence to find this maximum is computationally infeasible.

Approximation Methods

To overcome the challenge of evaluating every possible sequence, approximation methods are used. These methods try to find a balance between generating high-quality text and computational efficiency.

• Simple Methods: Basic methods like greedy decoding (choosing the highest probability token at each step) are straightforward but might not produce the best results.
• Advanced Methods: More sophisticated methods like beam search, nucleus sampling, and others provide a better balance by considering multiple possible sequences and refining the choice iteratively.