ibm-granite/granite-3.3-8b-instruct 🔢📝❓✓ → 📝

2025-04-15 17:15:15 [info     ] predict() commencing           request_id=c23a193892654772b688b7fd97f894b8 user_prompt=How is perplexity measured for LLMs and why is it useful?
2025-04-15 17:15:15 [debug    ] Formatted prompt using chat template formatted_prompt=<|start_of_role|>system<|end_of_role|> Knowledge Cutoff Date: April 2024.
 Today's Date: April 15, 2025. You are Granite, developed by IBM. You are a helpful AI assistant.<|end_of_text|>
<|start_of_role|>user<|end_of_role|>How is perplexity measured for LLMs and why is it useful?<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|> request_id=c23a193892654772b688b7fd97f894b8 user_prompt=How is perplexity measured for LLMs and why is it useful?
2025-04-15 17:15:15 [debug    ] SamplingParams                 request_id=c23a193892654772b688b7fd97f894b8 sampling_params=SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.6, top_p=0.9, top_k=50, min_p=0.0, seed=22349, stop=[], stop_token_ids=[0], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=512, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None) user_prompt=How is perplexity measured for LLMs and why is it useful?
INFO 04-15 17:15:15 [async_llm.py:228] Added request c23a193892654772b688b7fd97f894b8.
2025-04-15 17:15:19 [debug    ] result                         finish_reason=length request_id=c23a193892654772b688b7fd97f894b8 text=Perplexity is a common metric used to evaluate the performance of language models, including large language models (LLMs). It measures how well a language model predicts a sample. A lower perplexity score indicates better performance, as it suggests the model is more confident in its predictions.

Perplexity is calculated by raising 2 to the average negative log-likelihood of the test set. In simpler terms, it quantifies the uncertainty of the model in predicting the next word in a sequence. A perplexity of 2^n means that, on average, the model is uncertain about the next word, assigning a probability of 1/n to the correct word.

Here's a step-by-step breakdown of the calculation:

1. For each word in the test set, the language model computes a probability distribution over the vocabulary, predicting the likelihood of the next word.
2. The negative logarithm (base 2) of these probabilities is calculated for each word.
3. The negative log-likelihood is averaged across all words in the test set.
4. 2 raised to the power of this average negative log-likelihood gives the perplexity score.

Perplexity is useful for several reasons:

1. **Model comparison**: It allows for comparing different language models or model configurations. A model with lower perplexity on a given test set is generally considered to have better performance.

2. **Training optimization**: Perplexity can be used as a loss function during training, guiding the model to minimize uncertainty in its predictions. Lower perplexity during training indicates that the model is learning to make more accurate predictions.

3. **Transfer learning and fine-tuning**: Perplexity can help assess the impact of transfer learning or fine-tuning on pre-trained language models. A decrease in perplexity after applying these techniques suggests improved performance.

4. **Understanding model behavior**: Perplexity can offer insights into how well the model captures the underlying patterns and structures of the language.

However, it's essential to consider the limitations of perplexity. It doesn't always correlate with human-evaluated quality, especially for specific tasks or domains. Moreover, it tends to favor models that assign very low probabilities to a small subset of words, which might not necessarily indicate better performance in practical applications.

In summary, perplexity is a valuable user_prompt=How is perplexity measured for LLMs and why is it useful?
2025-04-15 17:15:19 [info     ] Generation took 3.98s          request_id=c23a193892654772b688b7fd97f894b8 user_prompt=How is perplexity measured for LLMs and why is it useful?
/root/.pyenv/versions/3.11.12/lib/python3.11/site-packages/cog/server/scope.py:21: ExperimentalFeatureWarning: current_scope is an experimental internal function. It may change or be removed without warning.
  warnings.warn(
2025-04-15 17:15:19 [info     ] predict() complete             request_id=c23a193892654772b688b7fd97f894b8 user_prompt=How is perplexity measured for LLMs and why is it useful?