text | token_short | token_long | p_short | p_long | JS |
---|---|---|---|---|---|

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# A catalog of several million tasks Pythia can do.

We’re sharing datasets that we hope will be useful for language model interpretability.

**Token-bigram and token-trigram prediction**: a dataset of n-gram statistics from The Pile (Gao et al. 2020) including tables of one and two token prompts with their most likely completions. One of the simplest “tasks” for a language model is bigram completion.- for example, during training, 99.8% of the time the model sees
`" telome"`

, the correct next token is`"res"`

.

- for example, during training, 99.8% of the time the model sees
**First token deletion**: a dataset constructed by differencing the outputs of Pythia-2.8B (Biderman et al. 2023) between four and five token prompts. This method highlights tokens that are extremely predictive in context.- for example, when prompted with
`", or common table"`

, the model predicts`" expression"`

(CTE) with probability 0.37. But, if we prompt with`" chloride, or common table"`

, then the model predicts`" salt"`

with probability 0.99.

- for example, when prompted with

## The data

In following sections we will give details on the construction and statistics of these datasets. Before continuing, we share some interactive data previews:

**Deletion**: the first 25000 rows of pile_scan_4.**Bigrams**: the entirety of pile_top_bigrams, which contains bigrams with suffix probability greater than 50%.**Trigrams**: the first 25000 rows of pile_top_trigrams, which contains trigrams with suffix probability greater than 50% and count greater than 1000.

The columns of the table below:

`text`

: the two prompts provided. The additional token of backwards context is surrounded by square brackets. The example in the introduction would be written`"[_chloride],_or_common_table"`

.`token_short`

: the most likely next token predicted by Pythia-2.8B for the*four*token prompt.`token_long`

: the most likely next token predicted by Pythia-2.8B for the*five*token prompt.`p_short`

: the probability Pythia-2.8B assigns to`token_short`

.`p_long`

: the probability Pythia-2.8B assigns to`token_long`

.`JS`

: the Jensen-Shannon divergence between the model’s output distributions for the four and five token prompts.

Note:

- in the table, spaces are replaced with underscores for clarity.
- there are offensive tokens in the dataset. We have not removed them.