INDEX

Explanations

proper nouns, specifically titles of books, movies, and TV shows

oai_token-act-pair · gpt-3.5-turbo Triggered by @bot

New Auto-Interp

Top Features by Cosine Similarity

Comparing With GPT2-SMALL @ 8-res_fs1536-jb

Configuration

jbloom/GPT2-Small-Feature-Splitting-Experiment-Layer-8/blocks.8.hook_resid_pre_1536

Prompts (Dashboard)

24,576 prompts, 128 tokens each

Dataset (Dashboard)

Skylion007/openwebtext

Features

1,536

Data Type

torch.float32

Hook Point

blocks.8.hook_resid_pre

Architecture

standard

Context Size

128

Dataset

Skylion007/openwebtext

Hook Point Layer

Activation Function

relu

Embeds

IFrame

Link

Not in Any Lists

No Comments

Negative Logits

thood

-0.74

 according

-0.70

 stationed

-0.70

perse

-0.69

 partake

-0.69

 directs

-0.68

alone

-0.68

 undergo

-0.68

omever

-0.67

 regardless

-0.67

POSITIVE LOGITS

atre

1.34

oret

1.28

odore

1.27

resa

1.25

orem

1.16

odor

1.07

 Netherlands

1.07

 Hague

1.06

 Economist

1.05

 Simpsons

1.05

Activations Density 1.286%

proper nouns, specifically titles of books, movies, and TV shows

No Comments

No Known Activations