# Selected Probes

Each probe is a CSV with `prompt`, `prompt_len`, and `target` columns. All targets are 0/1 integers unless noted. All datasets are balanced (50/50) unless noted.

---

## 5 — `hist_fig_ismale`

**Entries:** 5,000 | **Avg prompt length:** 20 chars | **Max:** 70 chars

**Prompts:** Historical figure names (e.g. "Margaret of Clisson", "Billy Mays").

**Target:** 1 = male, 0 = female — 50% / 50%

---

## 6 — `hist_fig_isamerican`

**Entries:** 5,000 | **Avg prompt length:** 17 chars | **Max:** 65 chars

**Prompts:** Historical figure names (same pool as above).

**Target:** 1 = American, 0 = non-American — 50% / 50%

---

## 22 — `headline_isobama`

**Entries:** 1,656 | **Avg prompt length:** 57 chars | **Max:** 108 chars

**Prompts:** News headlines (e.g. "Obama Sticks to a Deadline in Iraq .").

**Target:** 1 = headline is about Obama, 0 = not about Obama — 50% / 50%

*Note: target column was originally True/False; converted to 1/0.*

---

## 36 — `sciq_tf`

**Entries:** 5,000 | **Avg prompt length:** 94 chars | **Max:** 427 chars

**Prompts:** Science question + answer pairs formatted as `Q: ...\nA: ...`.

**Target:** 1 = answer is correct, 0 = answer is incorrect — 50% / 50%

---

## 49 — `cm_isshort`

**Entries:** 5,000 | **Avg prompt length:** 981 chars | **Max:** 9,851 chars

**Prompts:** Reddit-style posts and stories (long-form text, variable topics).

**Target:** 1 = short text, 0 = long text — 50% / 50%

*Note: target column was originally True/False; converted to 1/0.*

---

## 51 — `just_is`

**Entries:** 5,000 | **Avg prompt length:** 103 chars | **Max:** 327 chars

**Prompts:** Short descriptions of a person's action or decision, written in first or third person.

**Target:** 1 = action is justified, 0 = not justified — 50% / 50%

---

## 65 — `high-school`

**Entries:** 3,200 | **Avg prompt length:** 108 chars | **Max:** 786 chars

**Prompts:** Miscellaneous text excerpts, some of which contain a "high school" bigram near the end.

**Target:** 1 = ends with "high school" bigram, 0 = does not — 50% / 50%

---

## 92 — `glue_sst2`

**Entries:** 5,000 | **Avg prompt length:** 53 chars | **Max:** 256 chars

**Prompts:** Movie review excerpts (from the SST-2 benchmark).

**Target:** 1 = positive sentiment, 0 = negative sentiment — 50% / 50%

---

## 96 — `spam_is`

**Entries:** 1,494 | **Avg prompt length:** 108 chars | **Max:** 791 chars

**Prompts:** SMS/text messages of varying content and length.

**Target:** 1 = spam, 0 = not spam — 50% / 50%

---

## 122 — `us_timezone_Los_Angeles`

**Entries:** 3,332 | **Avg prompt length:** 13 chars | **Max:** 89 chars

**Prompts:** US location names (cities, towns, neighborhoods).

**Target:** 1 = Pacific timezone (Los Angeles), 0 = other timezone — 50% / 50%

---

## 129 — `arith_mc_A`

**Entries:** 1,494 | **Avg prompt length:** 53 chars | **Max:** 67 chars

**Prompts:** Arithmetic questions with four multiple-choice answers, formatted as:
```
Calculate 63 - 5 =
A.57 B.59 C.68 D.58
Answer: A
```
`"Answer: A"` appended to each prompt to steer the model toward option A.

**Target:** 1 = correct answer is A, 0 = correct answer is not A — 50% / 50%

---

## 139 — `news_class_Politics`

**Entries:** 2,500 | **Avg prompt length:** 237 chars | **Max:** 745 chars

**Prompts:** News article headlines + body snippets from various sections.

**Target:** 1 = politics section, 0 = not politics — 50% / 50%

---

## 145 — `disease_class_digestive system diseases`

**Entries:** 1,052 | **Avg prompt length:** 1,236 chars | **Max:** 2,939 chars

**Prompts:** Abstracts from medical/science articles about diseases.

**Target:** 1 = digestive system disease, 0 = other disease category — 50% / 50%

---

## 150 — `twt_emotion_sadness`

**Entries:** 2,500 | **Avg prompt length:** 76 chars | **Max:** 152 chars

**Prompts:** Tweets of varying emotional tone.

**Target:** 1 = tweet expresses sadness, 0 = other emotion — 50% / 50%

---

## 159 — `code_Python`

**Entries:** 3,672 | **Avg prompt length:** ~3,500 chars (median) | **Max:** 24,999 chars

**Prompts:** Code snippets of varying length and language. Filtered to <25k chars (dropped 328 rows that were large auto-generated files, C headers, etc.); original max was 853k chars and outliers skewed slightly non-Python.

**Target:** 1 = Python code, 0 = other language — 50.5% / 49.5%