Update dataset card with comprehensive README

README.md

@@ -1,52 +1,17 @@
 ---
 license: apache-2.0
+task_categories:
+  - text-generation
+  - text2text-generation
+language:
+  - en
+tags:
+  - long-context
+  - procedural-generation
+  - benchmark
+  - evaluation
+pretty_name: "LongProc: Long Procedural Generation Benchmark"
 configs:
-- config_name: countdown_0.5k
-  data_files:
-  - split: test
-    path: countdown_0.5k/test-*
-- config_name: countdown_2k
-  data_files:
-  - split: test
-    path: countdown_2k/test-*
-- config_name: countdown_8k
-  data_files:
-  - split: test
-    path: countdown_8k/test-*
-- config_name: default
-  data_files:
-  - split: html_to_tsv_0.5k
-    path: html_to_tsv_0.5k.jsonl
-  - split: html_to_tsv_2k
-    path: html_to_tsv_2k.jsonl
-  - split: html_to_tsv_8k
-    path: html_to_tsv_8k.jsonl
-  - split: pseudo_to_code_0.5k
-    path: pseudo_to_code_0.5k.jsonl
-  - split: pseudo_to_code_2k
-    path: pseudo_to_code_2k.jsonl
-  - split: path_traversal_0.5k
-    path: path_traversal_0.5k.jsonl
-  - split: path_traversal_2k
-    path: path_traversal_2k.jsonl
-  - split: path_traversal_8k
-    path: path_traversal_8k.jsonl
-  - split: tom_tracking_0.5k
-    path: tom_tracking_0.5k.jsonl
-  - split: tom_tracking_2k
-    path: tom_tracking_2k.jsonl
-  - split: tom_tracking_8k
-    path: tom_tracking_8k.jsonl
-  - split: countdown_0.5k
-    path: countdown_0.5k.jsonl
-  - split: countdown_2k
-    path: countdown_2k.jsonl
-  - split: countdown_8k
-    path: countdown_8k.jsonl
-  - split: travel_planning_2k
-    path: travel_planning_2k.jsonl
-  - split: travel_planning_8k
-    path: travel_planning_8k.jsonl
 - config_name: html_to_tsv_0.5k
   data_files:
   - split: test
@@ -59,6 +24,14 @@ configs:
   data_files:
   - split: test
     path: html_to_tsv_8k/test-*
+- config_name: pseudo_to_code_0.5k
+  data_files:
+  - split: test
+    path: pseudo_to_code_0.5k/test-*
+- config_name: pseudo_to_code_2k
+  data_files:
+  - split: test
+    path: pseudo_to_code_2k/test-*
 - config_name: path_traversal_0.5k
   data_files:
   - split: test
@@ -71,14 +44,6 @@ configs:
   data_files:
   - split: test
     path: path_traversal_8k/test-*
-- config_name: pseudo_to_code_0.5k
-  data_files:
-  - split: test
-    path: pseudo_to_code_0.5k/test-*
-- config_name: pseudo_to_code_2k
-  data_files:
-  - split: test
-    path: pseudo_to_code_2k/test-*
 - config_name: tom_tracking_0.5k
   data_files:
   - split: test
@@ -91,6 +56,18 @@ configs:
   data_files:
   - split: test
     path: tom_tracking_8k/test-*
+- config_name: countdown_0.5k
+  data_files:
+  - split: test
+    path: countdown_0.5k/test-*
+- config_name: countdown_2k
+  data_files:
+  - split: test
+    path: countdown_2k/test-*
+- config_name: countdown_8k
+  data_files:
+  - split: test
+    path: countdown_8k/test-*
 - config_name: travel_planning_2k
   data_files:
   - split: test
@@ -103,396 +80,425 @@ configs:
   data_files:
   - split: test
     path: travel_planning_icl_examples/test-*
-dataset_info:
-- config_name: countdown_0.5k
-  features:
-  - name: nums
-    list: int64
-  - name: target
-    dtype: int64
-  - name: solution
-    list: string
-  - name: search_steps
-    dtype: float64
-  - name: demonstration
-    dtype: string
-  - name: solution_text
-    dtype: string
-  - name: num_search_tokens
-    dtype: int64
-  splits:
-  - name: test
-    num_bytes: 274526
-    num_examples: 200
-  download_size: 73905
-  dataset_size: 274526
-- config_name: countdown_2k
-  features:
-  - name: nums
-    list: int64
-  - name: target
-    dtype: int64
-  - name: solution
-    list: string
-  - name: search_steps
-    dtype: float64
-  - name: demonstration
-    dtype: string
-  - name: solution_text
-    dtype: string
-  - name: num_search_tokens
-    dtype: int64
-  splits:
-  - name: test
-    num_bytes: 1025464
-    num_examples: 200
-  download_size: 223580
-  dataset_size: 1025464
-- config_name: countdown_8k
-  features:
-  - name: nums
-    list: int64
-  - name: target
-    dtype: int64
-  - name: solution
-    list: string
-  - name: search_steps
-    dtype: float64
-  - name: demonstration
-    dtype: string
-  - name: solution_text
-    dtype: string
-  - name: num_search_tokens
-    dtype: int64
-  splits:
-  - name: test
-    num_bytes: 3542139
-    num_examples: 200
-  download_size: 679197
-  dataset_size: 3542139
-- config_name: html_to_tsv_0.5k
-  features:
-  - name: task_id
-    dtype: string
-  - name: website_id
-    dtype: string
-  - name: task_topic
-    dtype: string
-  - name: task_description
-    dtype: string
-  - name: gt
-    dtype: string
-  - name: tsv_header
-    dtype: string
-  - name: filtering_instruction
-    dtype: string
-  - name: html_content
-    dtype: string
-  splits:
-  - name: test
-    num_bytes: 5412528
-    num_examples: 100
-  download_size: 1224938
-  dataset_size: 5412528
-- config_name: html_to_tsv_2k
-  features:
-  - name: task_id
-    dtype: string
-  - name: website_id
-    dtype: string
-  - name: task_topic
-    dtype: string
-  - name: task_description
-    dtype: string
-  - name: gt
-    dtype: string
-  - name: tsv_header
-    dtype: string
-  - name: filtering_instruction
-    dtype: string
-  - name: html_content
-    dtype: string
-  splits:
-  - name: test
-    num_bytes: 16085290
-    num_examples: 189
-  download_size: 3569635
-  dataset_size: 16085290
-- config_name: html_to_tsv_8k
-  features:
-  - name: task_id
-    dtype: string
-  - name: website_id
-    dtype: string
-  - name: task_topic
-    dtype: string
-  - name: task_description
-    dtype: string
-  - name: gt
-    dtype: string
-  - name: tsv_header
-    dtype: string
-  - name: filtering_instruction
-    dtype: string
-  - name: html_content
-    dtype: string
-  splits:
-  - name: test
-    num_bytes: 17341279
-    num_examples: 120
-  download_size: 3748019
-  dataset_size: 17341279
-- config_name: path_traversal_0.5k
-  features:
-  - name: context_nl
-    dtype: string
-  - name: question_repr
-    list: string
-  - name: answer_nl
-    dtype: string
-  splits:
-  - name: test
-    num_bytes: 1063102
-    num_examples: 200
-  download_size: 285075
-  dataset_size: 1063102
-- config_name: path_traversal_2k
-  features:
-  - name: context_nl
-    dtype: string
-  - name: question_repr
-    list: string
-  - name: answer_nl
-    dtype: string
-  splits:
-  - name: test
-    num_bytes: 4161616
-    num_examples: 200
-  download_size: 1037013
-  dataset_size: 4161616
-- config_name: path_traversal_8k
-  features:
-  - name: context_nl
-    dtype: string
-  - name: question_repr
-    list: string
-  - name: answer_nl
-    dtype: string
-  splits:
-  - name: test
-    num_bytes: 12489228
-    num_examples: 200
-  download_size: 3220328
-  dataset_size: 12489228
-- config_name: pseudo_to_code_0.5k
-  features:
-  - name: problem_id
-    dtype: string
-  - name: pseudocode_lines
-    list: string
-  - name: code_lines
-    list: string
-  - name: testcases
-    list:
-      list:
-        list: string
-  splits:
-  - name: test
-    num_bytes: 887917
-    num_examples: 199
-  download_size: 324567
-  dataset_size: 887917
-- config_name: pseudo_to_code_2k
-  features:
-  - name: problem_id
-    dtype: string
-  - name: pseudocode_lines
-    list: string
-  - name: code_lines
-    list: string
-  - name: testcases
-    list:
-      list:
-        list: string
-  splits:
-  - name: test
-    num_bytes: 1859944
-    num_examples: 200
-  download_size: 280306
-  dataset_size: 1859944
-- config_name: tom_tracking_0.5k
-  features:
-  - name: story_components
-    dtype: string
-  - name: story
-    dtype: string
-  - name: question
-    dtype: string
-  - name: solution
-    dtype: string
-  - name: answer
-    list: string
-  splits:
-  - name: test
-    num_bytes: 670403
-    num_examples: 200
-  download_size: 139968
-  dataset_size: 670403
-- config_name: tom_tracking_2k
-  features:
-  - name: story_components
-    dtype: string
-  - name: story
-    dtype: string
-  - name: question
-    dtype: string
-  - name: solution
-    dtype: string
-  - name: answer
-    list: string
-  splits:
-  - name: test
-    num_bytes: 2260843
-    num_examples: 200
-  download_size: 314324
-  dataset_size: 2260843
-- config_name: tom_tracking_8k
-  features:
-  - name: story_components
-    dtype: string
-  - name: story
-    dtype: string
-  - name: question
-    dtype: string
-  - name: solution
-    dtype: string
-  - name: answer
-    list: string
-  splits:
-  - name: test
-    num_bytes: 8644352
-    num_examples: 200
-  download_size: 972886
-  dataset_size: 8644352
-- config_name: travel_planning_2k
-  features:
-  - name: id
-    dtype: string
-  - name: ground_truth_cities
-    dtype: string
-  - name: ground_truth_durations
-    dtype: string
-  - name: num_cities
-    dtype: int64
-  - name: total_days
-    dtype: int64
-  - name: constraints
-    list:
-    - name: city
-      dtype: string
-    - name: end_day
-      dtype: int64
-    - name: num_days
-      dtype: int64
-    - name: start_day
-      dtype: int64
-    - name: type
-      dtype: string
-  - name: connected_cities
-    list:
-      list: string
-  - name: original_question_text
-    dtype: string
-  - name: disambig_question_text
-    dtype: string
-  - name: ground_truth_plan
-    dtype: string
-  - name: estimated_output_tokens
-    dtype: int64
-  splits:
-  - name: test
-    num_bytes: 1865273
-    num_examples: 769
-  download_size: 356514
-  dataset_size: 1865273
-- config_name: travel_planning_8k
-  features:
-  - name: id
-    dtype: string
-  - name: ground_truth_cities
-    dtype: string
-  - name: ground_truth_durations
-    dtype: string
-  - name: num_cities
-    dtype: int64
-  - name: total_days
-    dtype: int64
-  - name: constraints
-    list:
-    - name: city
-      dtype: string
-    - name: end_day
-      dtype: int64
-    - name: num_days
-      dtype: int64
-    - name: start_day
-      dtype: int64
-    - name: type
-      dtype: string
-  - name: connected_cities
-    list:
-      list: string
-  - name: original_question_text
-    dtype: string
-  - name: disambig_question_text
-    dtype: string
-  - name: ground_truth_plan
-    dtype: string
-  - name: estimated_output_tokens
-    dtype: int64
-  splits:
-  - name: test
-    num_bytes: 1160141
-    num_examples: 239
-  download_size: 250315
-  dataset_size: 1160141
-- config_name: travel_planning_icl_examples
-  features:
-  - name: id
-    dtype: string
-  - name: ground_truth_cities
-    dtype: string
-  - name: ground_truth_durations
-    dtype: string
-  - name: num_cities
-    dtype: int64
-  - name: total_days
-    dtype: int64
-  - name: constraints
-    list:
-    - name: city
-      dtype: string
-    - name: end_day
-      dtype: int64
-    - name: num_days
-      dtype: int64
-    - name: start_day
-      dtype: int64
-    - name: type
-      dtype: string
-  - name: connected_cities
-    list:
-      list: string
-  - name: original_question_text
-    dtype: string
-  - name: disambig_question_text
-    dtype: string
-  - name: ground_truth_plan
-    dtype: string
-  splits:
-  - name: test
-    num_bytes: 7311
-    num_examples: 4
-  download_size: 15869
-  dataset_size: 7311
 ---
+
+# LongProc: Benchmarking Long-Context Language Models on Long Procedural Generation
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2501.05414"><img src="https://img.shields.io/badge/Paper-arXiv:2501.05414-B31B1B.svg" alt="Paper"></a>
+  <a href="https://github.com/princeton-nlp/LongProc"><img src="https://img.shields.io/badge/GitHub-Code-black?logo=github" alt="GitHub"></a>
+  <a href="https://princeton-pli.github.io/LongProc/"><img src="https://img.shields.io/badge/Project-Page-blue" alt="Project Page"></a>
+</p>
+
+**LongProc** (**Long Proc**edural Generation) is a benchmark for evaluating long-context LLMs through long procedural generation tasks that require models to follow specified procedures and produce structured outputs. LongProc was accepted at [COLM 2025](https://colmweb.org/).
+
+<p align="center">
+  <img width="60%" alt="LongProc task examples" src="https://princeton-pli.github.io/LongProc/static/images/data_example.png">
+</p>
+
+## Dataset Overview
+
+LongProc consists of **6 tasks**, each at up to 3 difficulty levels based on the expected output length (~0.5K, ~2K, ~8K tokens). The dataset is organized into **17 subsets** (configs), each containing a single `test` split.
+
+| Task | Description | Configs | Total Examples |
+|------|-------------|---------|---------------|
+| **HTML to TSV** | Extract information from HTML pages into structured TSV tables | `html_to_tsv_0.5k`, `html_to_tsv_2k`, `html_to_tsv_8k` | 100 + 189 + 120 = 409 |
+| **Pseudocode to Code** | Translate line-by-line pseudocode into C++ code | `pseudo_to_code_0.5k`, `pseudo_to_code_2k` | 199 + 200 = 399 |
+| **Path Traversal** | Trace a route between cities in a directed graph where each city has one outgoing edge | `path_traversal_0.5k`, `path_traversal_2k`, `path_traversal_8k` | 200 + 200 + 200 = 600 |
+| **Theory-of-Mind Tracking** | Track object locations and agent beliefs through multi-step stories | `tom_tracking_0.5k`, `tom_tracking_2k`, `tom_tracking_8k` | 200 + 200 + 200 = 600 |
+| **Countdown** | Search to combine numbers with arithmetic operations to reach a target | `countdown_0.5k`, `countdown_2k`, `countdown_8k` | 200 + 200 + 200 = 600 |
+| **Travel Planning** | Search to construct a trip plan satisfying duration and flight constraints | `travel_planning_2k`, `travel_planning_8k`, `travel_planning_icl_examples` | 769 + 239 + 4 = 1012 |
+
+## Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load a specific task + difficulty
+ds = load_dataset("PrincetonPli/LongProc", "countdown_0.5k", split="test")
+print(ds)
+# Dataset({
+#     features: ['nums', 'target', 'solution', 'search_steps', 'demonstration', 'solution_text', 'num_search_tokens'],
+#     num_rows: 200
+# })
+
+# Load another config
+ds = load_dataset("PrincetonPli/LongProc", "html_to_tsv_2k", split="test")
+print(ds[0].keys())
+# dict_keys(['task_id', 'website_id', 'task_topic', 'task_description', 'gt', 'tsv_header', 'filtering_instruction', 'html_content'])
+```
+
+## Data Fields
+
+### html_to_tsv
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `task_id` | string | Unique identifier for the task instance |
+| `website_id` | string | Identifier for the source website |
+| `task_topic` | string | Topic of the webpage (e.g., "electronics", "books") |
+| `task_description` | string | Description of what properties to extract |
+| `gt` | string | Ground truth TSV output |
+| `tsv_header` | string | Header row for the TSV output |
+| `filtering_instruction` | string | Additional instructions for filtering rows |
+| `html_content` | string | Full HTML content of the webpage (inlined) |
+
+### pseudo_to_code
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `problem_id` | string | Unique problem identifier |
+| `pseudocode_lines` | list[string] | Pseudocode description, line by line |
+| `code_lines` | list[string] | Ground truth C++ code lines |
+| `testcases` | list | Test cases for validation |
+
+### path_traversal
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `context_nl` | string | Natural language description of the city graph |
+| `question_repr` | list[string] | Source and destination cities |
+| `answer_nl` | string | Ground truth route in natural language |
+
+### tom_tracking
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `story_components` | string | Components of the story (agents, objects, rooms, containers) |
+| `story` | string | The multi-step story |
+| `question` | string | Question about an agent's belief about an object's location |
+| `solution` | string | Step-by-step solution trace |
+| `answer` | list[string] | Final answer(s) |
+
+### countdown
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `nums` | list[int] | Four input numbers |
+| `target` | int | Target number to reach |
+| `solution` | list[string] | Sequence of equations forming the solution |
+| `search_steps` | float | Number of search steps in the ground truth trace |
+| `demonstration` | string | In-context demonstration of the search procedure |
+| `solution_text` | string | Full solution text including the search procedure |
+| `num_search_tokens` | int | Number of tokens in the search procedure |
+
+### travel_planning
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | string | Unique problem identifier |
+| `ground_truth_cities` | string | Ordered list of cities in the ground truth plan |
+| `ground_truth_durations` | string | Duration of stay for each city |
+| `num_cities` | int | Number of cities to visit |
+| `total_days` | int | Total number of trip days |
+| `constraints` | list[object] | Constraints with city, type, start/end days, num_days |
+| `connected_cities` | list[list[string]] | Direct flight connections between cities |
+| `original_question_text` | string | Original problem statement |
+| `disambig_question_text` | string | Disambiguated problem statement |
+| `ground_truth_plan` | string | Complete ground truth trip plan |
+| `estimated_output_tokens` | int | Estimated output length in tokens (not present in ICL examples) |
+
+The `travel_planning_icl_examples` config contains 4 in-context learning examples that share the same schema but without `estimated_output_tokens`.
+
+## Prompt Templates
+
+The prompt templates below are used to construct the input prompts for each task. Placeholders in `{braces}` are filled from the corresponding data fields.
+
+<details>
+<summary><b>HTML to TSV</b></summary>
+
+```
+[TASK]
+Your task is to extract specific information from an HTML webpage and output the extracted
+information in a tsv file. You will be first given an HTML webpage. Then, you should follow
+the specific instruction provided later and output the tsv file following the format provided
+in the instruction.
+
+[INPUT WEBPAGE]
+```html
+{html_content}
+```
+
+[TARGET INFORMATION]
+Based on the HTML webpage above about {task_topic}, extract the following properties from
+the items listed on the webpage: {task_description}{filtering_instruction}
+
+[OUTPUT FORMAT]
+Structure your output in TSV format such that each row of your output corresponds to the
+aforementioned properties of an item and each property is separated from each other by a
+tab "\t". Your output should be in the following format:
+```tsv
+{tsv_header}
+{Your TSV output}
+```
+
+[IMPORTANT NOTES]
+- Make sure that you have read through all items listed on the webpage and followed the
+  same order as they appear on the webpage.
+- If you are asked to only extract some rows that satisfy specific conditions, ONLY extract
+  those rows that satisfy the conditions and do NOT include other irrelevant rows in your output.
+- If a property of an item is blank, not applicable, or not parseable, please set the property
+  to "N/A" for the item.
+- If a property spans multiple lines, please extract all the lines and replace the newline
+  character with a space character.
+- If a property consists of a list of items, please replace the newline character with a space
+  character and separate the items with a comma ",".
+- If there are any special characters, numerical values of a specific format, or any unusual
+  formatting in the property, please keep them as they are. If the property comes with a unit,
+  please keep the unit as well in the property.
+- Do not include html tags in the extracted information. Only include the text.
+- Do not provide any additional information in your output other than the tsv.
+
+Now, extract the information from the HTML webpage above and follow the output format above
+in your answer.
+```
+</details>
+
+<details>
+<summary><b>Pseudocode to Code</b></summary>
+
+```
+[TASK]:
+You will be given lines of pseudocode, your task is to write the corresponding C++ code.
+The pseudocode will provide detailed description of the c++ code line by line. The pseudocode
+is garanteed to be correct and complete.
+
+[INSTRUCTION]:
+The following libraries are already included in the code.
+```cpp
+#include <cstdio>
+#include <iostream>
+#include <vector>
+#include <algorithm>
+#include <numeric>
+#include <cmath>
+#include <cstring>
+#include <set>
+#include <map>
+#include <queue>
+#include <stack>
+#include <list>
+#include <fstream>
+#include <climits>
+#include <cassert>
+#include <iomanip>
+#include <sstream>
+#include <bitset>
+using namespace std;
+```
+Do not include them in your code again. Please surround your code with ```cpp and ``` markers.
+Note that the code should correspond to the pseudocode line by line.
+
+[PSEUDOCODE]:
+{pseudocode}
+
+[CODE]:
+```
+
+Where `{pseudocode}` is constructed by joining `pseudocode_lines` with newlines.
+</details>
+
+<details>
+<summary><b>Path Traversal</b></summary>
+
+```
+[TASK]
+In a completely hypothetical world, there are a number of cities. Each city has a one-way
+connection to only one other city via a specific transit method (bus, train, plane, or ferry).
+Your task is to provide a route from a city to another city. You should follow the specific
+instruction provided later and output the route following the format provided in the instruction.
+
+[IMPORTANT NOTES]
+- All connections are one-way. If city A is connected to city B, you can travel from A to B,
+  but not the other way around.
+- Because each city is connected to only one other city, so there's only one possible route.
+  To find the route, you can simply start from the starting city, identify the next city it's
+  connected to, and repeat the process until you reach the destination city.
+- Please follow the exact format specified below when outputting the route.
+
+[OUTPUT FORMAT]
+Please mark the route with <Route> and </Route> tags. The route should be in the following
+format, where one line is one step of the route:
+<Route>
+From <CITY_NAME>, take a <TRANSIT_METHOD> to <CITY_NAME>.
+...
+From <CITY_NAME>, take a <TRANSIT_METHOD> to <CITY_NAME>.
+</Route>
+
+[EXAMPLE]
+...
+
+[PROBLEM]
+{context_nl}
+
+Now find the route from {src_city} to {dst_city} based on the information above.
+```
+
+Where `{src_city}` and `{dst_city}` come from the `question_repr` field.
+</details>
+
+<details>
+<summary><b>Theory-of-Mind Tracking</b></summary>
+
+```
+[TASK]
+You'll see a story about object placement. Each story involves four components: Agents,
+Objects, Rooms, and Containers. Given a question about an (agent, object) pair, your task
+is to track the locations and beliefs in stories about object placement asked in the question.
+
+[APPROACH]
+You will solve the problem by tracking the location of the agent, location of the object,
+and the agent's belief of the object.
+1. Initial Setup: set up agent's starting location, object's starting location, agent's
+   initial belief on the object's location. Note that if an agent does not see an object
+   at the start, their belief on the object is None.
+2. Then, track step-by-step:
+   - If a step involves that the agent moves to another room, leaves a room, or enters a
+     room, you should update the agent's location.
+   - If a step involves the object of interest moving, you should update the object's location.
+   - To keep track of the agent's belief on the object: If the agent and the object are in
+     the same room, the agent can see the object, so the agent's belief will reflect the
+     true location of the object. If the agent cannot see the object, the agent's belief
+     will remain unchanged until the agent sees the object again.
+3. Format your output exactly as shown in example answers below.
+
+[EXAMPLE STORY / QUESTION / ANSWER]
+...
+
+[PROBLEM]
+Read the following story and answer the question.
+
+[STORY]
+{story}
+
+[QUESTION]
+{question}
+
+[YOUR ANSWER]
+```
+</details>
+
+<details>
+<summary><b>Countdown</b></summary>
+
+```
+[TASK]
+You will be given four numbers and a target number, your task is to find a way to use all
+four numbers exactly once, along with the basic operations (+, -, *, /), to reach the
+target number.
+
+[RULES]
+- You can use each number exactly once.
+- You can use the four basic operations (+, -, *, /).
+- The intermediate results must be integers (no decimals allowed).
+- The intermediate results must be positive.
+- The intermediate results will not exceed 2000.
+
+[APPROACH]
+We will solve the problem by searching. Starting from a given set of four numbers, we will
+follow this search process...
+
+[EXAMPLES]
+{demonstration}
+
+[Problem]
+Numbers: {nums}
+Target: {target}
+```
+
+The `{demonstration}` field is provided per-example and contains worked examples of the search procedure. `{nums}` comes from joining the `nums` list.
+</details>
+
+<details>
+<summary><b>Travel Planning</b></summary>
+
+```
+TASK:
+Your task is to create a trip plan based on given constraints regarding cities to visit,
+duration of stays for each city, and available direct flight connections.
+
+REQUIREMENTS AND NOTES:
+- You will arrange a trip plan for visiting several cities for a specified total number of days.
+- You will be informed about how long we will stay in each city. Some cities have fixed
+  schedules because of pre-planned events. You have to follow the fixed schedules for those
+  cities. Cities without fixed schedules need to be arranged according to the constraints.
+- Only direct flights may be used to travel between cities.
+- When calculating the duration of a stay in a city, count both arrival and departure days
+  as full days.
+
+APPROACH:
+We will solve the problem by searching...
+
+EXAMPLES:
+{demonstration}
+
+YOUR TASK:
+{problem}
+```
+
+The `{demonstration}` is constructed from the `travel_planning_icl_examples` config. `{problem}` comes from `disambig_question_text` (or `original_question_text`).
+</details>
+
+## Running Evaluation
+
+We recommend using the [LongProc GitHub repository](https://github.com/princeton-nlp/LongProc) for data loading and evaluation:
+
+```python
+from longproc.longproc_data import load_longproc_data
+
+data, eval_fn = load_longproc_data("countdown_0.5k")
+# data: list of dicts with 'input_prompt', 'reference_output', 'item'
+# eval_fn: task-specific evaluation function
+```
+
+For large-scale evaluation, we recommend the [HELMET](https://github.com/princeton-nlp/HELMET) framework with the [LongProc Addon](https://github.com/princeton-nlp/HELMET/blob/longproc/longproc_addon/README.md).
+
+## Citation
+
+```bibtex
+@inproceedings{ye25longproc,
+    title={LongProc: Benchmarking Long-Context Language Models on Long Procedural Generation},
+    author={Ye, Xi and Yin, Fangcong and He, Yinghui and Zhang, Joie and Yen, Howard and Gao, Tianyu and Durrett, Greg and Chen, Danqi},
+    journal={Conference on Language Modeling},
+    year={2025}
+}
+```
+
+<details>
+<summary><b>LongProc adapts several existing datasets. Please also cite the original sources:</b></summary>
+
+```bibtex
+@article{arborist,
+    author = {Li, Xiang and Zhou, Xiangyu and Dong, Rui and Zhang, Yihong and Wang, Xinyu},
+    title = {Efficient Bottom-Up Synthesis for Programs with Local Variables},
+    year = {2024},
+    journal = {Proc. ACM Program. Lang.},
+    volume = {8},
+    number = {POPL},
+}
+
+@inproceedings{spoc,
+    author = {Kulal, Sumith and Pasupat, Panupong and Chandra, Kartik and Lee, Mina and Padon, Oded and Aiken, Alex and Liang, Percy S},
+    booktitle = {Proceedings of the Conference on Advances in Neural Information Processing Systems (NeurIPS)},
+    title = {{SPoC: Search-based Pseudocode to Code}},
+}
+
+@inproceedings{gandhi2024stream,
+    title={{Stream of Search (SoS): Learning to Search in Language}},
+    author={Kanishk Gandhi and Denise H J Lee and Gabriel Grand and Muxin Liu and Winson Cheng and Archit Sharma and Noah Goodman},
+    booktitle={First Conference on Language Modeling},
+    year={2024},
+}
+
+@article{natplan,
+    title={{NATURAL PLAN: Benchmarking LLMs on Natural Language Planning}},
+    author={Zheng, Huaixiu Steven and Mishra, Swaroop and Zhang, Hugh and Chen, Xinyun and Chen, Minmin and Nova, Azade and Hou, Le and Cheng, Heng-Tze and Le, Quoc V and Chi, Ed H and others},
+    journal={arXiv preprint arXiv:2406.04520},
+    year={2024}
+}
+```
+</details>
+
+## Contact
+
+For questions, feel free to open issues on the [GitHub repository](https://github.com/princeton-nlp/LongProc) or email `xi.ye@princeton.edu`.