| --- |
| license: apache-2.0 |
| task_categories: |
| - text-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: html_to_tsv_0.5k |
| data_files: |
| - split: test |
| path: html_to_tsv_0.5k/test-* |
| - config_name: html_to_tsv_2k |
| data_files: |
| - split: test |
| path: html_to_tsv_2k/test-* |
| - config_name: html_to_tsv_8k |
| data_files: |
| - split: test |
| path: html_to_tsv_8k/test-* |
| - config_name: path_traversal_0.5k |
| data_files: |
| - split: test |
| path: path_traversal_0.5k/test-* |
| - config_name: path_traversal_2k |
| data_files: |
| - split: test |
| path: path_traversal_2k/test-* |
| - config_name: path_traversal_8k |
| 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 |
| path: tom_tracking_0.5k/test-* |
| - config_name: tom_tracking_2k |
| data_files: |
| - split: test |
| path: tom_tracking_2k/test-* |
| - config_name: tom_tracking_8k |
| data_files: |
| - split: test |
| path: tom_tracking_8k/test-* |
| - config_name: travel_planning_2k |
| data_files: |
| - split: test |
| path: travel_planning_2k/test-* |
| - config_name: travel_planning_8k |
| data_files: |
| - split: test |
| path: travel_planning_8k/test-* |
| - config_name: travel_planning_icl_examples |
| data_files: |
| - split: test |
| path: travel_planning_icl_examples/test-* |
| dataset_info: |
| - config_name: countdown_0.5k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 3548952 |
| num_examples: 200 |
| download_size: 408756 |
| dataset_size: 3548952 |
| - config_name: countdown_2k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 4299610 |
| num_examples: 200 |
| download_size: 557786 |
| dataset_size: 4299610 |
| - config_name: countdown_8k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 6816325 |
| num_examples: 200 |
| download_size: 1012154 |
| dataset_size: 6816325 |
| - config_name: html_to_tsv_0.5k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 11123701 |
| num_examples: 100 |
| download_size: 2455321 |
| dataset_size: 11123701 |
| - config_name: html_to_tsv_2k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 32637641 |
| num_examples: 189 |
| download_size: 7035053 |
| dataset_size: 32637641 |
| - config_name: html_to_tsv_8k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 34465586 |
| num_examples: 120 |
| download_size: 7111617 |
| dataset_size: 34465586 |
| - config_name: path_traversal_0.5k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 2430029 |
| num_examples: 200 |
| download_size: 498807 |
| dataset_size: 2430029 |
| - config_name: path_traversal_2k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 7881106 |
| num_examples: 200 |
| download_size: 1868730 |
| dataset_size: 7881106 |
| - config_name: path_traversal_8k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 22492022 |
| num_examples: 200 |
| download_size: 5715549 |
| dataset_size: 22492022 |
| - config_name: pseudo_to_code_0.5k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 1127287 |
| num_examples: 199 |
| download_size: 410927 |
| dataset_size: 1127287 |
| - config_name: pseudo_to_code_2k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 2315410 |
| num_examples: 200 |
| download_size: 471798 |
| dataset_size: 2315410 |
| - config_name: tom_tracking_0.5k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 2425744 |
| num_examples: 200 |
| download_size: 311014 |
| dataset_size: 2425744 |
| - config_name: tom_tracking_2k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 4333995 |
| num_examples: 200 |
| download_size: 561712 |
| dataset_size: 4333995 |
| - config_name: tom_tracking_8k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 11996935 |
| num_examples: 200 |
| download_size: 1479326 |
| dataset_size: 11996935 |
| - config_name: travel_planning_2k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 23775247 |
| num_examples: 769 |
| download_size: 3407254 |
| dataset_size: 23775247 |
| - config_name: travel_planning_8k |
| features: |
| - name: id |
| dtype: string |
| - name: input_prompt |
| dtype: string |
| - name: reference_output |
| dtype: string |
| - name: metadata |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 11798420 |
| num_examples: 239 |
| download_size: 1833210 |
| dataset_size: 11798420 |
| --- |
| |
| # 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`. |
|
|
