| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - remark |
| - recommendation |
| - conversational-recommendation |
| - reddit |
| pretty_name: ReMaRk data (Reddit-Books, Reddit-Books-Late, Reddit-Music; IDs and labels) |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: books_requests |
| data_files: books_requests.jsonl |
| - config_name: books_responses |
| data_files: books_responses.jsonl |
| - config_name: books_sft_targets |
| data_files: books_sft_targets.jsonl |
| - config_name: books_catalog |
| data_files: books_catalog.jsonl |
| - config_name: books_late_requests |
| data_files: books_late_requests.jsonl |
| - config_name: books_late_responses |
| data_files: books_late_responses.jsonl |
| - config_name: books_late_sft_targets |
| data_files: books_late_sft_targets.jsonl |
| - config_name: books_late_catalog |
| data_files: books_late_catalog.jsonl |
| - config_name: music_requests |
| data_files: music_requests.jsonl |
| - config_name: music_responses |
| data_files: music_responses.jsonl |
| - config_name: music_sft_targets |
| data_files: music_sft_targets.jsonl |
| - config_name: music_catalog |
| data_files: music_catalog.jsonl |
| --- |
| |
| # ReMaRk data: Reddit-Books, Reddit-Books-Late and Reddit-Music (IDs and labels) |
|
|
| Data for **Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders** |
| (Xin Li, Hao Jiang, Jinghao Guo, Weisi Lin, Chau Yuen; Nanyang Technological University, Singapore). |
| Code: https://github.com/LiXin97/ReMaRk (the same files are in its `data_release/`). Paper (arXiv): https://arxiv.org/abs/2610.17771. Project page: https://lixin.ai/ReMaRk/. |
| Models and data: [ReMaRk collection](https://huggingface.co/collections/XINLI1997/remark-many-right-answers-one-training-example-6acdc1cf30ec71aff2b4bd62) |
|
|
| Each config is one file (the catalog configs read `*_catalog.jsonl`, a JSON-lines copy of `*_catalog.tsv` with the same rows and columns): requests, responders, labels, SFT targets and catalogs of three datasets built from public Reddit |
| threads (r/suggestmeabook, r/musicsuggestions). Identifiers only, no Reddit text. Reddit-v2 (movies), also used in the paper, |
| is the processed release of Rank-GRPO (Zhu et al.) and is not re-hosted here. |
|
|
| **LLM-produced labels.** Item mentions were extracted from human-written comments by GPT-5.6 and grounded by deterministic |
| rules to Goodreads works (UCSD Book Graph) or MusicBrainz artists. The SFT targets (`*_sft_targets`) are GPT-5.6 |
| recommendation lists grounded to the same catalogs. |
|
|
| **License.** The IDs and labels are released under CC BY-NC 4.0. No Reddit content is redistributed (fetch posts by id under |
| Reddit's terms). Goodreads titles and metadata are not included; look the work ids up in the UCSD Book Graph (academic use). |
| MusicBrainz names and years are CC0. |
|
|
| ## Usage |
|
|
| Each file is its own config (there is no default config): |
|
|
| ```python |
| from datasets import load_dataset |
| |
| requests = load_dataset("XINLI1997/ReMaRk-data", "books_requests", split="train") # 34,944 requests (column `split`: train / validation / test) |
| responses = load_dataset("XINLI1997/ReMaRk-data", "books_responses", split="train") # 162,271 (request, responder) rows |
| catalog = load_dataset("XINLI1997/ReMaRk-data", "books_catalog", split="train") # 74,474 items |
| |
| test_ids = set(requests.filter(lambda r: r["split"] == "test")["request_id"]) |
| test_rows = responses.filter(lambda r: r["request_id"] in test_ids) # 6,333 rows, as in the table below |
| ``` |
|
|
| All row counts in this card were recounted from the files and match `export_counts.json`. |
|
|
| ## Personal information |
|
|
| No Reddit text, user names or user hashes are included. Reddit submission and comment ids are public identifiers: anyone can |
| look the posts up on Reddit, and deleted or removed posts cannot be recovered through them. Responder ordinals are per request |
| and do not identify a person across requests. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{li2026remark, |
| title = {Many Right Answers, One Training Example: Removing Responder Noise from GRPO for LLM Recommenders}, |
| author = {Li, Xin and Jiang, Hao and Guo, Jinghao and Lin, Weisi and Yuen, Chau}, |
| year = {2026}, |
| eprint = {2610.17771}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CL}, |
| url = {https://arxiv.org/abs/2610.17771} |
| } |
| ``` |
|
|
| Please also cite the UCSD Book Graph (Wan and McAuley, RecSys 2018; Wan et al., ACL 2019) when using Reddit-Books, and |
| MusicBrainz when using Reddit-Music. |
|
|
| ## Data release: request / responder / item ids for Reddit-Books, Reddit-Books-Late and Reddit-Music |
|
|
| This repository holds identifiers and labels only. It contains no Reddit text (no posts, comments, prompts or model outputs), |
| no user names and no user hashes. Prompts are rebuilt from Reddit by the scripts in `remark/scripts/` (see "Rebuilding the prompts"). |
| Reddit-v2 (the movie dataset) is not re-hosted; it comes from the Rank-GRPO release (see the top-level README). |
|
|
| Files were written by `remark/scripts/release_export_ids.py`, which also asserts the alignment described below |
| (`export_counts.json` holds the counts it verified). |
|
|
| | dataset | requests file | responses file | SFT targets file | catalog file | |
| |---|---|---|---|---| |
| | Reddit-Books | `books_requests.jsonl` | `books_responses.jsonl` | `books_sft_targets.jsonl` | `books_catalog.tsv` | |
| | Reddit-Books-Late | `books_late_requests.jsonl` | `books_late_responses.jsonl` | `books_late_sft_targets.jsonl` | `books_late_catalog.tsv` | |
| | Reddit-Music | `music_requests.jsonl` | `music_responses.jsonl` | `music_sft_targets.jsonl` | `music_catalog.tsv` | |
|
|
| The `*_sft_targets.jsonl` files are written by `remark/scripts/release_export_sft_targets.py`. |
|
|
| ## Fields |
|
|
| `*_requests.jsonl` (one line per request) |
| - `request_id`: Reddit submission id. |
| - `split`: `train`, `validation` or `test` (by submission month, see below). |
| - `month`: `YYYY-MM` of the submission. |
| - `n_responders`: number of responders of this request that have at least one usable item, i.e. the number of lines of the responses file for this request. |
| - `in_sft_train` (train requests only): true if the request has an SFT target (see "Filters"); the SFT training rows are exactly the responders of these requests. |
| - `n_responders_all` (Music only): number of responders considered before the item filters (the first 20 top-level commenters, with or without a usable item). |
|
|
| `*_responses.jsonl` (one line per responder with at least one usable item; sorted by request, then responder) |
| - `request_id` |
| - `responder`: ordinal 0, 1, 2, ... of the responder within its request (the order of the rows in the dataset; for Books the order of the first comment). It is not an identifier across requests; the same person has unrelated ordinals in different requests. |
| - `item_ids`: indices into the catalog file (`item_id` column), in the order the responder named the items; de-duplicated, with the items the requester already named removed. This is exactly the ground-truth list of the corresponding row of the training/evaluation datasets. |
| - `comment_ids`: Reddit ids of the responder's top-level comments in this request. |
| - Consensus weights are not stored: the weight of item `i` in a request is the fraction of its responders whose `item_ids` contain `i` (this is how the `grpo_consensus` / `grpo_union` training sets are built; union = the set of items). |
|
|
| `*_sft_targets.jsonl` (one line per train request with `in_sft_train` = true, in the order of the requests file) |
| - `request_id` |
| - `item_ids`: the SFT target of the request as catalog indices, in list order (15 to 20 items). **These lists are LLM-generated**: the |
| teacher list is a GPT-5.6 recommendation list for the request, grounded to the catalog, with seen items and duplicates removed. |
| Every SFT training row of the request (one per responder) has this list as its completion, rendered one `title (year)` / |
| `name (year)` per line. The export checks that every line maps to a catalog item and that the rows align with `*_responses.jsonl`. |
|
|
| `books_catalog.tsv`: `item_id`, `goodreads_work_ids`. `item_id` is the 0-based line index of the catalog (also the position in the sorted `(title, first-publication year)` list used by the code). `goodreads_work_ids` lists the Goodreads work id(s) of the item, comma separated (several works can share one `(title, year)` item; most-rated first). Titles and years are not included: they come from the UCSD Book Graph (Wan and McAuley, RecSys 2018; academic use) through the work id, i.e. look `goodreads_work_ids` up in the Book Graph works file. The item title in the datasets is the work's most frequent surface form in the Reddit mentions (made a fixed point of the Rank-GRPO title parser), so it can differ slightly from the Book Graph display title; the year is the Book Graph first-publication year. |
|
|
| `music_catalog.tsv`: `item_id`, `mbid`, `name`, `year`. `item_id` is the line index of the sorted `(name, year)` catalog. `mbid`: MusicBrainz artist id(s) (comma separated; the id used most often in the data first) whose canonical name and begin year give this item (birth year of a solo artist, formation year of a group). `name` and `year` are MusicBrainz core data (CC0). |
|
|
| ## Splits, filters and counts |
|
|
| Request split by submission month; rows of the datasets are (request, responder) pairs. |
|
|
| | dataset | split | months | requests | responses (rows) | notes | |
| |---|---|---|---|---|---| |
| | Reddit-Books | train | 2014-05 .. 2017-07 | 32,539 | 149,679 | 31,172 requests / 146,633 rows have an SFT target | |
| | Reddit-Books | validation | 2017-08 | 1,255 | 6,259 | | |
| | Reddit-Books | test | 2017-09 | 1,150 | 6,333 | | |
| | Reddit-Books | catalog | | 74,474 items | | | |
| | Reddit-Books-Late | train / validation | as Books | 32,539 / 1,255 | 149,679 / 6,259 | same requests and responders as Reddit-Books; item ids refer to the Late catalog, so they differ from the Books ids | |
| | Reddit-Books-Late | test | 2017-10 .. 2017-12 | 3,795 | 21,369 | frozen later test set | |
| | Reddit-Books-Late | catalog | | 75,537 items | | | |
| | Reddit-Music | train | 2023-07 .. 2024-06 | 19,159 | 142,396 | 18,645 requests / 140,231 rows have an SFT target | |
| | Reddit-Music | validation | 2024-07 | 1,966 | 15,060 | | |
| | Reddit-Music | test | 2024-08 | 1,951 | 15,893 | | |
| | Reddit-Music | catalog | | 38,881 items | | | |
|
|
| The counts equal the `stats.json` of the datasets (train rows of the per-responder RL set, validation/test SFT-format rows). |
|
|
| Filters (identical for the three datasets unless noted): |
| - Request = one r/suggestmeabook (Books) or r/musicsuggestions (Music) submission; responders = distinct top-level commenters (Music: the first 20). |
| - Mentions of items were extracted from the comments with an LLM and grounded to Goodreads works (Books, UCSD Book Graph) or MusicBrainz artists (Music); items must have a usable year (Books: first-publication year; Music: begin year) and must be read back exactly by the Rank-GRPO title parser. |
| - Ground truth of a responder = its grounded items in order, de-duplicated, minus the items the requester named ("seen titles"). A responder without such an item gives no row; a request needs at least one row. |
| - Prompt length at most 2,048 tokens (Qwen3.5 chat template). Music: 1 request per split was dropped by this filter. |
| - SFT target (train only, `in_sft_train`): the request's grounded teacher list (GPT-5.6 recommendation list grounded to the catalog, seen items and duplicates removed) has at least 15 items. |
| - Music only: an (author, artist) pair named in 20 or more distinct requests is treated as request-independent promotion and all of its mentions are dropped. |
| - Catalog = all ground-truth items of all splits + the teacher items used in SFT completions + the most-rated 20,000 Goodreads works (Books) or the 20,000 most active MusicBrainz artists with a year (Music). |
|
|
| ## Alignment (how a dataset row maps to a request and responder) |
| - Books / Books-Late: each dataset (`sft_dataset/{train,validation,test}`, `grpo/train`) has a per-row id table in the same row order. A row belongs to the request of its submission id; its responder ordinal is its rank among the rows of that submission. Train responses come from the per-responder RL set (all train requests), validation and test responses from the SFT-format validation/test sets. For train requests with an SFT target, the SFT rows are checked to be the same responders, in the same order, with the same ground truth. |
| - `item_ids` are obtained by mapping each ground-truth `(title, year)` of a row to its position in the catalog (all matches exist; the match key is the exact `(title, int(year))` pair). |
| - Music has no id table; the build is replayed on the grounded mentions (promotion filter, year rendering, seen-name filter, per-responder de-duplication) and the dataset rows are matched to the replayed requests block by block, requiring exactly the same ground-truth lists. All requests of every split matched; the only unmatched candidates are the requests removed by the prompt-length filter (1 per split), and the matched request / row counts equal the build statistics. |
|
|
| ## Rebuilding the prompts |
| Prompts are the request text of the Reddit submission (title and body, and the thread context where used) behind a fixed instruction header |
| (see `remark/scripts/books_common.py` and `music_common.py`). Users with Reddit access (for example the Arctic Shift dumps) can run |
| `books_pull.py -> books_threads.py -> ...` and `books_build.py` / `music_build.py` as listed in the top-level README, or join this release by `request_id` |
| to rebuild each prompt and then attach `item_ids` as the label lists. Deleted or removed posts cannot be recovered. |
|
|
| ## Licences and sources |
| The IDs and labels in this directory are released under CC BY-NC 4.0. Labels were produced with LLMs: item mentions were extracted from human-written comments by GPT-5.6 and grounded by deterministic rules; SFT targets are GPT-5.6 recommendation lists grounded to the catalogs. |
| Reddit ids are public identifiers; no Reddit content is redistributed. Goodreads data (UCSD Book Graph) is academic-use only and is not redistributed here: only work ids are listed. MusicBrainz core data is CC0. |
| Sources of these terms: the UCSD Book Graph page (https://cseweb.ucsd.edu/~jmcauley/datasets/goodreads.html: "for academic use only. Please do not redistribute them or use for commercial purposes") and the MusicBrainz data license (https://musicbrainz.org/doc/About/Data_License: core data under CC0). |
| |
