Datasets:

stanfordnlp
/

SHP

@@ -20,12 +20,13 @@ language:
 ## Summary
-SHP is a dataset of **385K collective human preferences** over responses to questions/instructions in 18 different subject areas, from cooking to legal advice (see the [Design section](https://huggingface.co/datasets/stanfordnlp/SHP#dataset-design) for a breakdown of the domains).
-It is primarily intended to be used for training reward models for RLHF and automatic evaluation models for NLG.
-Each example is a Reddit post and a pair of top-level comments for that post, where one comment is more preferred by Reddit users (collectively).
 SHP exploits the fact that if comment A was written *after* comment B but has a higher score nonetheless, then A is ostensibly more preferred to B.
 If A had been written before B, then we could not conclude this, since its higher score could have been the result of more visibility from being written first.
 How is SHP different from [Anthropic's HH-RLHF dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf)?
 Most notably, all the data in SHP is naturally occurring and human-written, whereas the responses in HH-RLHF are machine-written, giving us two very different distributions that can complement each other.
@@ -36,7 +37,7 @@ Most notably, all the data in SHP is naturally occurring and human-written, wher
 | HH-RLHF    | 91K | Dialogue with LLM  | Individual Human Preference  | not labelled   | Live Chat (Multi-turn)              | up to 1.5K T5 tokens |
 How is SHP different from other datasets that have scraped Reddit, like [ELI5](https://huggingface.co/datasets/eli5#source-data)?
-Most notably, SHP uses the timestamp information to infer preferences, while ELI5 only provides comments and scores -- the latter are not enough to infer preferences since comments made earlier tend to get higher scores from more visibility.
 It also contains data from more domains:
 | Dataset              | Size | Comments + Scores  | Preferences  | Number of Domains  |
@@ -101,19 +102,16 @@ where the fields are:
 ## Dataset Design
 The data is sourced from Reddit, which is a public forum organized into topic-specific fora called *subreddits*.
 For example, the `askculinary` subreddit is where users ask cooking-related questions and are answered by other users.
-The score of a post/comment is 1 plus the number of upvotes it gets from users, minus the number of downvotes it gets.
-The value of a score is relative; in subreddits(posts) with more traffic, there will be more higher-scoring posts(comments).
-Within a post, comments posted earlier will tend to have a higher score simply due to having more exposure.
-### Subreddit Selection
 SHP contains a train, validation, and test split for comments scraped from 18 different subreddits. We chose subreddits based on:
 1. whether they were well-known (subscriber count >= 50K)
-2. whether posts were expected to pose a question or instruction that the top-level comments were meant to answer
-3. whether comments had to be rooted in some objectivity, instead of being entirely about personal experiences (e.g., `askscience` vs. `AskAmericans`)
 The train/validation/test splits were created by splitting the post IDs of a subreddit in 90%/5%/5% proportions respectively, so that no post would appear in multiple splits.
 Since different posts have different numbers of comments, the number of preferences in each split is not exactly 90%/5%/5%:
@@ -140,7 +138,11 @@ Since different posts have different numbers of comments, the number of preferen
 | legaladvice        | 21170 | 1106 | 1011 | 23287 |
 | ALL | 348718 | 18436 | 18409 | 385563 |
-### Post and Comment Selection
 Given a post P and two comments (A,B) we only included the preference A > B in the dataset if
 1. A was written *no later than* B and A has a higher score than B.
@@ -203,15 +205,20 @@ We encourage you to use SteamSHP for NLG evaluation, for building reward models
 ## Biases and Limitations
 Although we filtered out posts with NSFW (over 18) content, chose subreddits that were well-moderated and had policies against harassment and bigotry, some of the data may contain discriminatory or harmful language.
 The data does not reflect the views of the dataset creators.
-Reddit users on these subreddits are also not representative of the broader population. They are disproportionately from developed, Western, and English-speaking countries.
 One should keep that in mind before using any models trained on this data.
-It is also worth noting that the comment more preferred by Redditors is not necessarily the more correct one, and though some comments do provide citations to justify their response, most do not.
-There are exceptions to this, such as the `askhistorians` subreddit, which is heavily moderated and answers are expected to provide citations.
-As always, remember to evaluate!
 ## Contact

 ## Summary
+SHP is a dataset of **385K collective human preferences** over responses to questions/instructions in 18 different subject areas, from cooking to legal advice.
+The preferences are meant to reflect the helpfulness of one response over another, and are intended to be used for training RLHF reward models and NLG evaluation models (e.g., [SteamSHP](https://huggingface.co/stanfordnlp/SteamSHP-flan-t5-xl)).
+Each example is a Reddit post with a question/instruction and a pair of top-level comments for that post, where one comment is more preferred by Reddit users (collectively).
 SHP exploits the fact that if comment A was written *after* comment B but has a higher score nonetheless, then A is ostensibly more preferred to B.
 If A had been written before B, then we could not conclude this, since its higher score could have been the result of more visibility from being written first.
+We chose data where the preference label is intended to reflect which response is more *helpful* rather than which is less *harmful*, the latter being the focus of much past work.
 How is SHP different from [Anthropic's HH-RLHF dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf)?
 Most notably, all the data in SHP is naturally occurring and human-written, whereas the responses in HH-RLHF are machine-written, giving us two very different distributions that can complement each other.
 | HH-RLHF    | 91K | Dialogue with LLM  | Individual Human Preference  | not labelled   | Live Chat (Multi-turn)              | up to 1.5K T5 tokens |
 How is SHP different from other datasets that have scraped Reddit, like [ELI5](https://huggingface.co/datasets/eli5#source-data)?
+SHP uses the timestamp information to infer preferences, while ELI5 only provides comments and scores -- the latter are not enough to infer preferences since comments made earlier tend to get higher scores from more visibility.
 It also contains data from more domains:
 | Dataset              | Size | Comments + Scores  | Preferences  | Number of Domains  |
 ## Dataset Design
+### Domain Selection
 The data is sourced from Reddit, which is a public forum organized into topic-specific fora called *subreddits*.
 For example, the `askculinary` subreddit is where users ask cooking-related questions and are answered by other users.
 SHP contains a train, validation, and test split for comments scraped from 18 different subreddits. We chose subreddits based on:
 1. whether they were well-known (subscriber count >= 50K)
+2. whether posts were expected to pose a question or instruction
+3. whether responses were valued based on how *helpful* they were
+4. whether comments had to be rooted in some objectivity, instead of being entirely about personal experiences (e.g., `askscience` vs. `AskAmericans`)
 The train/validation/test splits were created by splitting the post IDs of a subreddit in 90%/5%/5% proportions respectively, so that no post would appear in multiple splits.
 Since different posts have different numbers of comments, the number of preferences in each split is not exactly 90%/5%/5%:
 | legaladvice        | 21170 | 1106 | 1011 | 23287 |
 | ALL | 348718 | 18436 | 18409 | 385563 |
+### Data Selection
+The score of a post/comment is 1 plus the number of upvotes (approvals) it gets from users, minus the number of downvotes (disapprovals) it gets.
+The value of a score is relative; in subreddits(posts) with more traffic, there will be more higher-scoring posts(comments).
+Within a post, comments posted earlier will tend to have a higher score simply due to having more exposure, which is why using timestamp information is essential when inferring preferences.
 Given a post P and two comments (A,B) we only included the preference A > B in the dataset if
 1. A was written *no later than* B and A has a higher score than B.
 ## Biases and Limitations
+### Biases
 Although we filtered out posts with NSFW (over 18) content, chose subreddits that were well-moderated and had policies against harassment and bigotry, some of the data may contain discriminatory or harmful language.
 The data does not reflect the views of the dataset creators.
+Reddit users on these subreddits are also not representative of the broader population.
+Although subreddit-specific demographic information is not available, Reddit users overall are disproportionately male and from developed, Western, and English-speaking countries ([Pew Research](https://www.pewresearch.org/internet/2013/07/03/6-of-online-adults-are-reddit-users/)).
 One should keep that in mind before using any models trained on this data.
+### Limitations
+The preference label in SHP is intended to reflect how *helpful* one response is relative to another, given an instruction/question.
+However, the more preferred response is not necessarily the more factual one.
+Though some comments do provide citations to justify their response, most do not.
+There are exceptions to this, such as the `askhistorians` subreddit, which is heavily moderated and answers are expected to provide citations.
 ## Contact