Update README.md

README.md

@@ -29,13 +29,13 @@ SHP exploits the fact that if comment A was written *after* comment B but has a
 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.
 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.
+How is SHP different from [Anthropic's HH-RLHF dataset](https://huggingface.co/datasets/Anthropic/hh-rlhf) and [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1)? 
 
 | Dataset              | Size | Input                      | Label                       | Domains           | Data Format                           | Length |
 | -------------------- | ---- | -------------------------- | ---------------------------- | ------------------------- | ------------------------------------- | --------------- |
 | SHP-2                  | 4.8M | Naturally occurring human-written responses   | Collective Human Preference  | 129 (labelled)  | Question/Instruction + Response (Single-turn) | up to 10.1K T5 tokens |
 | HH-RLHF    | 91K | Dialogue with LLM  | Individual Human Preference  | not labelled   | Live Chat (Multi-turn)              | up to 1.5K T5 tokens |
+| OASST      | 161K | Dialogue with LLM  | K Individual Preferences, Aggregated | 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.
@@ -88,23 +88,6 @@ Here's an example from `reddit/askculinary/train.json`:
 }
 ```
 
-where the fields are:
-- ```post_id```: the ID of the Reddit post (string)
-- ```domain```: the subreddit and split the example is drawn from, separated by an underscore (string)
-- ```upvote_ratio```: the percent of votes received by the post that were positive (aka upvotes) (float)
-- ```history```: the post title concatented to the post body (string)
-- ```c_root_id_A```: the ID of comment A (string)
-- ```c_root_id_B```: the ID of comment B (string)
-- ```created_at_utc_A```: utc timestamp of when comment A was created (integer)
-- ```created_at_utc_B```: utc timestamp of when comment B was created (integer)
-- ```score_A```: (# positive votes - # negative votes + 1) received by comment A (integer)
-- ```score_B```: (# positive votes - # negative votes + 1) received by comment B (integer)
-- ```human_ref_A```: text of comment A (string)
-- ```human_ref_B```: text of comment B (string)
-- ```labels```: the preference label -- it is 1 if A is preferred to B; 0 if B is preferred to A. This was randomized such that the label distribution is roughly 50/50. (integer)
-- ```seconds_difference```: how many seconds after the less preferred comment the more preferred one was created (will always be >= 0) (integer)
-- ```score_ratio```: the ratio of the more preferred comment's score to the less preferred comment's score (will be >= 1) (float)
-
 Here's an example from `stackexchange/stack_academia/validation.json`:
 ```
 {
@@ -126,23 +109,40 @@ Here's an example from `stackexchange/stack_academia/validation.json`:
 }
 ```
 
+where the fields are:
+- ```post_id```: the ID of the Reddit post (string)
+- ```domain```: the subreddit and split the example is drawn from, separated by an underscore (string)
+- ```upvote_ratio```: the percent of votes received by the post that were positive (aka upvotes) (float)
+- ```history```: the post title concatented to the post body (string)
+- ```c_root_id_A```: the ID of comment A (string)
+- ```c_root_id_B```: the ID of comment B (string)
+- ```created_at_utc_A```: utc timestamp of when comment A was created (integer)
+- ```created_at_utc_B```: utc timestamp of when comment B was created (integer)
+- ```score_A```: (# positive votes - # negative votes + 1) received by comment A (integer)
+- ```score_B```: (# positive votes - # negative votes + 1) received by comment B (integer)
+- ```human_ref_A```: text of comment A (string)
+- ```human_ref_B```: text of comment B (string)
+- ```labels```: the preference label -- it is 1 if A is preferred to B; 0 if B is preferred to A. This was randomized such that the label distribution is roughly 50/50. (integer)
+- ```seconds_difference```: how many seconds after the less preferred comment the more preferred one was created (will always be >= 0) (integer)
+- ```score_ratio```: the ratio of the more preferred comment's score to the less preferred comment's score (will be >= 1) (float)
+
+
+
 ## Dataset Design
 
 ### Domain Selection
-TODO: check if this section is still correct
-
-The data is sourced from Reddit and StackExchange, which are both public forums organized into different sub-domains.
+The data is sourced from Reddit and StackExchange, which are both public forums organized into different domains.
 
-SHP-2 contains a train, validation, and test split for comments scraped from each sub-domain. We chose sub-domains based on:
-1. whether they were well-known (subscriber count >= 100K)
+SHP-2 contains a train, validation, and test split for comments scraped from each domain. We chose domains based on:
+1. whether they were well-known (>= 100K subscribers for Reddit and >= 50K for StackExchange)
 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.
+The train/validation/test splits were created by splitting the post IDs of a domain 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%.
 
-See below for a list of all sub-domains:
+See below for a list of all domains:
 
 Reddit: \
 techsupport, asklinguistics, askscience, catadvice, campingandhiking, askphysics, espresso, botany, asksocialscience, askbaking, ultralight, legaladvice, hiking, webdev, askengineers, screenwriting, askhistorians, vegetarian, writing, diy, musictheory, camping, moviesuggestions, askeconomics, stocks, frugal, outoftheloop, booksuggestions, gamedev, linuxquestions, asknetsec, aviation, askacademia, asksciencefiction, askhr, explainlikeimfive, etymology, entrepreneur, cooking, puppy101, keto, crochet, smallbusiness, architecture, artfundamentals, sewing, zerowaste, changemyview, mechanicadvice, iwanttolearn, eatcheapandhealthy, askanthropology, askculinary, askphilosophy, tea, running, excel, homebrewing, solotravel, fishing, cookingforbeginners, homeautomation, ifyoulikeblank, travel, suggestmeabook, televisionsuggestions, sysadmin, askcarguys, askdocs, askvet
@@ -154,13 +154,14 @@ stack_unix, stack_android, stack_academia, stack_superuser, stack_tex, stack_pho
 ### Data Selection
 TODO: check if this section holds for stack
 
-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.
+For Reddit, 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.
+For Stackexchange, the score of a post/comment is 0 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 domains(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.
-2. The post is a self-post (i.e., a body of text and not a link to another page) made before 2023, was not edited, and is not NSFW (over 18).
+2. The post is a self-post (i.e., a body of text and not a link to another page) made before 2023, was not edited, and is not NSFW (over 18). For Stackexchange, 
 3. Neither comment was made by a deleted user, a moderator, or the post creator. The post was not made by a deleted user or moderator.
 4. The post has a score >= 10 and each comment has a score >= 2 (upvoted at least once).