Update README.md

README.md

@@ -37,7 +37,7 @@ Most notably, SHP exploits the timestamp hack to infer preferences, rather than
 | Dataset              | Size | Comments + Scores  | Preferences  | Number of Domains  |
 | -------------------- | ---- | ------------------ | -------------| ------------------ |
 | SHP                  | 385K | Yes                | Yes          | 18  |
-| ELI5                 | 91K  | Yes                | No           | 3   |
+| ELI5                 | 270K | Yes                | No           | 3   |
 
 
 ## Data Structure
@@ -91,7 +91,7 @@ where the fields are:
 - ```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 score_A:score B (will be >= 1) (float)
+- ```score_ratio```: the ratio of the more preferred comment's score to the less preferred comment's score (will be >= 1) (float)
 
 
 ## Dataset Design
@@ -163,7 +163,9 @@ In hyperlinks, only the referring text was kept and the URL was removed (if the
 
 If you want to finetune a model to predict human preferences (e.g., for NLG evaluation or an RLHF reward model), here are some helpful tips:
 
-1. **Use a sufficiently large model.** With FLAN-T5-xl, you can get 65-85% accuracies depending on the subreddit.
+1. **Use a sufficiently large model.**
+   Finetuning a single FLAN-T5-xl model across all the training data should give you a test accuracy between 72-73% (across all domains), ranging from 65-80% on individual subreddits.
+   Finetuning a single model on just a single domain will give you better performance on that domain.
 2. **Do in-domain prediction.** Out-of-domain performance will be poor if the subreddits are unrelated (e.g., if you fine-tune on `askculinary` preferences and test on `askcarguys` preferences).
 3. **Preprocess the data.** The total input length should fit under the model's token limit (usually 512 tokens).
    Although models like FLAN-T5 use positional embeddings, we found that the loss would not converge if we finetuned it on inputs over 512 tokens.