Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

docs: Update links to arxiv landing pages rather than PDFs #249

Merged
merged 1 commit into from
Jan 15, 2024
Merged

docs: Update links to arxiv landing pages rather than PDFs #249

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
6 changes: 3 additions & 3 deletions docs/technical-reference/tasks.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,7 @@ For the API reference visit [TextGenerationTask][distilabel.tasks.text_generatio
### SelfInstructTask

The task specially designed to build the prompts following the Self-Instruct paper: [SELF-INSTRUCT: Aligning Language Models
with Self-Generated Instructions](https://arxiv.org/pdf/2212.10560.pdf).
with Self-Generated Instructions](https://arxiv.org/abs/2212.10560).

From the original [repository](https://github.com/yizhongw/self-instruct/tree/main#how-self-instruct-works): *The Self-Instruct process is an iterative bootstrapping algorithm that starts with a seed set of manually-written instructions and uses them to prompt the language model to generate new instructions and corresponding input-output instances*, so this `Task` is specially interesting for generating new datasets from a set of predefined topics.

Expand All @@ -66,7 +66,7 @@ Contrary to the `TextGenerationTask`, the `PreferenceTask` is not intended for d

#### UltraFeedbackTask

This task is specifically designed to build the prompts following the format defined in the ["UltraFeedback: Boosting Language Models With High Quality Feedback"](https://arxiv.org/pdf/2310.01377.pdf) paper.
This task is specifically designed to build the prompts following the format defined in the ["UltraFeedback: Boosting Language Models With High Quality Feedback"](https://arxiv.org/abs/2310.01377) paper.

From the original [repository](https://github.com/OpenBMB/UltraFeedback): *To collect high-quality preference and textual feedback, we design a fine-grained annotation instruction, which contains 4 different aspects, namely instruction-following, truthfulness, honesty and helpfulness*. This `Task` is designed to label datasets following the different aspects defined for the UltraFeedback dataset creation.

Expand Down Expand Up @@ -123,7 +123,7 @@ For the API reference visit [UltraFeedbackTask][distilabel.tasks.preference.ultr

#### JudgeLMTask

The task specially designed to build the prompts following the UltraFeedback paper: [JudgeLM: Fine-tuned Large Language Models Are Scalable Judges](https://arxiv.org/pdf/2310.17631.pdf). This task is designed to evaluate the performance of AI assistants.
The task specially designed to build the prompts following the UltraFeedback paper: [JudgeLM: Fine-tuned Large Language Models Are Scalable Judges](https://arxiv.org/abs/2310.17631). This task is designed to evaluate the performance of AI assistants.

```python
--8<-- "docs/snippets/technical-reference/tasks/openai_judgelm.py"
Expand Down