Add pipeline tag, library name, and prominent GitHub link (#1)

- Add pipeline tag, library name, and prominent GitHub link (1a58f8cb96f41fa78fd2ced477dc9f7f069be983)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,19 +1,26 @@
 ---
-license: apache-2.0
-language:
-- en
 base_model:
 - Qwen/Qwen2.5-7B-Instruct
+language:
+- en
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
 ---
-# Introduction
 
-This is the official repo of the paper [Annotation-Efficient Universal Honesty Alignment](https://arxiv.org/abs/2510.17509)
+# Annotation-Efficient Universal Honesty Alignment
+
+This is the official repository for the paper [Annotation-Efficient Universal Honesty Alignment](https://arxiv.org/abs/2510.17509).
+
+Code: [https://github.com/Trustworthy-Information-Access/Annotation-Efficient-Universal-Honesty-Alignment](https://github.com/Trustworthy-Information-Access/Annotation-Efficient-Universal-Honesty-Alignment)
+
+## Introduction
 
 This repository provides modules that extend **Qwen2.5-7B-Instruct** with the ability to generate accurate confidence scores *before* response generation, indicating how likely the model is to answer a given question correctly across tasks. We offer two types of modules—**LoRA + Linear Head** and **Linear Head**—along with model parameters under three training settings:
 
-1. **Elicitation (greedy):** Trained on all questions (over 560k) using self-consistency-based confidence annotations.
-2. **Calibration-Only (right):** Trained on questions with explicit correctness annotations.
-3. **EliCal (hybrid):** Initialized from the Elicitation model and further trained on correctness-labeled data.
+1.  **Elicitation (greedy):** Trained on all questions (over 560k) using self-consistency-based confidence annotations.
+2.  **Calibration-Only (right):** Trained on questions with explicit correctness annotations.
+3.  **EliCal (hybrid):** Initialized from the Elicitation model and further trained on correctness-labeled data.
 
 For both **Calibration-Only** and **EliCal** settings, we provide models trained with different amounts of annotated data (1k, 2k, 3k, 5k, 8k, 10k, 20k, 30k, 50k, 80k, 200k, 560k+). Since **LoRA + Linear Head** is the main configuration used in our paper, the following description is based on this setup.