Update README.md

README.md

@@ -10,19 +10,11 @@ license: apache-2.0
 short_description: LLms for Detecting Bias in Job Descriptions.
 ---
 
-Abstract—This study explores the application of large language
-(LLM) models for detecting implicit bias in job descriptions,
-an important concern in human resources that shapes applicant
-pools and influences employer perception. We compare different
-LLM architectures—encoder, encoder-decoder, and decoder models—focusing on seven specific bias types. The research questions
-address the capability of foundation LLMs to detect implicit
-bias and the effectiveness of domain adaptation via fine-tuning
-versus prompt tuning. Results indicate that fine-tuned models
-outperform non-fine-tuned models in detecting biases, with Flan-T5-XL emerging as the top performer, surpassing the zero-shot
-prompting of GPT-4o model. A labelled dataset comprising gold,
-silver, and bronze-standard data was created for this purpose
-and open-sourced to advance the field and serve as a valuable
-resource for future studies.
+**Abstract**—This study explores the application of large language (LLM) models for detecting implicit bias in job descriptions, an important concern in human resources that shapes applicant pools and influences employer perception.
+We compare different LLM architectures—encoder, encoder-decoder, and decoder models—focusing on seven specific bias types. 
+The research questions address the capability of foundation LLMs to detect implicit bias and the effectiveness of domain adaptation via fine-tuning versus prompt-tuning.
+Results indicate that fine-tuned models are more effective in detecting biases, with Flan-T5-XL emerging as the top performer, surpassing the zero-shot prompting of GPT-4o model.
+A labelled dataset consisting of verified gold-standard, silver-standard, and unverified bronze-standard data was created for this purpose and [open-sourced](https://huggingface.co/datasets/2024-mcm-everitt-ryan/benchmark) to advance the field and serve as a valuable resource for future research.
 
 # Short Introduction
 Introduction—In human resources, bias affects both employers and employees in explicit and implicit forms. Explicit bias is