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@@ -39,7 +39,7 @@ <h2>TL;DR</h2>
 <p>We conclude as follows:</p>
 <blockquote id="conclusion">Openness is not the full solution to the scientific and ethical challenges of conversational text generators. Open data will not mitigate the harmful consequences of thoughtless deployment of large language models, nor the questionable copyright implications of scraping all publicly available data from the internet. However, openness does make original research possible, including efforts to build reproducible workflows and understand the fundamentals of instruction-tuned LLM architectures. Openness also enables checks and balances, fostering a culture of accountability for data and its curation, and for models and their deployment. We hope that our work provides a small step in this direction.
   </blockquote>
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+<h2>Papers</h2>  
 <p>Liesenfeld, Andreas, Alianda Lopez, and Mark Dingemanse. 2023. “Opening up ChatGPT: Tracking Openness, Transparency, and Accountability in Instruction-Tuned Text Generators.” In <em>CUI '23: Proceedings of the 5th International Conference on Conversational User Interfaces</em>. July 19-21, Eindhoven. doi: <a href="https://doi.org/10.1145/3571884.3604316" target="_blank">10.1145/3571884.3604316</a> (<a href="https://pure.mpg.de/pubman/item/item_3526897_1/component/file_3526898/Liesenfeld%20et%20al_2023_Opening%20up%20ChatGPT.pdf" target="_blank">PDF</a>).</p>
 
 <p>Andreas Liesenfeld and Mark Dingemanse. 2024. Rethinking open source generative AI: open washing and the EU AI Act. In <em>The 2024 ACM Conference on Fairness, Accountability, and Transparency (FAccT '24)</em>. Association for Computing Machinery, New York, NY, USA, 1774–1787. doi: <a href="https://doi.org/10.1145/3630106.3659005" target="_blank">10.1145/3630106.3659005</a></p>