Ever felt overwhelmed while reading a ton of research papers? If so, PaperPal is here to help.
PaperPal is an AI driven tool for *summarizing research papers and answering related research questions. *

This is a tool tailored for students and researchers who want to simplify the research process i.e consuming a lot of research in a very short amount of time.

Making of PaperPal

During the Initial planning phase of PaperPal, I thought of using ChatGpt to power the application. I had used this in my previous projects so I thought this was a good idea. But sadly, it turns out that Gpt no longer provides credits for testing.

Then, After some research on Anthropic and Mistral AI, I went on to LLAMA. This was the great moment of realization for PaperPal. Though, I faced a lot of challenges like:

1.Not having access to llama-3 models
2.Not knowing about huggingface_hub and login it requires
3.Even after logging it threw an error that I was trying to access a gated repo.

4.Then after taking a look around to see if there was another way of doing this, I found hugging face Api Inference. This is the very snippet that guided me on how to make it work.

5.A moment of breakthrough I must say because using models has never been this easy. This was my version of implementation.

Why did I face this Problem and How can people like me avoid this and make the most of such models?

Well, in all honesty, I faced this issue because I am more of a Visual Learner. Someone who learns more from Images, Videos rather than long boring texts. I was trying to figure it out on my own without relying on available relevant documentation. Due to this, even easy answers came to me through a hard route.

If you are someone like me, all I can advise is “Read the Docs”. That's all there is to it. The more you read the easier it becomes.

This tool comes with three primary functions:
1.Research Paper Summarization.
2.QnA related to the Research
3.Translation of Research to other languages for multilingual support

Problems in Current System

1.Time Consuming Research
2.Difficulty in extracting key insights
3.General Answers to paper related questions

Solution by PaperPal

1.Automated Summarization
2.Focused Insight Extraction
3.Research specific QnA

Feasibility of PaperPal

There are certain studies and tools that clearly show how feasible and important tools like PaperPal are in the market. Here are two examples:

• Genei's AI for Research Summarization: An AI tool, Genei, offers summarization and question-answering capabilities, demonstrates that 95% of its users find it enhances their research productivity.Link
• AI in Research Paper Summarization and Accessibility: A study published in Nature explores the potential of AI tools to mass-produce lay summaries of research articles, helping bridge the gap between complex scientific content and broader readerships.Link

Advantage of LLAMA over other models

While building this project, I observed some of the advantages of using LLAMA over other models like ChatGPT, Anthropic, and Mistral AI. Some of them are listed below:

1.LLAMA is an open-source model. While models like ChatGPT, Anthropic, Mistral AI offered free trials with limited access to the API, they often required a minimum credit balance even for limited access. On the other hand, LLAMA models that I have used such as facebook/nllb-200-distilled-600M and TinyLlama allowed access without any credit balance requirement and offered better customization and flexibility.

2.LLAMA allows us to fine-tune the model based on our requirements to perform specific tasks such as translation, chats, etc.

3.LLAMA offers better data privacy than other commercial models such as ChatGPT, Anthropic, Mistral AI, etc. The commercial models often collect user inputs and analyse them in order to improve their performance, raising the concerns over data privacy.

STRUCTURE OF PAPERPAL

USED MODELS

For PaperPal, I used two specific models for chat and translation, chosen for their effectiveness and suitability for these roles.
1.TinyLlama-1.1B-Chat-v1.0: This model is perfect for the chat functionality because of its ability to handle conversational queries efficiently. Despite being lightweight compared to other models, it delivers fast responses without compromising the quality of the generated text. It’s also highly customizable, which allowed me to fine-tune it to better answer research-specific questions and offer more targeted insights during the Q&A process.

2.facebook/nllb-200-distilled-600M: For the translation feature, this model excels because it supports over 200 languages with strong multilingual translation abilities. It's designed specifically for efficient and high-quality translations, making it ideal for researchers who need to read papers in different languages. The model’s distilled version provides faster translations with minimal resources, making it both practical and scalable.

Both models strike a balance between performance and resource efficiency, which makes them perfect for handling the core tasks in PaperPal.

CONCLUSION

The PaperPal project successfully utilizes the capabilities of the given models to provide user with automated summarization and translation of research articles thereby enhancing the accessibility of research.