Have you ever wondered what research papers have to say about your burning questions? Look no further than Semantic Scholar Q&A with GPT-4! 🙌
This Python script lets you enter a question, and it uses the power of Semantic Scholar and OpenAI to generate an answer based on the content of the top research papers. 🤖🔍
- Ollama support. See notebook
- S2QA now supports llama-index! Check out the data loader here on llama hub
- Updated UI with chat interface:
- Keeps the context
- Shows only used references
- Index once for each research space; vectors and index are cached
- Full-text support!
https://shauryr.github.io/s2qa-demo/
This project is in collaboration with the Semantic Scholar Team. I am thankful for their support and feedback.
- ollama.ipynb ⭐ Ollama support demo
- chat_qa.ipynb ⭐ sends the context to ChatGPT(using revChatGPT) for generating answers. This gives the best answers and is free. Seems less prone to hallucinations than other pipelines in this repo.
- utils.py - has all the necessary functions for search and GPT-3 prompting
- s2qa_sources_langchain.ipynb - Get better answers with langchain mapreduce but this is very expensive. Prone to hallucinations.
- s2qa_nb.ipynb - first notebook. Very basic QA. Prone to hallucinations.
Caution should be exercised when using generative AI for scientific text, as the output generated by such models may not be rigorously fact-checked or peer-reviewed, and may therefore contain errors or inaccuracies that could mislead researchers and readers. Be responsible and use this tool with caution.
Transformers have had a significant impact on language in a variety of ways. One of the most prominent areas of impact is in natural language processing (NLP), which has been revolutionized by the development of transformer-based language models. These models have been used for a variety of NLP tasks, including machine translation [1], code summarization [2], detecting patronizing and condescending language [3], and spoken language understanding tasks [4].
Moreover, transformer-based models have been shown to be effective in domain adaptation for bidirectional transformers [7], multi-modal transformer models have been explored for image captioning [8], and pre-training objectives have been used in software engineering to boost performance in code-related tasks [6]. Additionally, transformers have also been used to incorporate document timestamps into language models, which has shown to have a positive impact on downstream classification tasks and masked language modeling [5].
Overall, transformers have had a significant impact on language, particularly in the field of natural language processing, where transformer-based models have been used for a variety of tasks with great success. They have also been explored in other areas such as software engineering and image captioning, showing the versatility of the technology.
[1] A Study on the journey of Natural Language Processing models: from Symbolic Natural Language Processing to Bidirectional Encoder Representations from Transformers - International Journal of Scientific Research in Computer Science Engineering and Information Technology, 2021
[3] PALI-NLP at SemEval-2022 Task 4: Discriminative Fine-tuning of Transformers for Patronizing and Condescending Language Detection - International Workshop on Semantic Evaluation, 2022
[4] Benchmarking Transformers-based models on French Spoken Language Understanding tasks - Interspeech, 2022
[5] Temporal Language Modeling for Short Text Document Classification with Transformers - Conference on Computer Science and Information Systems, 2022
[7] Exploiting Auxiliary Data for Offensive Language Detection with Bidirectional Transformers - WOAH, 2021
[8] What Does a Language-And-Vision Transformer See: The Impact of Semantic Information on Visual Representations - Frontiers in Artificial Intelligence, 2021
OpenAI API key(if you are using langchain)Semantic Scholar Academic Graph API key- https://www.semanticscholar.org/product/api
These can be added in the constants.py
The main third-party package requirements are tiktoken, openai, transformers and langchain.
To install all the required packages
pip install -r requirements.txt
1️⃣ Searching : We begin by searching the vast and ever-growing database of Semantic Scholar to find the most up-to-date and relevant papers and articles related to your question.
2️⃣ Re-Ranking : We then use SPECTER to embed these papers and re-rank the search results, ensuring that the most informative and relevant articles appear at the top of your search results.
3️⃣ Answering : Finally, we use the powerful natural language processing capabilities of GPT-3 to generate informative and accurate answers to your question, using custom prompts to ensure the best results.
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Try other open embedding methods on huggingface to see better re-ranking results.
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Try other prompts or refine the current prompt to get even better answers.