August 2022 Summaries

We are outgrowing our Slack workspace and have decided to migrate our Haystack community to Discord, driven by the need for more informal virtual events, closer collaboration with the open source community, and a space for community members to hang out with like-minded people. We've shared our research and detailed rationale for the migration, including tips from another team that made a similar move last year, and will keep Slack live for some months to ensure smooth transition and provide ongoing support. Our Discord server is now active, and we'll arrange office hours and share our learnings with the community in the future. We're excited about the potential of this new platform and look forward to a bright future for the Haystack community.

The author attended the SIGIR22 conference, which was held as a hybrid event with both in-person and virtual components. The author found the in-person sessions to be highly engaging and productive, particularly the Short Paper and Demo Sessions, where they had opportunities to meet topic experts and learn about new topics. However, they noted that the remote talks did not quite match the quality of their in-person counterparts. The conference also featured an industry track, which was well-represented by companies such as Yext and e-commerce vendors. The author was impressed by the sponsors' booths, particularly Bloomberg's, which showcased their analytics system hands-on. They also had opportunities to learn about recent advances in dense retrieval methods and the future of sparse retrieval, with a panel discussion suggesting that sparse retrieval may be the way forward. Additionally, the author highlighted the importance of open-source frameworks and tools, such as Haystack, which enables anyone to build powerful NLP pipelines for search use cases. Overall, the conference was found to be highly engaging and informative, with opportunities for networking and learning from both researchers and industry professionals.

When training a language model, the vast majority of use cases don't require training from scratch, as tens of thousands of pre-trained models are available online that can be used out of the box. However, some use cases benefit from fine-tuning or domain adaptation, which involves refining a pre-trained model on a smaller custom dataset. A language model is not a knowledge base, but rather a computational representation of natural language that enables computers to process human-like language. Pre-trained models can be deployed with frameworks like Haystack without modification or training, and their prediction quality can be evaluated using metrics such as the F1 score. Fine-tuning an existing model involves adding more data and tweaking parameters to increase its accuracy, while domain adaptation focuses on better understanding domain-specific languages. Data labeling is crucial for machine learning models, particularly in NLP, where annotated data can teach a model to handle challenging cases. The process of training a language model can be made easier with tools like Haystack's annotation tool and platforms like deepset Cloud.