Neeva Profile picture
Jan 17, 2023 11 tweets 4 min read Read on X
1/ Have you heard? Bias Buster is now available in #Neeva's main search tab!

🔎 Try a search here: neeva.com/search?q=calif…

And if you're wondering how we crawled and evaluated topics to create our 5 point scale slider, stay tuned! 🤓

We dive into it on this thread 🧵… Image
2/ Our goal? 👉 Show a variety of POVs on particular news topics.

To reach this goal, we categorized results based on 5 buckets to ensure a smooth experience while interacting with the slider. This includes:

🪣 Far Left
🪣 Left Leaning
🪣 Center
🪣 Right Leaning
🪣 Far Right
3/ So, how do we categorize our results to fit these buckets?

By using third party media bias tools, such as @AllSidesNow and @MBFC_News.

Each result is categorized by its respective domain.
4/ We use these ratings to filter and re-rank what results show in what bucket.

Categorizing domains is not a perfect science, so we make sure to show results at most one bucket away from the selection on the slider. Image
5/ With this bucketing implementation, we need to ensure that we have domains to serve from all of these perspectives.

We collected a variety of domains from each of the 5 buckets, and pulled the respective sitemaps.

These sitemaps are fed into our crawl pipeline.
6/ Previously, crawling and indexing one URL into Neeva’s own index took more than 2 weeks after the URL’s discovery.

Apparently, this is too stale in terms of serving news pages. Image
7/ In order to serve news pages in a fresh way, we build our fresh crawl-indexing pipeline.

Every hour, we crawl and index URLs from a couple of sources, including:
📌 Sitemaps
📌 Twitter feeds
📌 API crawl, etc.

From there we fast-track these pages into our Koala indexing. 🐨
8/ To utilize Bias Buster to its full potential, we implemented triggering logic.

This allows the slider to show if there are a variety of results to view on the spectrum.

We determined these queries are typically ones that have a high news intent, as well as political intent.
9/ So, we:

1️⃣ Probe the result sets pulled from the buckets to gain intuition on variety
2️⃣ Check political intent & topicality
3️⃣ Check if the query has any identified intents for which we shouldn't trigger on
10/ Here's an example...

If the query includes a site restrict, we wouldn't want to display Bias Buster, since the ultimate intent is to see results from that site. Image
11/ Overall, Bias Buster gives an opportunity for our US users to explore different perspectives on the political spectrum when available.

Head over to neeva.com to try it out and let us know what you think!

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More from @Neeva

Mar 15, 2023
What if you could control the sources that go into your AI search engine?

At @Neeva, we're making that a reality.

We're excited to share we added #NeevaAI:
✅ Answer support for verified health sites, official programming sites, blogs, etc.
✅ Availability in the News tab

🧵
First, at @Neeva we're passionate about generative search engines combining the best of search & AI.

But it's clear generative AI systems have no notion of sources or authority.

Their content is based on their reading of source material, which is often a copy of the entire Web.
On the other hand, search engines care about authority very intimately.

#PageRank (the algorithm that got @Google going) was committed to a better authority signal to score pages, based on the citations they got from other high scoring pages.
Read 12 tweets
Mar 11, 2023
Considering using a LLM for production, but it's too slow?

Well fear not! Today you can with Double Pseudo Labeling!

Here's why the @Neeva team did, and we've never looked back!

🧵 Image
Before we get started, let's briefly go over #NeevaAI and what it is...

✅ Our AI generates answers
✅ Our Search makes sure answers are timely & factual

All in all, @Neeva combines search + AI to generate a single, cited for each of your searches.
#NeevaAI works by combining search & phases of generative LLMs to generate AI answers

Phase 1️⃣: Run per-doc summarization and question-answering models on the top results

Phase 2️⃣: Run a cross-document attributed summarizer to synthesize a single answer for the query

Example⬇️ Image
Read 17 tweets
Feb 10, 2023
Yesterday, we talked about why AI chatbots generate frankenanswers.

As promised, today we're going over how NeevaAI implements our solution to frankenanswers.

BIG thanks to our talented AI/ML team: @avinashparchuri @rahilbathwal

Keep reading for how we tackle this problem ⤵️
And if you didn't read our thread on this yesterday, check out part 1 below.

(we promise it makes more sense if you do! 😜)
In order to fix frankenanswers, we're required to take a step back and ask ourselves... what would we like NeevaAI to do in such cases?
Read 15 tweets
Feb 9, 2023
Have you seen ChatGPT combine info on multiple entities into an answer that’s completely WRONG? 😬

Generative AI and LLM models can mix up names or concepts & confidently regurgitate frankenanswers.

Neeva is solving this problem on our AI-powered search engine.

Here’s how 🧵
FYI This is a two-part thread series.

Today, with the help of @rahilbathwal, we’ll explain why the problems happen technically.

Tomorrow, we’ll talk through how we’re implementing our solution with our AI/ML team.

Make sure you're following... 👀
In frankenanswers, a generative AI model combines information about multiple possible entities into an answer that’s wrong.

Ex) On this query for `imran ahmed’ from our early test builds, you see a mix up of many intents corresponding to different entities with the same name.👇
Read 13 tweets
Feb 6, 2023
1/ NeevaAI serves abstractive summaries of web pages that are generated in real-time.

We achieved this by a ~10x reduction in latency of a fine-tuned t5-large encoder-decoder model.

TY @asimshankar, @rajhans_samdani, @AshwinDevaraj3 + @spacemanidol

See our lessons learned.. 🧵
2/ First off, we found that there are far fewer resources available for optimizing encoder-decoder models (when compared to encoder models like BERT and decoder models like GPT).

We hope this thread will fill in the void and serve as a good resource. 📂
3/ We started with a flan-T5-large model and tuned it on our dataset. We picked the large variant because we found it to generate better summaries with fewer hallucinations and fluency issues.

The problem? The latency is too high for a search product.
Read 14 tweets
Jan 18, 2023
1/ At #Neeva, design is the act of giving form to an idea: we gather data and inspiration, think, make, and iterate through feedback. 💡

Here's how our team, working alongside the ✨Neeva Community✨, shaped our latest news tool, #BiasBuster...

(read on 📖)
2/ To improve the news experience on Neeva, we solicited insights from users of various news outlets.

One early finding 👉 the journey to get daily news typically started from news providers' sites and apps, but NOT from a search engine.

🤔
3/ So we asked ourselves, when does a search engine becomes necessary and helpful in the journey? 💭

Several users shared that they searched for specific events and stories about which they wanted to learn more.

An avid news user put, "Search is for focused topics.".
Read 13 tweets

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