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Shreya Shankar Profile picture
@sh_reya
Jul 22, 2020 • 3 tweets • 1 min read • Read on X
I should have spoken up earlier about the ability to easily get toxic outputs from GPT-3. By posting a demo without reinforcing that it can exhibit harmful biases, I probably exacerbated the hype. The paradigm shift towards few-shot learning is cool, but there are consequences.
I also think it’s possible to believe in #ResponsibleAI and be optimistic about the potential of language models. I currently wouldn’t trust a language model to make decisions that determine the fates of humans, but I’m excited by cute apps that enhance my developer productivity.
I’m glad people are speaking up sooner rather than later about the consequences of models exhibiting bias at scale, and I hope more people continue to work on solutions to these problems.

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More from @sh_reya

Shreya Shankar Profile picture
Apr 24
⭐new MLOps preprint⭐

RAG is everywhere, but building RAG is still painful. When something breaks--the retriever? the LLM?--developers are left guessing, & iterating is often slow

we built a better way & used it as a design probe to study expert workflows 👇 Image
Meet raggy: an interactive debugging interface for RAG pipelines. It pairs a Python library of RAG primitives with a UI that lets devs inspect, edit, & rerun steps in real time. raggy precomputes many indexes for retrieval upfront, so you can easily swap them out when debugging! Image
Then, to learn more about expert workflows, we ran a study with 12 engineers who’ve built production RAG pipelines. We simulated a question-answering application from a hospital & watched our participants use raggy to build and iterate on their pipelines. The paper reports a bunch of qualitative findings, including:

🔍 They always debug retrieval first
⚙️ Fixes to one step often break another
⚡ Fast iteration was key: raggy turned half-day experiments into seconds!!
Read 6 tweets
Shreya Shankar Profile picture
Jan 13
Introducing 📜DocWrangler: an open-source IDE for AI-powered data processing with built-in prompt engineering guidance and output inspection tools.

Code: github.com/ucbepic/docetl
Blog: data-people-group.github.io/blogs/2025/01/…
Free research preview: docetl.org/playground

Built @ Berkeley (1/7) Image
(2/7) Following the release of DocETL (our data processing framework), we observed users struggling to articulate what they want & changing their preferences based on what the LLM could or couldn't do well. The main challenge is that no one knows what outputs they want until they see it; that is, agentic workflows are inherently iterative.
(3/7) This release of DocWrangler has 3 main features. Key feature 1: spreadsheet interface with automatic summary overlays Image
Read 7 tweets
Shreya Shankar Profile picture
Dec 29, 2024
how come nobody is talking about how much shittier eng on-calls are thanks to blind integrations of AI-generated code? LLMs are great coders but horrible engineers. no, the solution is not “prompt the LLM to write more documentation and tests” (cont.)
i will take react development as an example. I use cursor but I think the problems are not specific to cursor. Every time I ask for a new feature to be added to my codebase, it almost always uses at least 1 too many state variables. When the code is not correct (determined by my interaction with the react app), and I prompt the LLM with the bug + to fix it, it will almost always add complexity rather than rewrite parts of what it already had
so the burden is on me to exhaustively test the generated app via my interactions, and then reverse engineer the mental model of what the code should be, and then eyeball the generated code to make sure this matches my model. This is so horrible to do for multi-file edits or > 800 lines of generated code (which is super common for web dev diffs)
Read 11 tweets
Shreya Shankar Profile picture
Nov 4, 2024
what makes LLM frameworks feel unusable is that there's still so much burden for the user to figure out the bespoke amalgamation of LLM calls to ensure end-to-end accuracy. in , we've found that relying on an agent to do this requires lots of scaffolding docetl.orgImage
first there needs to be a way of getting theoretically valid task decompositions. simply asking an LLM to break down a complex task over lots of data may result in a logically incorrect plan. for example, the LLM might choose the wrong data operation (projection instead of aggregation), and this would be a different pipeline entirely.
to solve this problem, DocETL uses hand-defined rewrite directives that can enumerate theoretically-equivalent decompositions/pipeline rewrites. the agent is then limited to creating prompts/output schemas for newly synthesized operations, according to the rewrite rules, which bounds its errors.
Read 5 tweets
Shreya Shankar Profile picture
Oct 31, 2024
I have a lot of thoughts on this as someone who has manually combed through hundreds of humans' prompt deltas
first, humans tend to underspecify the first version of their prompt. if they're in the right environment where they can get a near-instantaneous LLM response in the same interface (e.g., chatgpt, Claude, openai playground), they just want to see what the llm can do
there's a lot of literature on LLM sensemaking from the HCI community here (our own "who validates the validators" paper is one of many), but I still think LLM sensemaking is woefully unexplored, especially with respect to the stage in the mlops lifecycle
Read 9 tweets
Shreya Shankar Profile picture
Oct 21, 2024
Our (first) DocETL preprint is now on Arxiv! "DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing" It has been almost 2 years in the making, so I am very happy we hit this milestone :-) arxiv.org/abs/2410.12189Image
DocETL is a framework for LLM-powered unstructured data processing and analysis. The big new idea in this paper is to automatically rewrite user-specified pipelines into a sequence of finer-grained and more accurate operators.
I'll mention two big contributions in this paper. First, we present a rich suite of operators, with three entirely new operators to deal with decomposing complex documents: the split, gather, and resolve operators.
Read 12 tweets

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