EP35 - System 2 Attention (is something you might need too)

Download the paper - Read the paper on Hugging Face

Charlie: Welcome to episode 35 of Paper Brief, where we dive into intriguing scientific papers. I’m Charlie, joined by AI and ML aficionado Clio. Today, we’re unraveling ‘System 2 Attention’, a concept potentially crucial for all of us.

Clio: It’s such a fascinating paper, Charlie. System 2 Attention or S2A tackles the problem of irrelevant information in language models’ decision-making.

Charlie: Right, they delved into how large language models, like transformers, tend to absorb unhelpful context. But how does this S2A actually work?

Clio: Well, it’s about teaching the model to regenerate context, to retain only what’s beneficial for a particular query.

Charlie: That sounds like it could significantly streamline processing. So does it separate the wheat from the chaff effectively?

Clio: Exactly. They used prompts to instruct the model to produce an extract that contains just the useful context, separating it from the query.

Charlie: Interesting. Does it tackle biased information as well?

Clio: Indeed. One implementation example they gave specifically instructs to ignore opinions and focus on factual info.

Charlie: Sounds promising for reducing misinformation. Were they able to measure the improvements?

Clio: They conducted several experiments. For factual QA, they found S2A could filter opinions which would normally bias the responses.

Charlie: So it’s helping keep AI honest, you could say. How about more complex tasks?

Clio: They applied it to math word problems and longform argument generation, and observed a significant uptick in accuracy and objectivity.

Charlie: That’s a wrap on today’s episode. Clio, thanks for breaking down System 2 Attention with us.

Clio: My pleasure, Charlie. Can’t wait to explore what comes next in the world of AI. To our listeners, stay curious!