Research
Paper breakdowns
A breakdown is our technical reading of one influential paper: what problem it tackled, how the method works, what the authors report and where the evidence stops.
Numbers are the authors’ own reported results, quoted from the paper and checked against the source. We don’t re-run the experiments, and we say so.
Every breakdown covers
- TL;DR
- The problem
- The method
- What the paper reports
- Why it mattered
- Limitations
- Reading notes
Tagged “Prompting”
2 breakdowns of 6
Chain-of-Thought Prompting: eliciting step-by-step reasoning with a few examples
A technical reading of the 2022 paper showing that few-shot examples with written-out reasoning steps improve large language models on arithmetic, commonsense and symbolic tasks — and why the effect appeared only at large scale.
- Prompting
- Reasoning
- Large language models
- Paper authors
- Wei et al.
- Paper year
- 2022
- arXiv identifier
- arXiv 2201.11903
ReAct: interleaving reasoning and actions in language models
A technical reading of the 2022 paper that had language models alternate between written reasoning and tool actions — how the thought–action–observation loop works, what it improved on question answering and interactive tasks, and where it failed.
- Agents
- Reasoning
- Prompting
- Paper authors
- Yao et al.
- Paper year
- 2022
- arXiv identifier
- arXiv 2210.03629
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