# The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators

Authors: Tzu-Heng Huang, Catherine Cao, Vaishnavi Bhargava, Frederic Sala.

Asks a language model to write labeling programs that can be saved and run locally.

ArXiv initial submission: 2024-06-25.

[Original paper](https://arxiv.org/abs/2407.11004) · [Reading-list entry](https://programasweights.com/reading-list/papers/2407.11004/)
[Preprint BibTeX](https://programasweights.com/reading-list/papers/2407.11004/citation.bib) · [Preprint CSL-JSON](https://programasweights.com/reading-list/papers/2407.11004/citation.csl.json) · [JSON record](https://programasweights.com/reading-list/papers/2407.11004/record.json)

**Contribution:** Reuses generated code across many labeling decisions and combines noisy program outputs with weak supervision.

**Program form:** Symbolic labeling programs.

**Conditioning:** A task description, labeling instructions, and a function signature.

**Execution:** Generated programs label examples locally; their outputs can be combined to train a downstream model.

**Relationship to PAW:** PAW evaluates an ALCHEmist-style approach. Both build reusable tools: ALCHEmist produces symbolic code, while PAW produces programs for a neural interpreter.

**Limitations:** Label quality depends on the model’s ability to write useful programs. Complex tasks can produce code that is harder to inspect.

[Discussion in the paper](https://arxiv.org/html/2607.02512v1#S10)

Related terms: ALCHEmist, program synthesis, automated labeling, reusable tools.

## Method comparisons

**Code and neural programs**

ALCHEmist generates symbolic labeling programs. PAW compiles fuzzy functions into text and learned parameters. Both build an artifact once and reuse it; one runs as code, the other on a neural interpreter.

Sources: [ALCHEmist paper](https://proceedings.neurips.cc/paper_files/paper/2024/hash/72802bef5cf1a3449e909b20c2ae18d5-Abstract-Conference.html) · [PAW evaluation](https://arxiv.org/html/2607.02512v1#S6)

Paper details: [The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators](https://programasweights.com/reading-list/papers/2407.11004/) · [Program-as-Weights: A Programming Paradigm for Fuzzy Functions](https://programasweights.com/reading-list/papers/2607.02512/)


## Published version

The ALCHEmist: Automated Labeling 500x CHEaper than LLM Data Annotators. Tzu-Heng Huang, Catherine Cao, Vaishnavi Bhargava, Frederic Sala. Advances in Neural Information Processing Systems, 2024.
[Publication](https://proceedings.neurips.cc/paper_files/paper/2024/hash/72802bef5cf1a3449e909b20c2ae18d5-Abstract-Conference.html) · [BibTeX](https://programasweights.com/reading-list/papers/2407.11004/publication.bib) · [CSL-JSON](https://programasweights.com/reading-list/papers/2407.11004/publication.csl.json)
[Publication record](https://proceedings.neurips.cc/paper_files/paper/2024/hash/72802bef5cf1a3449e909b20c2ae18d5-Abstract-Conference.html).

## Approaches

- [Programs and reusable artifacts](https://programasweights.com/reading-list/guides/fuzzy-functions/): Learn programs that can be saved and run on new inputs.

Reading-list version 1.2.0; updated 2026-10-19.