Researchers retrofit LLMs to count letters using byteification
Minixhofer et al. describe a method called byteification that modifies existing token‑based large language models so they can operate on raw bytes instead of word‑level tokens. By exposing the underlying byte sequences, the retrofitted models can read individual characters, allowing them to answer questions such as how many times the letter ‘i’ appears in the phrase “artificial intelligence”.
Key points
- Byteification retrofits token‑based LLMs to operate at the byte level.
- Byteified models can count individual characters, e.g., the letter ‘i’ in “artificial intelligence”.
- Performance stays competitive with standard token‑based LLMs.
The authors evaluate the byteified models on standard benchmarks and report that performance remains competitive with the original token‑based versions, despite the added ability to perform character‑level analysis. This approach shows that token‑only architectures are not fundamentally limited to coarse‑grained text representations and can be extended to finer granularity without sacrificing overall capability.
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