HyperTuning via Gisting: Cheap Adapter Generation, Mixed Results

Investigating the Effectiveness of HyperTuning via Gisting · Jason Phang (NYU, EleutherAI) · 2024
Author's summary: "economical and easy to implement, but mixed empirical performance"
arXiv:2402.16817

Three ways to adapt a frozen LLM

Where does the task live, and who pays for it? Click a lane.

Examples re-read vs compressed Illustrative arithmetic

Counts attended example tokens only, not latency or memory. Example length fixed at 1024 (the hypernetwork's max example input); the Gist prefix is 16 tokens.

Gist attention mask

Rows = query token, columns = key token it may attend to. Hover a cell.Illustrative: 6 example, 4 Gist, 3 input tokens (paper uses 16 Gists)

Hover a cell.

Gist K/V states = a soft prefix

Read out the Gist tokens' key and value states at every layer and prepend them to a frozen copy of the model.

HyperLlama: one model, two roles

Single Llama-2-7B, 4-bit quantized (QLoRA). Flip the LoRA adapter.

Position offset fix (Appendix A)

Gists computed near the end of the examples (e.g. position ~800) need a matching shift downstream.

Training stages

Step through the recipe.

Reported results

Numbers as reported in the episode from the paper. No seeds or confidence intervals are reported.

What was and wasn't measured

Hover cells for detail.

The capacity question Illustrative

Only one Gist length (16) and one model size were evaluated. Everything else is unknown.

References