Learned Readout Tokens for Extracting Large Language Model Confidence
We show that our learned readout approach ScratchProbe outperforms its existing-token counterpart on a variety of datasets and LLMs. We also extend our method to a multi-token approach which we show provides further performance improvements.
ProofPaper ↗
Key points
- Large language models (LLMs) are increasingly used within everyday applications where their responses have significant downstream impacts.
- A popular approach for estimating LLM confidence is to train probes on the hidden states of the LLM's tokens to predict the correctness of the model's output.
- These probes typically read hidden states from tokens that are already in the model's vocabulary.
- In this work, we introduce a new approach to confidence probes that learn a new readout token and utilize its hidden states to train the probe.
Sources (1)
- [1]Learned Readout Tokens for Extracting Large Language Model ConfidencearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 03:30 AM
We show that our learned readout approach ScratchProbe outperforms its existing-token counterpart on a variety of datasets and LLMs. We also extend our method to a multi-token approach which we show provides further performance improvements.
Large language models (LLMs) are increasingly used within everyday applications where their responses have significant downstream impacts.
Extractive summary: sentences quoted from the sources.
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