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Found 5 papers, 1 papers with code
Llamas Know What GPTs Don't Show: Surrogate Models for Confidence Estimation
To maintain user trust, large language models (LLMs) should signal low confidence on examples where they are incorrect, instead of misleading the user.
Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs
Our experiments with ChatGPT-3. 5 show that this bias is ubiquitous - 80% of our personas demonstrate bias; it is significant - some datasets show performance drops of 70%+; and can be especially harmful for certain groups - some personas suffer statistically significant drops on 80%+ of the datasets.
Benchmarking and Improving Generator-Validator Consistency of Language Models
To improve the consistency of LMs, we propose to finetune on the filtered generator and validator responses that are GV-consistent, and call this approach consistency fine-tuning.
Exploring Low-Cost Transformer Model Compression for Large-Scale Commercial Reply Suggestions
Fine-tuning pre-trained language models improves the quality of commercial reply suggestion systems, but at the cost of unsustainable training times.
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis
As such, these models are often unable to produce personalized responses for individual users, based on their data.
