12 citations · 18 across the 6 of their papers we have counts for
6 papers
Language Models Prefer What They Know: Relative Confidence Estimation via Confidence Preferences
Vaishnavi Shrivastava, Ananya Kumar, Percy Liang
Language models (LMs) should provide reliable confidence estimates to help users detect mistakes in their outputs and defer to human experts when necessary. Asking a language model…
Llamas Know What GPTs Don't Show: Surrogate Models for Confidence Estimation
Vaishnavi Shrivastava, Percy Liang, Ananya Kumar
To maintain user trust, large language models (LLMs) should signal low confidence on examples where they are incorrect, instead of misleading the user. The standard approach of est…
Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs
Shashank Gupta, Vaishnavi Shrivastava, Ameet Deshpande +4
Recent works have showcased the ability of LLMs to embody diverse personas in their responses, exemplified by prompts like 'You are Yoda. Explain the Theory of Relativity.' While t…
Benchmarking and Improving Generator-Validator Consistency of Language Models
Xiang Lisa Li, Vaishnavi Shrivastava, Siyan Li +2
As of September 2023, ChatGPT correctly answers "what is 7+8" with 15, but when asked "7+8=15, True or False" it responds with "False". This inconsistency between generating and va…
Exploring Low-Cost Transformer Model Compression for Large-Scale Commercial Reply Suggestions
Vaishnavi Shrivastava, Radhika Gaonkar, Shashank Gupta +1
Fine-tuning pre-trained language models improves the quality of commercial reply suggestion systems, but at the cost of unsustainable training times. Popular training time reductio…
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis
Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi +3
Global models are trained to be as generalizable as possible, with user invariance considered desirable since the models are shared across multitudes of users. As such, these model…