7 citations · 9 across the 6 of their papers we have counts for
5 papers · 1 filter
Sycophancy Towards Researchers Drives Performative Misalignment
David D. Baek, Xinnuo Li, Anay Gupta +4
The increasing situational awareness of language models raises safety concerns: models might be aware when they are evaluated, and adjust their behavior to evade monitoring and res…
Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer?
Nishant Balepur, Feng Gu, Abhilasha Ravichander +3
Question answering (QA), giving correct answers to questions, is a popular task, but we test reverse question answering (RQA): for an input answer, give a question with that answer…
A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick
Nishant Balepur, Matthew Shu, Alexander Hoyle +4
Keyword mnemonics are memorable explanations that link new terms to simpler keywords. Prior work generates mnemonics for students, but they do not train models using mnemonics stud…
KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students
Matthew Shu, Nishant Balepur, Shi Feng +1
Flashcard schedulers rely on 1) student models to predict the flashcards a student knows; and 2) teaching policies to pick which cards to show next via these predictions. Prior stu…
Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong
Chenglei Si, Navita Goyal, Sherry Tongshuang Wu +4
Large Language Models (LLMs) are increasingly used for accessing information on the web. Their truthfulness and factuality are thus of great interest. To help users make the right…