6 citations · 12 across the 14 of their papers we have counts for
20 papers · 1 filter
Weird Generalization is Weirdly Brittle
Miriam Wanner, Hannah Collison, William Jurayj +3
Weird generalization is a phenomenon in which models fine-tuned on data from a narrow domain (e.g. insecure code) develop surprising traits that manifest even outside that domain (…
Knowing But Not Doing: Convergent Morality and Divergent Action in LLMs
Jen-tse Huang, Jiantong Qin, Xueli Qiu +3
Value alignment is central to the development of safe and socially compatible artificial intelligence. However, how Large Language Models (LLMs) represent and enact human values in…
Demo: Statistically Significant Results On Biases and Errors of LLMs Do Not Guarantee Generalizable Results
Jonathan Liu, Haoling Qiu, Jonathan Lasko +3
Recent research has shown that hallucinations, omissions, and biases are prevalent in everyday use-cases of LLMs. However, chatbots used in medical contexts must provide consistent…
Evaluating the Evaluators: Are readability metrics good measures of readability?
Isabel Cachola, Daniel Khashabi, Mark Dredze
Plain Language Summarization (PLS) aims to distill complex documents into accessible summaries for non-expert audiences. In this paper, we conduct a thorough survey of PLS literatu…
Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflict
Kaiser Sun, Fan Bai, Mark Dredze
Large language models (LLMs) draw on both contextual information and parametric memory, yet these sources can conflict. Prior studies have largely examined this issue in contextual…
LLMs are Better Than You Think: Label-Guided In-Context Learning for Named Entity Recognition
Fan Bai, Hamid Hassanzadeh, Ardavan Saeedi +1
In-context learning (ICL) enables large language models (LLMs) to perform new tasks using only a few demonstrations. However, in Named Entity Recognition (NER), existing ICL method…