2.9k citations · 12k across the 144 of their papers we have counts for
7 papers · 1 filter
How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions
Lorenzo Pacchiardi, Alex J. Chan, Sören Mindermann +5
Large language models (LLMs) can "lie", which we define as outputting false statements despite "knowing" the truth in a demonstrable sense. LLMs might "lie", for example, when inst…
In-Context Learning Learns Label Relationships but Is Not Conventional Learning
Jannik Kossen, Yarin Gal, Tom Rainforth
The predictions of Large Language Models (LLMs) on downstream tasks often improve significantly when including examples of the input--label relationship in the context. However, th…
Revisiting Automated Prompting: Are We Actually Doing Better?
Yulin Zhou, Yiren Zhao, Ilia Shumailov +2
Current literature demonstrates that Large Language Models (LLMs) are great few-shot learners, and prompting significantly increases their performance on a range of downstream task…
Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation
Lorenz Kuhn, Yarin Gal, Sebastian Farquhar
We introduce a method to measure uncertainty in large language models. For tasks like question answering, it is essential to know when we can trust the natural language outputs of…
CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models
Lorenz Kuhn, Yarin Gal, Sebastian Farquhar
Users often ask dialogue systems ambiguous questions that require clarification. We show that current language models rarely ask users to clarify ambiguous questions and instead pr…
Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers
Tim Z. Xiao, Aidan N. Gomez, Yarin Gal
We detect out-of-training-distribution sentences in Neural Machine Translation using the Bayesian Deep Learning equivalent of Transformer models. For this we develop a new measure…
Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature
Yarin Gal
Over the past 50 years many have debated what representation should be used to capture the meaning of natural language utterances. Recently new needs of such representations have b…