3 citations · 11 across the 6 of their papers we have counts for
6 papers
Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark
Jason Hoelscher-Obermaier, Julia Persson, Esben Kran +2
Recent model editing techniques promise to mitigate the problem of memorizing false or outdated associations during LLM training. However, we show that these techniques can introdu…
Neuron to Graph: Interpreting Language Model Neurons at Scale
Alex Foote, Neel Nanda, Esben Kran +3
Advances in Large Language Models (LLMs) have led to remarkable capabilities, yet their inner mechanisms remain largely unknown. To understand these models, we need to unravel the…
The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering
Sabrina Chiesurin, Dimitris Dimakopoulos, Marco Antonio Sobrevilla Cabezudo +4
Large language models are known to produce output which sounds fluent and convincing, but is also often wrong, e.g. "unfaithful" with respect to a rationale as retrieved from a kno…
The Larger They Are, the Harder They Fail: Language Models do not Recognize Identifier Swaps in Python
Antonio Valerio Miceli-Barone, Fazl Barez, Ioannis Konstas +1
Large Language Models (LLMs) have successfully been applied to code generation tasks, raising the question of how well these models understand programming. Typical programming lang…
iLab at SemEval-2023 Task 11 Le-Wi-Di: Modelling Disagreement or Modelling Perspectives?
Nikolas Vitsakis, Amit Parekh, Tanvi Dinkar +3
There are two competing approaches for modelling annotator disagreement: distributional soft-labelling approaches (which aim to capture the level of disagreement) or modelling pers…
A Theme-Rewriting Approach for Generating Algebra Word Problems
Rik Koncel-Kedziorski, Ioannis Konstas, Luke Zettlemoyer +1
Texts present coherent stories that have a particular theme or overall setting, for example science fiction or western. In this paper, we present a text generation method called {\…