← Search

Quentin Brabant

3 accepted papers

2025

Factual Knowledge Assessment of Language Models Using Distractors

COLING 2025main

Language models encode extensive factual knowledge within their parameters. The accurate assessment of this knowledge is crucial for understanding and improving these models. In the literature, factual knowledge assessment often relies on cloze sentences, which can lead to erroneous conclusions due…

2024

KGConv, a Conversational Corpus Grounded in Wikidata

COLING 2024main

We present KGConv, a large corpus of 71k English conversations where each question-answer pair is grounded in a Wikidata fact. Conversations contain on average 8.6 questions and for each Wikidata fact, we provide multiple variants (12 on average) of the corresponding question using templates, human…

Cited by 1SourcePDFScholar
2024

WikiFactDiff: A Large, Realistic, and Temporally Adaptable Dataset for Atomic Factual Knowledge Update in Causal Language Models

COLING 2024main

The factuality of large language model (LLMs) tends to decay over time since events posterior to their training are “unknown” to them. One way to keep models up-to-date could be factual update: the task of inserting, replacing, or removing certain simple (atomic) facts within the model. To study thi…