7 papers
Adapting LLMs for Minimal-edit Grammatical Error Correction
Ryszard Staruch, Filip Graliński, Daniel Dzienisiewicz
Decoder-only large language models have shown superior performance in the fluency-edit English Grammatical Error Correction, but their adaptation for minimal-edit English GEC is st…
CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation
Youngwon Lee, Seung-won Hwang, Daniel Campos +3
With the adoption of retrieval-augmented generation (RAG), large language models (LLMs) are expected to ground their generation to the retrieved contexts. Yet, this is hindered by…
Inference Scaling for Bridging Retrieval and Augmented Generation
Youngwon Lee, Seung-won Hwang, Daniel Campos +3
Retrieval-augmented generation (RAG) has emerged as a popular approach to steering the output of a large language model (LLM) by incorporating retrieved contexts as inputs. However…
Tackling prediction tasks in relational databases with LLMs
Marek Wydmuch, Łukasz Borchmann, Filip Graliński
Though large language models (LLMs) have demonstrated exceptional performance across numerous problems, their application to predictive tasks in relational databases remains largel…
POLygraph: Polish Fake News Dataset
Daniel Dzienisiewicz, Filip Graliński, Piotr Jabłoński +3
This paper presents the POLygraph dataset, a unique resource for fake news detection in Polish. The dataset, created by an interdisciplinary team, is composed of two parts: the "fa…
Two Approaches to Diachronic Normalization of Polish Texts
Kacper Dudzic, Filip Graliński, Krzysztof Jassem +2
This paper discusses two approaches to the diachronic normalization of Polish texts: a rule-based solution that relies on a set of handcrafted patterns, and a neural normalization…