Data scarcity is a prevalent challenge in the era of Large Language Models (LLMs). The insatiable hunger of LLMs for large corpora becomes even more pronounced when dealing with non-English and low-resource languages. The issue is particularly exacerbated in Semantic Parsing (SP), i.e. the task of...
04b Atto di convegno in volume
-
-
Entity Linking (EL) and Relation Extraction (RE) are fundamental tasks in Natural Language Processing, serving as critical components in a wide range of applications. In this paper, we propose ReLiK, a Retriever-Reader architecture for both EL and RE, where, given an input text, the Retriever...
-
Several Natural Language Understanding (NLU) tasks focus on linking text to explicit knowledge, including Word Sense Disambiguation, Semantic Role Labeling, Semantic Parsing, and Relation Extraction. In addition to the importance of connecting raw text with explicit knowledge bases, the integration...
-
Non-markovian Reinforcement Learning (RL) tasks are very hard to solve, because agents must consider the entire history of state-action pairs to act rationally in the environment. Most works use symbolic formalisms (as Linear Temporal Logic or automata) to specify the temporally-extended task....
-
Large Language Models (LLMs) and Visual Language Models (VLMs) are attracting increasing interest due to their improving performance and applications across various domains and tasks. However, LLMs and VLMs can produce erroneous results, especially when a deep understanding of the problem domain is...
-
Task planning for robots in real-life settings presents significant challenges. These challenges stem from three primary issues: the difficulty in identifying grounded sequences of steps to achieve a goal; the lack of a standardized mapping between high-level actions and low-level commands; and the...
-
The majority of Neural Semantic Parsing (NSP) models are developed with the assumption that there are no concepts outside the ones such models can represent with their target symbols (closed-world assumption). This assumption leads to generate hallucinated outputs rather than admitting their lack...
-
In recent years, the dominance of Large Language Models (LLMs) in the English language has become evident. However, there remains a pronounced gap in resources and evaluation tools tailored for non-English languages, underscoring a significant disparity in the global AI landscape. This paper seeks...
-
Annually, at the Conference of Machine Translation (WMT), the Metrics Shared Task organizers conduct the Machine Translation (MT) meta-evaluation, assessing MT metrics' capabilities according to their correlation with human judgments. Their results guide researchers toward enhancing the next...
-
Annually, at the Conference of Machine Translation (WMT), the Metrics Shared Task organizers conduct the Machine Translation (MT) meta-evaluation, assessing MT metrics' capabilities according to their correlation with human judgments. Their results guide researchers toward enhancing the next...