Large Language Models as Assessors: On the Impact of Relevance Scales
Poster - The 48th European Conference on Information Retrieval (ECIR 2026). Delft, The Netherlands.
Poster - The 48th European Conference on Information Retrieval (ECIR 2026). Delft, The Netherlands.
Traditionally, relevance judgments have relied on human annotators, but recent advances in Large Language Models (LLMs) have prompted growing interest in their use as a proxy for …
Crowdsourcing tasks have been widely used to collect a large number of human labels at scale. While some of these tasks are deployed by requesters and performed only once by crowd …
There is an important ongoing effort aimed to tackle misinformation and to perform reliable fact-checking by employing human assessors at scale, with a crowdsourcing-based …
The increase of the amount of misinformation spread every day online is a huge threat to the society. Organizations and researchers are working to contrast this misinformation …
To scale the size of Information Retrieval collections, crowdsourcing has become a common way to collect relevance judgments at scale. Crowdsourcing experiments usually employ …
Automated fact-checking (AFC) systems exist to combat disinformation, however their complexity usually makes them opaque to the end user, making it difficult to foster trust in the …
Automated fact-checking (AFC) systems exist to combat disinformation, however their complexity makes them opaque to the end user, making it difficult to foster trust. In this …
Recently, the misinformation problem has been addressed with a crowdsourcing-based approach: to assess the truthfulness of a statement, instead of relying on a few experts, a crowd …
Misinformation is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large. Very recently, the problem has …