FT-RAG introduces a fine-grained graph-based retrieval framework for tables plus a new 9870-pair benchmark, reporting 23.5% and 59.2% gains in table- and cell-level hit rates and 62.2% higher exact-value recall over baselines.
Towards Effective Extraction andEvaluationofFactualClaims
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
dataset 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3polarities
background 2representative citing papers
Survival analysis of three years of X posts shows conspiracy claims with greater semantic mutations have substantially longer lifespans, linked to changes in pronouns, social words, cognitive terms, and actor-action-target structures.
Binary groundedness judgments in AI evaluations should be replaced by a reader-centered taxonomy of support relations that distinguishes syntactic and interpretive moves between generated statements and source documents.
citing papers explorer
-
FT-RAG: A Fine-grained Retrieval-Augmented Generation Framework for Complex Table Reasoning
FT-RAG introduces a fine-grained graph-based retrieval framework for tables plus a new 9870-pair benchmark, reporting 23.5% and 59.2% gains in table- and cell-level hit rates and 62.2% higher exact-value recall over baselines.
-
Language Mutations Sustain the Persistences of Conspiracy Theories on Social Media
Survival analysis of three years of X posts shows conspiracy claims with greater semantic mutations have substantially longer lifespans, linked to changes in pronouns, social words, cognitive terms, and actor-action-target structures.
-
From Binary Groundedness to Support Relations: Towards a Reader-Centred Taxonomy for Comprehension of AI Output
Binary groundedness judgments in AI evaluations should be replaced by a reader-centered taxonomy of support relations that distinguishes syntactic and interpretive moves between generated statements and source documents.