Publications - Current Year

This article presents the QUASAR system for question answering over
unstructured text, structured tables, and knowledge graphs, with unified
treatment of all sources. The system adopts a RAG-based architecture, with a
pipeline of evidence retrieval followed by answer generation, with the latter
powered by a moderate-sized language model. Additionally and uniquely, QUASAR
has components for question understanding, to derive crisper input for evidence
retrieval, and for re-ranking and filtering the retrieved evidence before
feeding the most informative pieces into the answer generation. Experiments
with three different benchmarks demonstrate the high answering quality of our
approach, being on par with or better than large GPT models, while keeping the
computational cost and energy consumption orders of magnitude lower.

General-domain knowledge bases (KB), in particular the "big three" --
Wikidata, Yago and DBpedia -- are the backbone of many intelligent
applications. While these three have seen steady development, comprehensive KB
construction at large has seen few fresh attempts. In this work, we propose to
build a large general-domain KB entirely from a large language model (LLM). We
demonstrate the feasibility of large-scale KB construction from LLMs, while
highlighting specific challenges arising around entity recognition, entity and
property canonicalization, and taxonomy construction. As a prototype, we use
GPT-4o-mini to construct GPTKB, which contains 105 million triples for more
than 2.9 million entities, at a cost 100x less than previous KBC projects. Our
work is a landmark for two fields: For NLP, for the first time, it provides
\textit{constructive} insights into the knowledge (or beliefs) of LLMs. For the
Semantic Web, it shows novel ways forward for the long-standing challenge of
general-domain KB construction. GPTKB is accessible at gptkb.org.

Methods for relation extraction from text mostly focus on high precision, at
the cost of limited recall. High recall is crucial, though, to populate long
lists of object entities that stand in a specific relation with a given
subject. Cues for relevant objects can be spread across many passages in long
texts. This poses the challenge of extracting long lists from long texts. We
present the L3X method which tackles the problem in two stages: (1)
recall-oriented generation using a large language model (LLM) with judicious
techniques for retrieval augmentation, and (2) precision-oriented
scrutinization to validate or prune candidates. Our L3X method outperforms
LLM-only generations by a substantial margin.