TL;QR: Querying Temporal Discursive Patterns in Textual Corpora

Key points

• Many questions about large textual corpora concern patterns that unfold over time, such as a political group whose stance on a topic drifts from support to criticism over several years of debates.1
• In TL;QR, a single query combines natural-language predicates, whose truth is judged by a Large Language Model (LLM), with regular expressions and the operators of a temporal logic.1
• On a suite of controlled synthetic benchmarks with temporally and semantically complex patterns, TL;QR is substantially more accurate than LLM-only baselines.1
• In a case study on ten years of European Parliament debates (442,593 multilingual speeches), TL;QR identifies candidate shifts in the political groups' stances on the GDPR privacy regulation.1

Sources (1)

• [1]TL;QR: Querying Temporal Discursive Patterns in Textual Corpora
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 11, 03:52 PM

  We present TL;QR (Too Long; Query with logical temporal Reasoning), a declarative query language and open-source engine for finding such temporal discursive patterns.

  Many questions about large textual corpora concern patterns that unfold over time, such as a political group whose stance on a topic drifts from support to criticism over several years of debates.

Extractive summary: sentences quoted from the sources.