6 papers
Multi-LLM Collaborative Alignment via Stackelberg Games
Christina Hahn, Shangbin Feng, Dean Light +3
A pool of language models can collaborate and improve collectively by learning from one another's responses. These interactions depend on the instructions used during training. Exi…
Realize What Matters: Principled Context Representation for Large-Scale Reasoning
Michael Theologitis, Dean Light, Shuyue Stella Li +3
Solving complex tasks in domains such as science, medicine, law, and finance often requires assembling interdependent information scattered across vast, heterogeneous sources far b…
Incorporating Deep Learning Design in Database Queries
Yuval Lev Lubarsky, Dean Light, Boaz Berger +2
Deep learning over relational databases is conventionally realized by translating data into graph representations and applying graph-based neural networks within external framework…
Deep Reasoning in General Purpose Agents via Structured Meta-Cognition
Dean Light, Michael Theologitis, Kshitish Ghate +7
Humans intuitively solve complex problems by flexibly shifting among reasoning modes: they plan, execute, revise intermediate goals, resolve ambiguity through associative judgment,…
Cognitive Foundations for Reasoning and Their Manifestation in LLMs
Priyanka Kargupta, Shuyue Stella Li, Haocheng Wang +9
Large language models (LLMs) solve complex problems yet fail on simpler variants, suggesting they achieve correct outputs through mechanisms fundamentally different from human reas…
SpannerLib: Embedding Declarative Information Extraction in an Imperative Workflow
Dean Light, Ahmad Aiashy, Mahmoud Diab +3
Document spanners have been proposed as a formal framework for declarative Information Extraction (IE) from text, following IE products from the industry and academia. Over the pas…