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Found 4 papers, 2 papers with code
Understanding the Reasoning Ability of Language Models From the Perspective of Reasoning Paths Aggregation
To understand how pre-training with a next-token prediction objective contributes to the emergence of such reasoning capability, we propose that we can view an LM as deriving new conclusions by aggregating indirect reasoning paths seen at pre-training time.
Knowledge of Knowledge: Exploring Known-Unknowns Uncertainty with Large Language Models
This paper investigates the capabilities of Large Language Models (LLMs) in the context of understanding their own knowledge and measuring their uncertainty.
Neural Methods for Logical Reasoning Over Knowledge Graphs
We introduce a set of models that use Neural Networks to create one-point vector embeddings to answer the queries.
The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective
For example, in multi-stage settings where decisions are made in multiple screening rounds, we use our framework to derive the minimal distributions required to design a fair algorithm.
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