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Found 7 papers, 5 papers with code
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?
We find evidence that LLMs, with and without instruction-tuning, exhibit human-like biases in both the text-comprehension and the solution-planning steps of the solving process, but not during the final step which relies on the problem's arithmetic expressions (solution execution).
An Exploration of Left-Corner Transformations
The left-corner transformation (Rosenkrantz and Lewis, 1970) is used to remove left recursion from context-free grammars, which is an important step towards making the grammar parsable top-down with simple techniques.
Efficient Semiring-Weighted Earley Parsing
This paper provides a reference description, in the form of a deduction system, of Earley's (1970) context-free parsing algorithm with various speed-ups.
World Models for Math Story Problems
In this paper, we consolidate previous work on categorizing and representing math story problems and develop MathWorld, which is a graph-based semantic formalism specific for the domain of math story problems.
On the Intersection of Context-Free and Regular Languages
It shows, by a simple construction, that the intersection of a context-free language and a regular language is itself context-free.
Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang
We analyze the semantic change and frequency shift of slang words and compare them to those of standard, nonslang words.
Recovering Barabási-Albert Parameters of Graphs through Disentanglement
We first learn the generative BA parameters in a supervised fashion using a Graph Neural Network (GNN) and a Random Forest Regressor, by minimizing the squared loss between the true generative parameters and the latent variables.
