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Found 11 papers, 7 papers with code
A Probabilistic Generative Grammar for Semantic Parsing
The work relies on a novel application of hierarchical Dirichlet processes (HDPs) for structured prediction, which we also present in this manuscript.
Jelly Bean World: A Testbed for Never-Ending Learning
Never-ending learning is a machine learning paradigm that aims to bridge this gap, with the goal of encouraging researchers to design machine learning systems that can learn to perform a wider variety of inter-related tasks in more complex environments.
Towards General Natural Language Understanding with Probabilistic Worldbuilding
We derive and implement an inference algorithm that reads sentences by parsing and abducing updates to its latent world model that capture the semantics of those sentences, and evaluate it on two out-of-domain question-answering datasets: (1) ProofWriter and (2) a new dataset we call FictionalGeoQA, designed to be more representative of real language but still simple enough to focus on evaluating reasoning ability, while being robust against heuristics.
Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought
Large language models (LLMs) have shown remarkable reasoning capabilities given chain-of-thought prompts (examples with intermediate reasoning steps).
Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples
Given the intractably large size of the space of proofs, any model that is capable of general deductive reasoning must generalize to proofs of greater complexity.
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.
Retrieval-Augmented Chain-of-Thought in Semi-structured Domains
Applying existing question answering (QA) systems to specialized domains like law and finance presents challenges that necessitate domain expertise.
Personas as a Way to Model Truthfulness in Language Models
This allows the model to separate truth from falsehoods and controls the truthfulness of its generation.
Noisy Exemplars Make Large Language Models More Robust: A Domain-Agnostic Behavioral Analysis
Recent advances in prompt engineering enable large language models (LLMs) to solve multi-hop logical reasoning problems with impressive accuracy.
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).
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs).
