activity
20132026
most citedProgrammatically Interpretable Reinforcement Learning

94 citations · 449 across the 53 of their papers we have counts for

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Showing cs.CLShow all

5 papers · 1 filter

cs.CL2023★ 6 cited

MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning

Zayne Sprague, Xi Ye, Kaj Bostrom +2

While large language models (LLMs) equipped with techniques like chain-of-thought prompting have demonstrated impressive capabilities, they still fall short in their ability to rea…

cs.CL2023

Deductive Additivity for Planning of Natural Language Proofs

Zayne Sprague, Kaj Bostrom, Swarat Chaudhuri +1

Current natural language systems designed for multi-step claim validation typically operate in two phases: retrieve a set of relevant premise statements using heuristics (planning)…

cs.CL2022★ 1 cited

Natural Language Deduction with Incomplete Information

Zayne Sprague, Kaj Bostrom, Swarat Chaudhuri +1

A growing body of work studies how to answer a question or verify a claim by generating a natural language "proof": a chain of deductive inferences yielding the answer based on a s…

cs.CL2022★ 1 cited

Natural Language Deduction through Search over Statement Compositions

Kaj Bostrom, Zayne Sprague, Swarat Chaudhuri +1

In settings from fact-checking to question answering, we frequently want to know whether a collection of evidence (premises) entails a hypothesis. Existing methods primarily focus…

cs.CL2021

Flexible Generation of Natural Language Deductions

Kaj Bostrom, Xinyu Zhao, Swarat Chaudhuri +1

An interpretable system for open-domain reasoning needs to express its reasoning process in a transparent form. Natural language is an attractive representation for this purpose --…