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20162023
most citedSupporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Coding

224 citations · 1.1k across the 33 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.CL2023★ 5 cited

Joint Prompt Optimization of Stacked LLMs using Variational Inference

Alessandro Sordoni, Xingdi Yuan, Marc-Alexandre Côté +6

Large language models (LLMs) can be seen as atomic units of computation mapping sequences to a distribution over sequences. Thus, they can be seen as stochastic language layers in…

cs.AI2023★ 4 cited

Augmenting Autotelic Agents with Large Language Models

Cédric Colas, Laetitia Teodorescu, Pierre-Yves Oudeyer +2

Humans learn to master open-ended repertoires of skills by imagining and practicing their own goals. This autotelic learning process, literally the pursuit of self-generated (auto)…

cs.CL2023

It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance

Arjun Subramonian, Xingdi Yuan, Hal Daumé +1

Progress in NLP is increasingly measured through benchmarks; hence, contextualizing progress requires understanding when and why practitioners may disagree about the validity of be…

cs.CL2023

ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games

Ruoyao Wang, Graham Todd, Eric Yuan +3

In this work, we investigate the capacity of language models to generate explicit, interpretable, and interactive world models of scientific and common-sense reasoning tasks. We op…

cs.CL2023★ 224 cited

Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Coding

Ziang Xiao, Xingdi Yuan, Q. Vera Liao +2

Qualitative analysis of textual contents unpacks rich and valuable information by assigning labels to the data. However, this process is often labor-intensive, particularly when wo…