35 research outputs found
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Analysis of Language Change in Collaborative Instruction Following
We analyze language change over time in a collaborative, goal-oriented instructional task, where utility-maximizing participants form conventions and increase their expertise. Prior work studied such scenarios mostly in the context of reference games, and consistently found that language complexity is reduced along multiple dimensions, such as utterance length, as conventions are formed. In contrast, we find that, given the ability to increase instruction utility, instructors increase language complexity along these previously studied dimensions to better collaborate with increasingly skilled instruction followers
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling
Reinforcement learning (RL) agents typically learn tabula rasa, without prior
knowledge of the world. However, if initialized with knowledge of high-level
subgoals and transitions between subgoals, RL agents could utilize this
Abstract World Model (AWM) for planning and exploration. We propose using
few-shot large language models (LLMs) to hypothesize an AWM, that will be
verified through world experience, to improve sample efficiency of RL agents.
Our DECKARD agent applies LLM-guided exploration to item crafting in Minecraft
in two phases: (1) the Dream phase where the agent uses an LLM to decompose a
task into a sequence of subgoals, the hypothesized AWM; and (2) the Wake phase
where the agent learns a modular policy for each subgoal and verifies or
corrects the hypothesized AWM. Our method of hypothesizing an AWM with LLMs and
then verifying the AWM based on agent experience not only increases sample
efficiency over contemporary methods by an order of magnitude but is also
robust to and corrects errors in the LLM, successfully blending noisy
internet-scale information from LLMs with knowledge grounded in environment
dynamics.Comment: in proceedings of ICML 2
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
Language technologies that accurately model the dynamics of events must
perform commonsense reasoning. Existing work evaluating commonsense reasoning
focuses on making inferences about common, everyday situations. To instead
investigate the ability to model unusual, unexpected, and unlikely situations,
we explore the task of uncommonsense abductive reasoning. Given a piece of
context with an unexpected outcome, this task requires reasoning abductively to
generate a natural language explanation that makes the unexpected outcome more
likely in the context. To this end, we curate and release a new English
language corpus called UNcommonsense. We characterize the differences between
the performance of human explainers and the best performing large language
models, finding that model-enhanced human-written explanations achieve the
highest quality by trading off between specificity and diversity. Finally, we
experiment with several online imitation learning algorithms to train open and
accessible language models on this task. When compared with the vanilla
supervised fine-tuning approach, these methods consistently reduce lose rates
on both common and uncommonsense abductive reasoning judged by human
evaluators
What's In My Big Data?
Large text corpora are the backbone of language models. However, we have a
limited understanding of the content of these corpora, including general
statistics, quality, social factors, and inclusion of evaluation data
(contamination). In this work, we propose What's In My Big Data? (WIMBD), a
platform and a set of sixteen analyses that allow us to reveal and compare the
contents of large text corpora. WIMBD builds on two basic capabilities -- count
and search -- at scale, which allows us to analyze more than 35 terabytes on a
standard compute node. We apply WIMBD to ten different corpora used to train
popular language models, including C4, The Pile, and RedPajama. Our analysis
uncovers several surprising and previously undocumented findings about these
corpora, including the high prevalence of duplicate, synthetic, and low-quality
content, personally identifiable information, toxic language, and benchmark
contamination. For instance, we find that about 50% of the documents in
RedPajama and LAION-2B-en are duplicates. In addition, several datasets used
for benchmarking models trained on such corpora are contaminated with respect
to important benchmarks, including the Winograd Schema Challenge and parts of
GLUE and SuperGLUE. We open-source WIMBD's code and artifacts to provide a
standard set of evaluations for new text-based corpora and to encourage more
analyses and transparency around them: github.com/allenai/wimbd
Relatório de estágio em farmácia comunitária
Relatório de estágio realizado no âmbito do Mestrado Integrado em Ciências Farmacêuticas, apresentado à Faculdade de Farmácia da Universidade de Coimbr