324 research outputs found
Argument Mining with Structured SVMs and RNNs
We propose a novel factor graph model for argument mining, designed for
settings in which the argumentative relations in a document do not necessarily
form a tree structure. (This is the case in over 20% of the web comments
dataset we release.) Our model jointly learns elementary unit type
classification and argumentative relation prediction. Moreover, our model
supports SVM and RNN parametrizations, can enforce structure constraints (e.g.,
transitivity), and can express dependencies between adjacent relations and
propositions. Our approaches outperform unstructured baselines in both web
comments and argumentative essay datasets.Comment: Accepted for publication at ACL 2017. 11 pages, 5 figures. Code at
https://github.com/vene/marseille and data at http://joonsuk.org
On the Analysis of Cross-Lingual Prompt Tuning for Decoder-based Multilingual Model
An exciting advancement in the field of multilingual models is the emergence
of autoregressive models with zero- and few-shot capabilities, a phenomenon
widely reported in large-scale language models. To further improve model
adaptation to cross-lingual tasks, another trend is to further fine-tune the
language models with either full fine-tuning or parameter-efficient tuning.
However, the interaction between parameter-efficient fine-tuning (PEFT) and
cross-lingual tasks in multilingual autoregressive models has yet to be
studied. Specifically, we lack an understanding of the role of linguistic
distributions in multilingual models in the effectiveness of token-based prompt
tuning. To address this question, we conduct experiments comparing prompt
tuning and fine-tuning on the decoder-based multilingual model, XGLM, with four
cross-lingual tasks (XNLI, PAWS-X, POS, NER). According to our study, prompt
tuning achieves on par or better performance over fine-tuning across all
languages while updating at most 0.13\% of the model parameters. Moreover, we
empirically show that prompt tuning is more effective in enhancing the
performance of low-resource languages than fine-tuning. Our further analysis
shows that the phenomenon is related to the tokenization scheme of the
multilingual model
Classical-to-quantum convolutional neural network transfer learning
Machine learning using quantum convolutional neural networks (QCNNs) has
demonstrated success in both quantum and classical data classification. In
previous studies, QCNNs attained a higher classification accuracy than their
classical counterparts under the same training conditions in the few-parameter
regime. However, the general performance of large-scale quantum models is
difficult to examine because of the limited size of quantum circuits, which can
be reliably implemented in the near future. We propose transfer learning as an
effective strategy for utilizing small QCNNs in the noisy intermediate-scale
quantum era to the full extent. In the classical-to-quantum transfer learning
framework, a QCNN can solve complex classification problems without requiring a
large-scale quantum circuit by utilizing a pre-trained classical convolutional
neural network (CNN). We perform numerical simulations of QCNN models with
various sets of quantum convolution and pooling operations for MNIST data
classification under transfer learning, in which a classical CNN is trained
with Fashion-MNIST data. The results show that transfer learning from classical
to quantum CNN performs considerably better than purely classical transfer
learning models under similar training conditions.Comment: 16 pages, 7 figure
From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models
Being able to predict people's opinions on issues and behaviors in realistic
scenarios can be helpful in various domains, such as politics and marketing.
However, conducting large-scale surveys like the European Social Survey to
solicit people's opinions on individual issues can incur prohibitive costs.
Leveraging prior research showing influence of core human values on individual
decisions and actions, we propose to use value-injected large language models
(LLM) to predict opinions and behaviors. To this end, we present Value
Injection Method (VIM), a collection of two methods -- argument generation and
question answering -- designed to inject targeted value distributions into LLMs
via fine-tuning. We then conduct a series of experiments on four tasks to test
the effectiveness of VIM and the possibility of using value-injected LLMs to
predict opinions and behaviors of people. We find that LLMs value-injected with
variations of VIM substantially outperform the baselines. Also, the results
suggest that opinions and behaviors can be better predicted using
value-injected LLMs than the baseline approaches.Comment: EMNLP 2023 main paper accepte
Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models
Questions in open-domain question answering are often ambiguous, allowing
multiple interpretations. One approach to handling them is to identify all
possible interpretations of the ambiguous question (AQ) and to generate a
long-form answer addressing them all, as suggested by Stelmakh et al., (2022).
While it provides a comprehensive response without bothering the user for
clarification, considering multiple dimensions of ambiguity and gathering
corresponding knowledge remains a challenge. To cope with the challenge, we
propose a novel framework, Tree of Clarifications (ToC): It recursively
constructs a tree of disambiguations for the AQ -- via few-shot prompting
leveraging external knowledge -- and uses it to generate a long-form answer.
ToC outperforms existing baselines on ASQA in a few-shot setup across the
metrics, while surpassing fully-supervised baselines trained on the whole
training set in terms of Disambig-F1 and Disambig-ROUGE. Code is available at
https://github.com/gankim/tree-of-clarifications.Comment: Accepted to EMNLP 202
KoSBi: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Application
Large language models (LLMs) learn not only natural text generation abilities
but also social biases against different demographic groups from real-world
data. This poses a critical risk when deploying LLM-based applications.
Existing research and resources are not readily applicable in South Korea due
to the differences in language and culture, both of which significantly affect
the biases and targeted demographic groups. This limitation requires localized
social bias datasets to ensure the safe and effective deployment of LLMs. To
this end, we present KO SB I, a new social bias dataset of 34k pairs of
contexts and sentences in Korean covering 72 demographic groups in 15
categories. We find that through filtering-based moderation, social biases in
generated content can be reduced by 16.47%p on average for HyperCLOVA (30B and
82B), and GPT-3.Comment: 17 pages, 8 figures, 12 tables, ACL 202
Facilitative Moderation for Online Participation in eRulemaking
This paper describes the use of facilitative moderation strategies in an online rulemaking public participation system. Rulemaking is one of the U.S. government\u27s most important policymaking methods. Although broad transparency and participation rights are part of its legal structure, significant barriers prevent effective engagement by many groups of interested citizens. Regulation Room, an experimental open-government partnership between academic researchers and government agencies, is a socio-technical participation system that uses multiple methods to lower potential barriers to broader participation. To encourage effective individual comments and productive group discussion in Regulation Room, we adapt strategies for facilitative human moderation originating from social science research in deliberative democracy and alternative dispute resolution [24, 1, 18, 14] for use in the demanding online participation setting of eRulemaking. We develop a moderation protocol, deploy it in live Department of Transportation (DOT) rulemakings, and provide an initial analysis of its use through a manual coding of all moderator interventions with respect to the protocol. We then investigate the feasibility of automating the moderation protocol: we employ annotated data from the coding project to train machine learning-based classifers to identify places in the online discussion where human moderator intervention is required. Though the trained classifiers only marginally outperform the baseline, the improvement is statistically signifcant in spite of limited data and a very basic feature set, which is a promising result
Asking Clarification Questions to Handle Ambiguity in Open-Domain QA
Ambiguous questions persist in open-domain question answering, because
formulating a precise question with a unique answer is often challenging.
Previously, Min et al. (2020) have tackled this issue by generating
disambiguated questions for all possible interpretations of the ambiguous
question. This can be effective, but not ideal for providing an answer to the
user. Instead, we propose to ask a clarification question, where the user's
response will help identify the interpretation that best aligns with the user's
intention. We first present CAMBIGNQ, a dataset consisting of 5,654 ambiguous
questions, each with relevant passages, possible answers, and a clarification
question. The clarification questions were efficiently created by generating
them using InstructGPT and manually revising them as necessary. We then define
a pipeline of tasks and design appropriate evaluation metrics. Lastly, we
achieve 61.3 F1 on ambiguity detection and 40.5 F1 on clarification-based QA,
providing strong baselines for future work.Comment: 15 pages, 4 figure
Facilitative Moderation for Online Participation in eRulemaking
This paper describes the use of facilitative moderation strategies in an online rulemaking public participation system. Rulemaking is one of the U.S. government\u27s most important policymaking methods. Although broad transparency and participation rights are part of its legal structure, significant barriers prevent effective engagement by many groups of interested citizens. Regulation Room, an experimental open-government partnership between academic researchers and government agencies, is a socio-technical participation system that uses multiple methods to lower potential barriers to broader participation. To encourage effective individual comments and productive group discussion in Regulation Room, we adapt strategies for facilitative human moderation originating from social science research in deliberative democracy and alternative dispute resolution [24, 1, 18, 14] for use in the demanding online participation setting of eRulemaking. We develop a moderation protocol, deploy it in live Department of Transportation (DOT) rulemakings, and provide an initial analysis of its use through a manual coding of all moderator interventions with respect to the protocol. We then investigate the feasibility of automating the moderation protocol: we employ annotated data from the coding project to train machine learning-based classifers to identify places in the online discussion where human moderator intervention is required. Though the trained classifiers only marginally outperform the baseline, the improvement is statistically signifcant in spite of limited data and a very basic feature set, which is a promising result
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