128 research outputs found
Selecting and Generating Computational Meaning Representations for Short Texts
Language conveys meaning, so natural language processing (NLP) requires representations of meaning. This work addresses two broad questions: (1) What meaning representation should we use? and (2) How can we transform text to our chosen meaning representation? In the first part, we explore different meaning representations (MRs) of short texts, ranging from surface forms to deep-learning-based models. We show the advantages and disadvantages of a variety of MRs for summarization, paraphrase detection, and clustering. In the second part, we use SQL as a running example for an in-depth look at how we can parse text into our chosen MR. We examine the text-to-SQL problem from three perspectives—methodology, systems, and applications—and show how each contributes to a fuller understanding of the task.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143967/1/cfdollak_1.pd
State-of-the-art generalisation research in NLP: a taxonomy and review
The ability to generalise well is one of the primary desiderata of natural
language processing (NLP). Yet, what `good generalisation' entails and how it
should be evaluated is not well understood, nor are there any common standards
to evaluate it. In this paper, we aim to lay the ground-work to improve both of
these issues. We present a taxonomy for characterising and understanding
generalisation research in NLP, we use that taxonomy to present a comprehensive
map of published generalisation studies, and we make recommendations for which
areas might deserve attention in the future. Our taxonomy is based on an
extensive literature review of generalisation research, and contains five axes
along which studies can differ: their main motivation, the type of
generalisation they aim to solve, the type of data shift they consider, the
source by which this data shift is obtained, and the locus of the shift within
the modelling pipeline. We use our taxonomy to classify over 400 previous
papers that test generalisation, for a total of more than 600 individual
experiments. Considering the results of this review, we present an in-depth
analysis of the current state of generalisation research in NLP, and make
recommendations for the future. Along with this paper, we release a webpage
where the results of our review can be dynamically explored, and which we
intend to up-date as new NLP generalisation studies are published. With this
work, we aim to make steps towards making state-of-the-art generalisation
testing the new status quo in NLP.Comment: 35 pages of content + 53 pages of reference
A Survey on LLM-generated Text Detection: Necessity, Methods, and Future Directions
The powerful ability to understand, follow, and generate complex language
emerging from large language models (LLMs) makes LLM-generated text flood many
areas of our daily lives at an incredible speed and is widely accepted by
humans. As LLMs continue to expand, there is an imperative need to develop
detectors that can detect LLM-generated text. This is crucial to mitigate
potential misuse of LLMs and safeguard realms like artistic expression and
social networks from harmful influence of LLM-generated content. The
LLM-generated text detection aims to discern if a piece of text was produced by
an LLM, which is essentially a binary classification task. The detector
techniques have witnessed notable advancements recently, propelled by
innovations in watermarking techniques, zero-shot methods, fine-turning LMs
methods, adversarial learning methods, LLMs as detectors, and human-assisted
methods. In this survey, we collate recent research breakthroughs in this area
and underscore the pressing need to bolster detector research. We also delve
into prevalent datasets, elucidating their limitations and developmental
requirements. Furthermore, we analyze various LLM-generated text detection
paradigms, shedding light on challenges like out-of-distribution problems,
potential attacks, and data ambiguity. Conclusively, we highlight interesting
directions for future research in LLM-generated text detection to advance the
implementation of responsible artificial intelligence (AI). Our aim with this
survey is to provide a clear and comprehensive introduction for newcomers while
also offering seasoned researchers a valuable update in the field of
LLM-generated text detection. The useful resources are publicly available at:
https://github.com/NLP2CT/LLM-generated-Text-Detection
Recommended from our members
Enabling Structured Navigation of Longform Spoken Dialog with Automatic Summarization
Longform spoken dialog is a rich source of information that is present in all facets of everyday life, taking the form of podcasts, debates, and interviews; these mediums contain important topics ranging from healthcare and diversity to current events, economics and politics. Individuals need to digest informative content to know how to vote, decide how to stay safe from COVID-19, and how to increase diversity in the workplace.
Unfortunately compared to text, spoken dialog can be challenging to consume as it is slower than reading and difficult to skim or navigate. Although an individual may be interested in a given topic, they may be unwilling to commit the required time necessary to consume long form auditory media given the uncertainty as to whether such content will live up to their expectations. Clearly, there exists a need to provide access to the information spoken dialog provides in a manner through which individuals can quickly and intuitively access areas of interest without investing large amounts of time.
From Human Computer Interaction, we apply the idea of information foraging, which theorizes how people browse and navigate to satisfy an information need, to the longform spoken dialog domain. Information foraging states that people do not browse linearly. Rather people “forage” for information similar to how animals sniff around for food, scanning from area to area, constantly deciding whether to keep investigating their current area or to move on to greener pastures. This is an instance of the classic breadth vs. depth dilemma. People rely on perceived structure and information cues to make these decisions. Unfortunately speech, either spoken or transcribed, is unstructured and lacks information cues, making it difficult for users to browse and navigate.
We create a longform spoken dialog browsing system that utilizes automatic summarization and speech modeling to structure longform dialog to present information in a manner that is both intuitive and flexible towards different user browsing needs. Leveraging summarization models to automatically and hierarchically structure spoken dialog, the system is able to distill information into increasingly salient and abstract summaries, allowing for a tiered representation that, if interested, users can progressively explore. Additionally, we address spoken dialog’s own set of technical challenges to speech modeling that are not present in written text, such as disfluencies, improper punctuation, lack of annotated speech data, and inherent lack of structure.
We create a longform spoken dialog browsing system that utilizes automatic summarization and speech modeling to structure longform dialog to present information in a manner that is both intuitive and flexible towards different user browsing needs. Leveraging summarization models to automatically and hierarchically structure spoken dialog, the system is able to distill information into increasingly salient and abstract summaries, allowing for a tiered representation that, if interested, users can progressively explore. Additionally, we address spoken dialog’s own set of technical challenges to speech modeling that are not present in written text, such as disfluencies, improper punctuation, lack of annotated speech data, and inherent lack of structure. Since summarization is a lossy compression of information, the system provides users with information cues to signal how much additional information is contained on a topic.
This thesis makes the following contributions:
1. We applied the HCI concept of information foraging to longform speech, enabling people to browse and navigate information in podcasts, interviews, panels, and meetings.
2. We created a system that structures longform dialog into hierarchical summaries which help users to 1) skim (browse) audio and 2) navigate and drill down into interesting sections to read full details.
3. We created a human annotated hierarchical dataset to quantitatively evaluate the effectiveness of our system’s hierarchical text generation performance.
4. Lastly, we developed a suite of dialog oriented processing optimizations to improve the user experience of summaries: enhanced readability and fluency of short summaries through better topic chunking and pronoun imputation, and reliable indication of semantic coverage within short summaries to help direct navigation towards interesting information.
We discuss future research in extending the browsing and navigating system to more challenging domains such as lectures, which contain many external references, or workplace conversations, which contain uncontextualized background information and are far less structured than podcasts and interviews
Natural Language Processing: Emerging Neural Approaches and Applications
This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains
Backdoor Attacks and Countermeasures in Natural Language Processing Models: A Comprehensive Security Review
Deep Neural Networks (DNNs) have led to unprecedented progress in various
natural language processing (NLP) tasks. Owing to limited data and computation
resources, using third-party data and models has become a new paradigm for
adapting various tasks. However, research shows that it has some potential
security vulnerabilities because attackers can manipulate the training process
and data source. Such a way can set specific triggers, making the model exhibit
expected behaviors that have little inferior influence on the model's
performance for primitive tasks, called backdoor attacks. Hence, it could have
dire consequences, especially considering that the backdoor attack surfaces are
broad.
To get a precise grasp and understanding of this problem, a systematic and
comprehensive review is required to confront various security challenges from
different phases and attack purposes. Additionally, there is a dearth of
analysis and comparison of the various emerging backdoor countermeasures in
this situation. In this paper, we conduct a timely review of backdoor attacks
and countermeasures to sound the red alarm for the NLP security community.
According to the affected stage of the machine learning pipeline, the attack
surfaces are recognized to be wide and then formalized into three
categorizations: attacking pre-trained model with fine-tuning (APMF) or
prompt-tuning (APMP), and attacking final model with training (AFMT), where
AFMT can be subdivided into different attack aims. Thus, attacks under each
categorization are combed. The countermeasures are categorized into two general
classes: sample inspection and model inspection. Overall, the research on the
defense side is far behind the attack side, and there is no single defense that
can prevent all types of backdoor attacks. An attacker can intelligently bypass
existing defenses with a more invisible attack. ......Comment: 24 pages, 4 figure
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