238 research outputs found
A Slight Re-telling of the David and Goliath Story: Surprising Power Dynamics in Proxy Relationships
This thesis discusses how local forces, despite being the weaker actor in a proxy relationship, manipulate external powers’ support to pursue their own objectives. Three factors – practical advantage, relative will, and diverging objectives – explain this counterintuitive power dynamic. First, local forces have better local knowledge, more extensive networks, and greater legitimacy, which give them leverage and make them desirable partners. Second, local forces\u27 involvement is often existential rather than selective; unlike external powers, local forces are thus unconstrained by domestic political vulnerabilities. This enables them to close the significant power gap with external powers. Third, local forces\u27 objectives may diverge from their sponsors\u27, creating incentives for exploitation and manipulation of external support to pursue their own agenda, regardless of the external powers’ interests. These three factors effectively explain the dynamic between the Soviet Union and Cuba during the Angolan civil war and the relationship between the U.S. and the Kurds in the fight against ISIS. Cuba mostly operated within the Soviet strategic parameters, while at the same time manipulating Soviet support to forward its own interests in Africa. The Kurds manipulated U.S. support while fighting ISIS to acquire territories and to pursue autonomy and independence, goals inconsistent with US interests. Further research is still needed to identify under what conditions local partners will wield this counterintuitive power, since there also are cases in which this does not take place
ON THE USE OF MODERN APPLICATIONS IN ENGLISH CLASS
Recently, more and more modern applications have been applied to the English learning class, among which the most outstanding ones are the ‘The Rain Classroom’ and ‘The Super Star’. The first one, ‘The Rain Classroom’ is a mini-program in Wechat, through which the students can get connected to the teacher directly; they can do homework online and express their ideas on the class’ screen simultaneously. The second one, ‘The Super Star’ is an application that the students have to download on the mobile-phone, and then they can scan the teachers’ material and assignment in the app. In this essay, in order to make a comparison between the two apps, the author tries to carry an investigation and an experiment into the students, so as to find a better way of using the modern applications, in which case, can attract the students’ attention, arouse their interest and guarantee their speaking and writing hours at the same time. Furthermore, more scholars can get a better understanding of these two apps through the essay, and the producers of the app will be able to make some adjustment to them timely. This essay will form a new viewpoint on the multimedia English teaching in China, even in the world
Estimating a Large Drive Time Matrix between Zip Codes in the United States: A Differential Sampling Approach
Estimating a massive drive time matrix between locations is a practical but
challenging task. The challenges include availability of reliable road network
(including traffic) data, programming expertise, and access to high-performance
computing resources. This research proposes a method for estimating a
nationwide drive time matrix between ZIP code areas in the U.S.--a geographic
unit at which many national datasets such as health information are compiled
and distributed. The method (1) does not rely on intensive efforts in data
preparation or access to advanced computing resources, (2) uses algorithms of
varying complexity and computational time to estimate drive times of different
trip lengths, and (3) accounts for both interzonal and intrazonal drive times.
The core design samples ZIP code pairs with various intensities according to
trip lengths and derives the drive times via Google Maps API, and the Google
times are then used to adjust and improve some primitive estimates of drive
times with low computational costs. The result provides a valuable resource for
researchers
Learning Purified Feature Representations from Task-irrelevant Labels
Learning an empirically effective model with generalization using limited
data is a challenging task for deep neural networks. In this paper, we propose
a novel learning framework called PurifiedLearning to exploit task-irrelevant
features extracted from task-irrelevant labels when training models on
small-scale datasets. Particularly, we purify feature representations by using
the expression of task-irrelevant information, thus facilitating the learning
process of classification. Our work is built on solid theoretical analysis and
extensive experiments, which demonstrate the effectiveness of PurifiedLearning.
According to the theory we proved, PurifiedLearning is model-agnostic and
doesn't have any restrictions on the model needed, so it can be combined with
any existing deep neural networks with ease to achieve better performance. The
source code of this paper will be available in the future for reproducibility.Comment: arXiv admin note: substantial text overlap with arXiv:2011.0847
Safe RLHF: Safe Reinforcement Learning from Human Feedback
With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations
Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
Knowledge-intensive tasks (e.g., open-domain question answering (QA)) require
a substantial amount of factual knowledge and often rely on external
information for assistance. Recently, large language models (LLMs) (e.g.,
ChatGPT), have demonstrated impressive prowess in solving a wide range of tasks
with world knowledge, including knowledge-intensive tasks. However, it remains
unclear how well LLMs are able to perceive their factual knowledge boundaries,
particularly how they behave when incorporating retrieval augmentation. In this
study, we present an initial analysis of the factual knowledge boundaries of
LLMs and how retrieval augmentation affects LLMs on open-domain QA. Specially,
we focus on three primary research questions and analyze them by examining QA
performance, priori judgement and posteriori judgement of LLMs. We show
evidence that LLMs possess unwavering confidence in their capabilities to
respond to questions and the accuracy of their responses. Furthermore,
retrieval augmentation proves to be an effective approach in enhancing LLMs'
awareness of knowledge boundaries, thereby improving their judgemental
abilities. Additionally, we also find that LLMs have a propensity to rely on
the provided retrieval results when formulating answers, while the quality of
these results significantly impacts their reliance. The code to reproduce this
work is available at https://github.com/RUCAIBox/LLM-Knowledge-Boundary
Coenzyme Q deficiency may predispose to sudden unexplained death via an increased risk of cardiac arrhythmia
Cardiac arrhythmia is currently considered to be the direct cause of death in a majority of sudden unexplained death (SUD) cases, yet the genetic predisposition and corresponding endophenotypes contributing to SUD remain incompletely understood. In this study, we aimed to investigate the involvement of Coenzyme Q (CoQ) deficiency in SUD. First, we re-analyzed the exome sequencing data of 45 SUD and 151 sudden infant death syndrome (SIDS) cases from our previous studies, focusing on previously overlooked genetic variants in 44 human CoQ deficiency-related genes. A considerable proportion of the SUD (38%) and SIDS (37%) cases were found to harbor rare variants with likely functional effects. Subsequent burden testing, including all rare exonic and untranslated region variants identified in our case cohorts, further confirmed the existence of significant genetic burden. Based on the genetic findings, the influence of CoQ deficiency on electrophysiological and morphological properties was further examined in a mouse model. A significantly prolonged PR interval and an increased occurrence of atrioventricular block were observed in the 4-nitrobenzoate induced CoQ deficiency mouse group, suggesting that CoQ deficiency may predispose individuals to sudden death through an increased risk of cardiac arrhythmia. Overall, our findings suggest that CoQ deficiency-related genes should also be considered in the molecular autopsy of SUD
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
In various natural language processing tasks, passage retrieval and passage
re-ranking are two key procedures in finding and ranking relevant information.
Since both the two procedures contribute to the final performance, it is
important to jointly optimize them in order to achieve mutual improvement. In
this paper, we propose a novel joint training approach for dense passage
retrieval and passage re-ranking. A major contribution is that we introduce the
dynamic listwise distillation, where we design a unified listwise training
approach for both the retriever and the re-ranker. During the dynamic
distillation, the retriever and the re-ranker can be adaptively improved
according to each other's relevance information. We also propose a hybrid data
augmentation strategy to construct diverse training instances for listwise
training approach. Extensive experiments show the effectiveness of our approach
on both MSMARCO and Natural Questions datasets. Our code is available at
https://github.com/PaddlePaddle/RocketQA.Comment: EMNLP 202
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