215 research outputs found
Distributed Multi-Task Relationship Learning
Multi-task learning aims to learn multiple tasks jointly by exploiting their
relatedness to improve the generalization performance for each task.
Traditionally, to perform multi-task learning, one needs to centralize data
from all the tasks to a single machine. However, in many real-world
applications, data of different tasks may be geo-distributed over different
local machines. Due to heavy communication caused by transmitting the data and
the issue of data privacy and security, it is impossible to send data of
different task to a master machine to perform multi-task learning. Therefore,
in this paper, we propose a distributed multi-task learning framework that
simultaneously learns predictive models for each task as well as task
relationships between tasks alternatingly in the parameter server paradigm. In
our framework, we first offer a general dual form for a family of regularized
multi-task relationship learning methods. Subsequently, we propose a
communication-efficient primal-dual distributed optimization algorithm to solve
the dual problem by carefully designing local subproblems to make the dual
problem decomposable. Moreover, we provide a theoretical convergence analysis
for the proposed algorithm, which is specific for distributed multi-task
relationship learning. We conduct extensive experiments on both synthetic and
real-world datasets to evaluate our proposed framework in terms of
effectiveness and convergence.Comment: To appear in KDD 201
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
How to develop slim and accurate deep neural networks has become crucial for
real- world applications, especially for those employed in embedded systems.
Though previous work along this research line has shown some promising results,
most existing methods either fail to significantly compress a well-trained deep
network or require a heavy retraining process for the pruned deep network to
re-boost its prediction performance. In this paper, we propose a new layer-wise
pruning method for deep neural networks. In our proposed method, parameters of
each individual layer are pruned independently based on second order
derivatives of a layer-wise error function with respect to the corresponding
parameters. We prove that the final prediction performance drop after pruning
is bounded by a linear combination of the reconstructed errors caused at each
layer. Therefore, there is a guarantee that one only needs to perform a light
retraining process on the pruned network to resume its original prediction
performance. We conduct extensive experiments on benchmark datasets to
demonstrate the effectiveness of our pruning method compared with several
state-of-the-art baseline methods
Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning
In deep reinforcement learning (RL) tasks, an efficient exploration mechanism
should be able to encourage an agent to take actions that lead to less frequent
states which may yield higher accumulative future return. However, both knowing
about the future and evaluating the frequentness of states are non-trivial
tasks, especially for deep RL domains, where a state is represented by
high-dimensional image frames. In this paper, we propose a novel informed
exploration framework for deep RL, where we build the capability for an RL
agent to predict over the future transitions and evaluate the frequentness for
the predicted future frames in a meaningful manner. To this end, we train a
deep prediction model to predict future frames given a state-action pair, and a
convolutional autoencoder model to hash over the seen frames. In addition, to
utilize the counts derived from the seen frames to evaluate the frequentness
for the predicted frames, we tackle the challenge of matching the predicted
future frames and their corresponding seen frames at the latent feature level.
In this way, we derive a reliable metric for evaluating the novelty of the
future direction pointed by each action, and hence inform the agent to explore
the least frequent one
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities
How to learn an effective reinforcement learning-based model for control
tasks from high-level visual observations is a practical and challenging
problem. A key to solving this problem is to learn low-dimensional state
representations from observations, from which an effective policy can be
learned. In order to boost the learning of state encoding, recent works are
focused on capturing behavioral similarities between state representations or
applying data augmentation on visual observations. In this paper, we propose a
novel meta-learner-based framework for representation learning regarding
behavioral similarities for reinforcement learning. Specifically, our framework
encodes the high-dimensional observations into two decomposed embeddings
regarding reward and dynamics in a Markov Decision Process (MDP). A pair of
meta-learners are developed, one of which quantifies the reward similarity and
the other quantifies dynamics similarity over the correspondingly decomposed
embeddings. The meta-learners are self-learned to update the state embeddings
by approximating two disjoint terms in on-policy bisimulation metric. To
incorporate the reward and dynamics terms, we further develop a strategy to
adaptively balance their impacts based on different tasks or environments. We
empirically demonstrate that our proposed framework outperforms
state-of-the-art baselines on several benchmarks, including conventional DM
Control Suite, Distracting DM Control Suite and a self-driving task CARLA
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning
Large language models (LLMs) have showcased their capability with few-shot
inference known as in-context learning. However, in-domain demonstrations are
not always readily available in real scenarios, leading to cross-domain
in-context learning. Besides, LLMs are still facing challenges in long-tail
knowledge in unseen and unfamiliar domains. The above limitations demonstrate
the necessity of Unsupervised Domain Adaptation (UDA). In this paper, we study
the UDA problem under an in-context learning setting to adapt language models
from the source domain to the target domain without any target labels. The core
idea is to retrieve a subset of cross-domain elements that are the most similar
to the query, and elicit language model to adapt in an in-context manner by
learning both target domain distribution and the discriminative task signal
simultaneously with the augmented cross-domain in-context examples. We devise
different prompting and training strategies, accounting for different LM
architectures to learn the target distribution via language modeling. With
extensive experiments on Sentiment Analysis (SA) and Named Entity Recognition
(NER) tasks, we thoroughly study the effectiveness of ICL for domain transfer
and demonstrate significant improvements over baseline models.Comment: EMNLP 202
SOUL: Towards Sentiment and Opinion Understanding of Language
Sentiment analysis is a well-established natural language processing task,
with sentiment polarity classification being one of its most popular and
representative tasks. However, despite the success of pre-trained language
models in this area, they often fall short of capturing the broader
complexities of sentiment analysis. To address this issue, we propose a new
task called Sentiment and Opinion Understanding of Language (SOUL). SOUL aims
to evaluate sentiment understanding through two subtasks: Review Comprehension
(RC) and Justification Generation (JG). RC seeks to validate statements that
focus on subjective information based on a review text, while JG requires
models to provide explanations for their sentiment predictions. To enable
comprehensive evaluation, we annotate a new dataset comprising 15,028
statements from 3,638 reviews. Experimental results indicate that SOUL is a
challenging task for both small and large language models, with a performance
gap of up to 27% when compared to human performance. Furthermore, evaluations
conducted with both human experts and GPT-4 highlight the limitations of the
small language model in generating reasoning-based justifications. These
findings underscore the challenging nature of the SOUL task for existing
models, emphasizing the need for further advancements in sentiment analysis to
address its complexities. The new dataset and code are available at
https://github.com/DAMO-NLP-SG/SOUL.Comment: EMNLP 2023 Main Conference, Short Pape
Multilingual Jailbreak Challenges in Large Language Models
While large language models (LLMs) exhibit remarkable capabilities across a
wide range of tasks, they pose potential safety concerns, such as the
``jailbreak'' problem, wherein malicious instructions can manipulate LLMs to
exhibit undesirable behavior. Although several preventive measures have been
developed to mitigate the potential risks associated with LLMs, they have
primarily focused on English data. In this study, we reveal the presence of
multilingual jailbreak challenges within LLMs and consider two potential risk
scenarios: unintentional and intentional. The unintentional scenario involves
users querying LLMs using non-English prompts and inadvertently bypassing the
safety mechanisms, while the intentional scenario concerns malicious users
combining malicious instructions with multilingual prompts to deliberately
attack LLMs. The experimental results reveal that in the unintentional
scenario, the rate of unsafe content increases as the availability of languages
decreases. Specifically, low-resource languages exhibit three times the
likelihood of encountering harmful content compared to high-resource languages,
with both ChatGPT and GPT-4. In the intentional scenario, multilingual prompts
can exacerbate the negative impact of malicious instructions, with
astonishingly high rates of unsafe output: 80.92\% for ChatGPT and 40.71\% for
GPT-4. To handle such a challenge in the multilingual context, we propose a
novel \textsc{Self-Defense} framework that automatically generates multilingual
training data for safety fine-tuning. Experimental results show that ChatGPT
fine-tuned with such data can achieve a substantial reduction in unsafe content
generation. Data is available at
https://github.com/DAMO-NLP-SG/multilingual-safety-for-LLMs. Warning: This
paper contains examples with potentially harmful content
Sentiment Analysis in the Era of Large Language Models: A Reality Check
Sentiment analysis (SA) has been a long-standing research area in natural
language processing. It can offer rich insights into human sentiments and
opinions and has thus seen considerable interest from both academia and
industry. With the advent of large language models (LLMs) such as ChatGPT,
there is a great potential for their employment on SA problems. However, the
extent to which existing LLMs can be leveraged for different sentiment analysis
tasks remains unclear. This paper aims to provide a comprehensive investigation
into the capabilities of LLMs in performing various sentiment analysis tasks,
from conventional sentiment classification to aspect-based sentiment analysis
and multifaceted analysis of subjective texts. We evaluate performance across
13 tasks on 26 datasets and compare the results against small language models
(SLMs) trained on domain-specific datasets. Our study reveals that while LLMs
demonstrate satisfactory performance in simpler tasks, they lag behind in more
complex tasks requiring deeper understanding or structured sentiment
information. However, LLMs significantly outperform SLMs in few-shot learning
settings, suggesting their potential when annotation resources are limited. We
also highlight the limitations of current evaluation practices in assessing
LLMs' SA abilities and propose a novel benchmark, \textsc{SentiEval}, for a
more comprehensive and realistic evaluation. Data and code during our
investigations are available at
\url{https://github.com/DAMO-NLP-SG/LLM-Sentiment}
Study on the characteristics and mechanism of antibiotic pollution in different aquifers
The risk posed by antibiotics in various aquifers has attracted wide attention. This study investigated the pollution characteristics and controlling factors of antibiotics in different types of aquifers, and identified the indicator factors of antibiotic pollution in aquifers based on a total of 309 sets of samples from Songnen Plain, North China Plain, and Southwest Karst area. The concentrations of 35 antibiotics were analyzed using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The results show that: (1) all 35 antibiotics were detected, with karst aquifers (34 types) and North China porous aquifers (32 types) mainly containing quinolones and macrolide antibiotics, while only 6 types of antibiotics, mainly erythromycin, were detected in Northeast porous aquifers. In porous aquifers, the overall pollution in Northeast China is characterized by low concentrations (median = 2.07 ng/L, detection rate = 100%), while the pollution in North China is relatively heavy (11.76 ng/L), accounting for 49% of the spatial distribution. In the karst aquifers, the antibiotic pollution is characterized by high concentrations (37.5 ng/L) and a large spatial extent (87%). (2) The characteristic differences in antibiotic pollution between karst and porous aquifers are attributed to the hydrogeological conditions (openness and permeability), while the emission intensity of antibiotics is the primary reason for the differences between porous aquifers in different regions. (3) Cluster analysis based on correlation coefficients identified the indicator factors of antibiotics in different types of aquifers. Total organic carbon (TOC) can effectively indicate the antibiotic pollution in porous aquifers, while \begin{document}\end{document} and \begin{document}\end{document} reveal a positive correlation between human activities and antibiotics. Groundwater property parameters are reliable indicators of antibiotic pollution in karst aquifers, with lower antibiotic concentrations observed in alkaline and oxidizing karst water. The research results can provide scientific basis for regional prevention and control of emerging organic contaminants in groundwater
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