38 research outputs found
Zero-Resource Hallucination Prevention for Large Language Models
The prevalent use of large language models (LLMs) in various domains has
drawn attention to the issue of "hallucination," which refers to instances
where LLMs generate factually inaccurate or ungrounded information. Existing
techniques for hallucination detection in language assistants rely on intricate
fuzzy, specific free-language-based chain of thought (CoT) techniques or
parameter-based methods that suffer from interpretability issues. Additionally,
the methods that identify hallucinations post-generation could not prevent
their occurrence and suffer from inconsistent performance due to the influence
of the instruction format and model style. In this paper, we introduce a novel
pre-detection self-evaluation technique, referred to as {\method}, which
focuses on evaluating the model's familiarity with the concepts present in the
input instruction and withholding the generation of response in case of
unfamiliar concepts. This approach emulates the human ability to refrain from
responding to unfamiliar topics, thus reducing hallucinations. We validate
{\method} across four different large language models, demonstrating
consistently superior performance compared to existing techniques. Our findings
propose a significant shift towards preemptive strategies for hallucination
mitigation in LLM assistants, promising improvements in reliability,
applicability, and interpretability
Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation
Session-based recommendation intends to predict next purchased items based on
anonymous behavior sequences. Numerous economic studies have revealed that item
price is a key factor influencing user purchase decisions. Unfortunately,
existing methods for session-based recommendation only aim at capturing user
interest preference, while ignoring user price preference. Actually, there are
primarily two challenges preventing us from accessing price preference.
Firstly, the price preference is highly associated to various item features
(i.e., category and brand), which asks us to mine price preference from
heterogeneous information. Secondly, price preference and interest preference
are interdependent and collectively determine user choice, necessitating that
we jointly consider both price and interest preference for intent modeling. To
handle above challenges, we propose a novel approach Bi-Preference Learning
Heterogeneous Hypergraph Networks (BiPNet) for session-based recommendation.
Specifically, the customized heterogeneous hypergraph networks with a
triple-level convolution are devised to capture user price and interest
preference from heterogeneous features of items. Besides, we develop a
Bi-Preference Learning schema to explore mutual relations between price and
interest preference and collectively learn these two preferences under the
multi-task learning architecture. Extensive experiments on multiple public
datasets confirm the superiority of BiPNet over competitive baselines.
Additional research also supports the notion that the price is crucial for the
task.Comment: This paper has been accepted by ACM TOI
Towards Personalized Federated Learning via Heterogeneous Model Reassembly
This paper focuses on addressing the practical yet challenging problem of
model heterogeneity in federated learning, where clients possess models with
different network structures. To track this problem, we propose a novel
framework called pFedHR, which leverages heterogeneous model reassembly to
achieve personalized federated learning. In particular, we approach the problem
of heterogeneous model personalization as a model-matching optimization task on
the server side. Moreover, pFedHR automatically and dynamically generates
informative and diverse personalized candidates with minimal human
intervention. Furthermore, our proposed heterogeneous model reassembly
technique mitigates the adverse impact introduced by using public data with
different distributions from the client data to a certain extent. Experimental
results demonstrate that pFedHR outperforms baselines on three datasets under
both IID and Non-IID settings. Additionally, pFedHR effectively reduces the
adverse impact of using different public data and dynamically generates diverse
personalized models in an automated manner
Weak Supervision for Fake News Detection via Reinforcement Learning
Today social media has become the primary source for news. Via social media
platforms, fake news travel at unprecedented speeds, reach global audiences and
put users and communities at great risk. Therefore, it is extremely important
to detect fake news as early as possible. Recently, deep learning based
approaches have shown improved performance in fake news detection. However, the
training of such models requires a large amount of labeled data, but manual
annotation is time-consuming and expensive. Moreover, due to the dynamic nature
of news, annotated samples may become outdated quickly and cannot represent the
news articles on newly emerged events. Therefore, how to obtain fresh and
high-quality labeled samples is the major challenge in employing deep learning
models for fake news detection. In order to tackle this challenge, we propose a
reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which
can leverage users' reports as weak supervision to enlarge the amount of
training data for fake news detection. The proposed framework consists of three
main components: the annotator, the reinforced selector and the fake news
detector. The annotator can automatically assign weak labels for unlabeled news
based on users' reports. The reinforced selector using reinforcement learning
techniques chooses high-quality samples from the weakly labeled data and
filters out those low-quality ones that may degrade the detector's prediction
performance. The fake news detector aims to identify fake news based on the
news content. We tested the proposed framework on a large collection of news
articles published via WeChat official accounts and associated user reports.
Extensive experiments on this dataset show that the proposed WeFEND model
achieves the best performance compared with the state-of-the-art methods.Comment: AAAI 202
A Benchmark Dataset for Understandable Medical Language Translation
In this paper, we introduce MedLane -- a new human-annotated Medical Language
translation dataset, to align professional medical sentences with
layperson-understandable expressions. The dataset contains 12,801 training
samples, 1,015 validation samples, and 1,016 testing samples. We then evaluate
one naive and six deep learning-based approaches on the MedLane dataset,
including directly copying, a statistical machine translation approach Moses,
four neural machine translation approaches (i.e., the proposed PMBERT-MT model,
Seq2Seq and its two variants), and a modified text summarization model
PointerNet. To compare the results, we utilize eleven metrics, including three
new measures specifically designed for this task. Finally, we discuss the
limitations of MedLane and baselines, and point out possible research
directions for this task