134 research outputs found
Substitutive First-party Content as a Strategic Decision for Platform Growth â Evidence from a B2B Platform
This paper examines the effect of substitutive first-party content (SFPC) as a strategic variable by a business-to-business (B2B) e-commerce platform. Constructing a unique time-series dataset, we find that SFPCâs impact differs in the early stage of the platform and in the later stage when it has a larger user base and has transformed itself into a service provider. In the early stage, increasing SFPC can attract more buyers to trade but may crowd out sellers, leading to an insignificant impact on total trading volume. In the second stage, however, SFPC no longer hurts seller participation and increases total trading volume. We also find that SFPC could attract new users consistently across the two stages. Our findings suggest a strategic role of SFPC to mitigate the âchickenâandâegg problem â in the early stage of a two-sided B2B platform and to continuously grow platform size when it becomes more established
Differentiation With Shared Features And Cannibalization Of Information Goods
Large sunk cost of development, negligible cost of reproduction and distribution and substantial economies of scale make information goods distinct from industry goods. In this paper, we analyse versioning strategies of horizontally differentiated information goods with shared feature sets, discrete hierarchical groups and continuous individual consumer tastes. Based on our modelling results, when cannibalization is considered among different market segments, it is always sub-optimal to differentiate information goods if market is not fully differentiated or characteristics of the information goods are not specifically designed to relate to certain market segments
Inducing Causal Structure for Abstractive Text Summarization
The mainstream of data-driven abstractive summarization models tends to
explore the correlations rather than the causal relationships. Among such
correlations, there can be spurious ones which suffer from the language prior
learned from the training corpus and therefore undermine the overall
effectiveness of the learned model. To tackle this issue, we introduce a
Structural Causal Model (SCM) to induce the underlying causal structure of the
summarization data. We assume several latent causal factors and non-causal
factors, representing the content and style of the document and summary.
Theoretically, we prove that the latent factors in our SCM can be identified by
fitting the observed training data under certain conditions. On the basis of
this, we propose a Causality Inspired Sequence-to-Sequence model (CI-Seq2Seq)
to learn the causal representations that can mimic the causal factors, guiding
us to pursue causal information for summary generation. The key idea is to
reformulate the Variational Auto-encoder (VAE) to fit the joint distribution of
the document and summary variables from the training corpus. Experimental
results on two widely used text summarization datasets demonstrate the
advantages of our approach
Retrieve Only When It Needs: Adaptive Retrieval Augmentation for Hallucination Mitigation in Large Language Models
Hallucinations pose a significant challenge for the practical implementation
of large language models (LLMs). The utilization of parametric knowledge in
generating factual content is constrained by the limited knowledge of LLMs,
potentially resulting in internal hallucinations. While incorporating external
information can help fill knowledge gaps, it also introduces the risk of
irrelevant information, thereby increasing the likelihood of external
hallucinations. A careful and balanced integration of the parametric knowledge
within LLMs with external information is crucial to alleviate hallucinations.
In this study, we present Rowen, a novel approach that enhances LLMs with a
selective retrieval augmentation process tailored to address hallucinated
outputs. This process is governed by a multilingual semantic-aware detection
module, which evaluates the consistency of the perturbed responses across
various languages for the same queries. Upon detecting inconsistencies
indicative of hallucinations, Rowen activates the retrieval of external
information to rectify the model outputs. Rowen adeptly harmonizes the
intrinsic parameters in LLMs with external knowledge sources, effectively
mitigating hallucinations by ensuring a balanced integration of internal
reasoning and external evidence. Through a comprehensive empirical analysis, we
demonstrate that Rowen surpasses the current state-of-the-art in both detecting
and mitigating hallucinated content within the outputs of LLMs
PD-L1 aptamer-functionalized degradable hafnium oxide nanoparticles for near infrared-II diagnostic imaging and radiosensitization
Immune checkpoint blockade is now recognized as a paradigm-shifting cancer therapeutic strategy, whereas there remains difficulty in accurately predicting immunotherapy efficacy by PD-L1 expression. In addition, radiotherapy for cancer patients faces the problem of insufficient dose of radiotherapy at the tumor site while which have been not tolerated by normal tissues. In this study, we created PD-L1 aptamer-anchored spherical nucleic acids (SNAs) with a shell made of PD-L1 aptamer and indocyanine green (ICG) embedded in a mesoporous hafnium oxide nanoparticle core (Hf@ICG-Apt). Upon low pH irradiation in the tumor sites, the nano-system enabled the release of ICG in the high PD-L1 expression tumor to develop a high tumor-to-background ratio of 7.97 ± 0.76 and enhanced the ICG tumor retention to more than 48 h. Moreover, Hf@ICG-Apt improved radiation therapy (RT) when combined with radiation. Notably, Hf@ICG-Apt showed scarcely any systemic toxicity in vivo. Overall, this research offered a novel approach for applying reliable monitoring of PD-L1 expression and localization and robust RT sensitization against cancer with good biosafety
On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective
Recently, we have witnessed generative retrieval increasingly gaining
attention in the information retrieval (IR) field, which retrieves documents by
directly generating their identifiers. So far, much effort has been devoted to
developing effective generative retrieval models. There has been less attention
paid to the robustness perspective. When a new retrieval paradigm enters into
the real-world application, it is also critical to measure the
out-of-distribution (OOD) generalization, i.e., how would generative retrieval
models generalize to new distributions. To answer this question, firstly, we
define OOD robustness from three perspectives in retrieval problems: 1) The
query variations; 2) The unforeseen query types; and 3) The unforeseen tasks.
Based on this taxonomy, we conduct empirical studies to analyze the OOD
robustness of several representative generative retrieval models against dense
retrieval models. The empirical results indicate that the OOD robustness of
generative retrieval models requires enhancement. We hope studying the OOD
robustness of generative retrieval models would be advantageous to the IR
community.Comment: 4 pages, submit to GenIR2
L^2R: Lifelong Learning for First-stage Retrieval with Backward-Compatible Representations
First-stage retrieval is a critical task that aims to retrieve relevant
document candidates from a large-scale collection. While existing retrieval
models have achieved impressive performance, they are mostly studied on static
data sets, ignoring that in the real-world, the data on the Web is continuously
growing with potential distribution drift. Consequently, retrievers trained on
static old data may not suit new-coming data well and inevitably produce
sub-optimal results. In this work, we study lifelong learning for first-stage
retrieval, especially focusing on the setting where the emerging documents are
unlabeled since relevance annotation is expensive and may not keep up with data
emergence. Under this setting, we aim to develop model updating with two goals:
(1) to effectively adapt to the evolving distribution with the unlabeled
new-coming data, and (2) to avoid re-inferring all embeddings of old documents
to efficiently update the index each time the model is updated.
We first formalize the task and then propose a novel Lifelong Learning method
for the first-stage Retrieval, namely L^2R. L^2R adopts the typical memory
mechanism for lifelong learning, and incorporates two crucial components: (1)
selecting diverse support negatives for model training and memory updating for
effective model adaptation, and (2) a ranking alignment objective to ensure the
backward-compatibility of representations to save the cost of index rebuilding
without hurting the model performance. For evaluation, we construct two new
benchmarks from LoTTE and Multi-CPR datasets to simulate the document
distribution drift in realistic retrieval scenarios. Extensive experiments show
that L^2R significantly outperforms competitive lifelong learning baselines.Comment: accepted by CIKM202
Stable Knowledge Editing in Large Language Models
Efficient knowledge editing of large language models is crucial for replacing
obsolete information or incorporating specialized knowledge on a large scale.
However, previous methods implicitly assume that knowledge is localized and
isolated within the model, an assumption that oversimplifies the interconnected
nature of model knowledge. The premise of localization results in an incomplete
knowledge editing, whereas an isolated assumption may impair both other
knowledge and general abilities. It introduces instability to the performance
of the knowledge editing method. To transcend these assumptions, we introduce
StableKE, a method adopts a novel perspective based on knowledge augmentation
rather than knowledge localization. To overcome the expense of human labeling,
StableKE integrates two automated knowledge augmentation strategies: Semantic
Paraphrase Enhancement strategy, which diversifies knowledge descriptions to
facilitate the teaching of new information to the model, and Contextual
Description Enrichment strategy, expanding the surrounding knowledge to prevent
the forgetting of related information. StableKE surpasses other knowledge
editing methods, demonstrating stability both edited knowledge and multi-hop
knowledge, while also preserving unrelated knowledge and general abilities.
Moreover, StableKE can edit knowledge on ChatGPT
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