62 research outputs found
Modeling document features for expert finding
We argue that expert finding is sensitive to multiple document features in an organization, and therefore, can benefit from the incorporation of these document features. We propose a unified language model, which integrates multiple document features, namely, multiple levels of associations, PageRank, indegree, internal document structure, and URL length. Our experiments on two TREC Enterprise Track collections, i.e., the W3C and CSIRO datasets, demonstrate that the natures of the two organizational intranets and two types of expert finding tasks, i.e., key contact finding for CSIRO and knowledgeable person finding for W3C, influence the effectiveness of different document features. Our work provides insights into which document features work for certain types of expert finding tasks, and helps design expert finding strategies that are effective for different scenarios
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Integrating multiple document features in language models for expert finding
We argue that expert finding is sensitive to multiple document features in an organizational intranet. These document features include multiple levels of associations between experts and a query topic from sentence, paragraph, up to document levels, document authority information such as the PageRank, indegree, and URL length of documents, and internal document structures that indicate the experts' relationship with the content of documents. Our assumption is that expert finding can largely benefit from the incorporation of these document features. However, existing language modeling approaches for expert finding have not sufficiently taken into account these document features. We propose a novel language modeling approach, which integrates multiple document features, for expert finding. Our experiments on two large scale TREC Enterprise Track datasets, i.e., the W3C and CSIRO datasets, demonstrate that the natures of the two organizational intranets and two types of expert finding tasks, i.e., key contact finding for CSIRO and knowledgeable person finding for W3C, influence the effectiveness of different document features. Our work provides insights into which document features work for certain types of expert finding tasks, and helps design expert finding strategies that are effective for different scenarios. Our main contribution is to develop an effective formal method for modeling multiple document features in expert finding, and conduct a systematic investigation of their effects. It is worth noting that our novel approach achieves better results in terms of MAP than previous language model based approaches and the best automatic runs in both the TREC2006 and TREC2007 expert search tasks, respectively
SpikeBERT: A Language Spikformer Trained with Two-Stage Knowledge Distillation from BERT
Spiking neural networks (SNNs) offer a promising avenue to implement deep
neural networks in a more energy-efficient way. However, the network
architectures of existing SNNs for language tasks are too simplistic, and deep
architectures have not been fully explored, resulting in a significant
performance gap compared to mainstream transformer-based networks such as BERT.
To this end, we improve a recently-proposed spiking transformer (i.e.,
Spikformer) to make it possible to process language tasks and propose a
two-stage knowledge distillation method for training it, which combines
pre-training by distilling knowledge from BERT with a large collection of
unlabelled texts and fine-tuning with task-specific instances via knowledge
distillation again from the BERT fine-tuned on the same training examples.
Through extensive experimentation, we show that the models trained with our
method, named SpikeBERT, outperform state-of-the-art SNNs and even achieve
comparable results to BERTs on text classification tasks for both English and
Chinese with much less energy consumption
Tailoring Personality Traits in Large Language Models via Unsupervisedly-Built Personalized Lexicons
Personality plays a pivotal role in shaping human expression patterns, thus
regulating the personality of large language models (LLMs) holds significant
potential in enhancing the user experience of LLMs. Previous methods either
relied on fine-tuning LLMs on specific corpora or necessitated manually crafted
prompts to elicit specific personalities from LLMs. However, the former
approach is inefficient and costly, while the latter cannot precisely
manipulate personality traits at a fine-grained level. To address the above
challenges, we have employed a novel Unsupervisedly-Built Personalized Lexicons
(UBPL) in a pluggable manner during the decoding phase of LLMs to manipulate
their personality traits. UBPL is a lexicon built through an unsupervised
approach from a situational judgment test dataset (SJTs4LLM). Users can utilize
UBPL to adjust the probability vectors of predicted words in the decoding phase
of LLMs, thus influencing the personality expression of LLMs. Extensive
experimentation demonstrates the remarkable effectiveness and pluggability of
our method for fine-grained manipulation of LLM's personality.Comment: Work in progres
Adaptive mesh refinement based simulations of three-dimensional detonation combustion in supersonic combustible mixtures with a detailed reaction model
Adsorption Thermodynamics of Methyl Orange from Aqueous Solution onto a Hyper-Cross-Linked Polystyrene Resin Modified with Phenolic Hydroxy Groups
A novel hyper-cross-linked polystyrene resin, HJ-P01, was prepared and its structure characterized via nitrogen adsorption/desorption isotherms, its residual chlorine content and its Fourier-transform infrared (FT-IR) spectrum. The adsorption isotherms of Methyl Orange from aqueous solution onto HJ-P01 resin were subsequently measured and the corresponding thermodynamic parameters calculated. The results indicated that HJ-P01 resin possessed a micro-/meso-porous structure, with its surface being chemically modified by phenolic hydroxy groups. The resin was capable of the effective adsorption of Methyl Orange from aqueous solution, although its adsorption capacity decreased with increasing temperature. The adsorption isotherms could be fitted by the Freundlich isotherm model and the adsorption was shown to be a spontaneous, exothermic process leading to a more ordered system
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