arXiv subjects

Yonghang Zhou

Publications and source records attributed to Yonghang Zhou.

3 recordsLinked to original sources

arXiv2025

Measuring Investor Learning in Private Markets: A Sequential LLM-Bayesian Analysis of Expert Network Calls

We study investor learning and information acquisition in private markets using a large dataset of expert network calls. We develop a sequential Large Language Model (LLM)-Bayesian framework that treats expert interactions as sequential signals and recovers time-varying beliefs about firm success and associated uncertainty from unstructured conversations, providing a measurement system for how qualitative information is aggregated into investment expectations. We show that expert network calls contain decision-relevant information: a single call increases subsequent investment probability by 6.9 to 9.0 percentage points, while positive sentiment raises deal likelihood by 3.9 to 4.1 percentage points. Informativeness varies across topics and environments: discussions of technology adoption and customer acquisition increase deal probability by up to 14.7 percentage points, particularly in high-uncertainty settings. Information is asymmetric across horizons, with positive signals predicting short-term investment decisions and negative signals more informative about long-run firm performance. Consistent with a belief-based mechanism, investment decisions respond to inferred beliefs rather than raw signals. A one standard deviation increase in success belief raises deal probability by approximately 11 percentage points, while reductions in uncertainty further increase investment likelihood. Our framework improves capital allocation, increasing portfolio returns by 15.26% and F1 by 6.69%, with gains concentrated in the upper tail. Attention and ablation analyses show that conversational cues are particularly informative for technologically complex startups, young firms, diverse founding teams, and firms with low public visibility, where information frictions are severe.

cs.CE↗

arXiv2025

A Bayesian Hybrid Parameter-Efficient Fine-Tuning Method for Large Language Models

Large Language Models (LLMs) have demonstrated transformative potential in reshaping the world. As these models are pretrained on general corpora, they often require domain-specific fine-tuning to optimize performance in specialized business applications. Due to their massive scale, parameter-efficient fine-tuning (PEFT) methods are widely used to reduce training costs. Among them, hybrid PEFT methods that combine multiple PEFT techniques have achieved the best performance. However, existing hybrid PEFT methods face two main challenges when fine-tuning LLMs for specialized applications: (1) relying on point estimates, lacking the ability to quantify uncertainty for reliable decision-making, and (2) struggling to dynamically adapt to emerging data, lacking the ability to suit real-world situations. We propose Bayesian Hybrid Parameter-Efficient Fine-Tuning (BH-PEFT), a novel method that integrates Bayesian learning into hybrid PEFT. BH-PEFT combines Adapter, LoRA, and prefix-tuning to fine-tune feedforward and attention layers of the Transformer. By modeling learnable parameters as distributions, BH-PEFT enables uncertainty quantification. We further propose a Bayesian dynamic fine-tuning approach where the last posterior serves as the prior for the next round, enabling effective adaptation to new data. We evaluated BH-PEFT on business tasks such as sentiment analysis, news categorization, and commonsense reasoning. Results show that our method outperforms existing PEFT baselines, enables uncertainty quantification for more reliable decisions, and improves adaptability in dynamic scenarios. This work contributes to business analytics and data science by proposing a novel BH-PEFT method and dynamic fine-tuning approach that support uncertainty-aware and adaptive decision-making in real-world situations.

cs.LG↗

arXiv2024

Towards Trustworthy Web Attack Detection: An Uncertainty-Aware Ensemble Deep Kernel Learning Model

Web attacks are one of the major and most persistent forms of cyber threats, which bring huge costs and losses to web application-based businesses. Various detection methods, such as signature-based, machine learning-based, and deep learning-based, have been proposed to identify web attacks. However, these methods either (1) heavily rely on accurate and complete rule design and feature engineering, which may not adapt to fast-evolving attacks, or (2) fail to estimate model uncertainty, which is essential to the trustworthiness of the prediction made by the model. In this study, we proposed an Uncertainty-aware Ensemble Deep Kernel Learning (UEDKL) model to detect web attacks from HTTP request payload data with the model uncertainty captured from the perspective of both data distribution and model parameters. The proposed UEDKL utilizes a deep kernel learning model to distinguish normal HTTP requests from different types of web attacks with model uncertainty estimated from data distribution perspective. Multiple deep kernel learning models were trained as base learners to capture the model uncertainty from model parameters perspective. An attention-based ensemble learning approach was designed to effectively integrate base learners' predictions and model uncertainty. We also proposed a new metric named High Uncertainty Ratio-F Score Curve to evaluate model uncertainty estimation. Experiments on BDCI and SRBH datasets demonstrated that the proposed UEDKL framework yields significant improvement in both web attack detection performance and uncertainty estimation quality compared to benchmark models.

cs.LG↗