Dayu Yang, Tianyang Liu, Daoan Zhang, Antoine Simoulin, Xiaoyi Liu, Yuwei Cao, Zhaopu Teng, Xin Qian, Grey Yang, Jiebo Luo, Julian McAuley. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Large Language Models (LLMs) have demonstrated great potential in Conversational Recommender Systems (CRS). However, the application of LLMs to CRS has exposed a notable discrepancy in behavior between LLM-based CRS and human recommenders: LLMs often appear inflexible and passive, frequently rushing to complete the recommendation task without sufficient inquiry. This behavior discrepancy can lead to decreased accuracy in recommendations and lower user satisfaction. Despite its importance, existing studies in CRS lack a study about how to measure such behavior discrepancy. To fill this gap, we propose Behavior Alignment, a new evaluation metric to measure how well the recommendation strategies made by a LLM-based CRS are consistent with human recommenders'. Our experiment results show that the new metric is better aligned with human preferences and can better differentiate how systems perform than existing evaluation metrics. As Behavior Alignment requires explicit and costly human annotations on the recommendation strategies, we also propose a classification-based method to implicitly measure the Behavior Alignment based on the responses. The evaluation results confirm the robustness of the method.

Quickest detection schemes are geared toward detecting a change in the state of a data stream or a real-time process. Classical quickest detection schemes invariably assume knowledge of the pre-change and post-change distributions that may not be available in many applications. In this paper, we present a distribution free nonparametric quickest detection procedure based on a novel distance measure, referred to as the Q-Q distance calculated from the Q-Q plot, for detection of distribution changes. Through experimental study, we show that the Q-Q distance-based detection procedure presents comparable or better performance compared to classical parametric and other nonparametric procedures. The proposed procedure is most effective when detecting small changes.

This paper studies decentralized quickest detection schemes that can be deployed in a sensing environment where data streams are simultaneously collected from multiple channels located distributively to jointly support the detection. Existing decentralized detection approaches are largely parametric that require the knowledge of pre-change and post-change distributions. In this paper, we first present an effective nonparametric detection procedure based on Q-Q distance measure. We then describe two implementations schemes, binary quickest detection and local decision fusion by majority voting, that realize decentralized nonparametric detection. Experimental results show that the proposed method has a comparable performance to the parametric CUSUM test in binary detection. Its decision fusion-based implementation also outperforms the other three popular fusion rules under the parametric framework.

This paper compares different DeltaSigma modulation techniques for direct digital frequency synthesis (DDS). DeltaSigma modulators such as MASH, feedforward, feedback, and error feedback have been implemented in both the phase and frequency domains in a CMOS DDS prototype IC fabricated in a 0.35-mum CMOS technology with core area of 1.7times2.1 mm2and total current consumption of 75 mA. Measured DDS performance demonstrates that the frequency domain DeltaSigma modulation technique achieves better output spectrum purity than the phase domain method. Moreover, a programmable feedforward DeltaSigma modulator is proposed to achieve different in-band and out-band noise shaping effects for DDS applications.

An intrusion detection system (IDS) detects illegal manipulations of computer systems. In intrusion detection systems, feature reduction, including feature extraction and feature selection, plays an important role in a sense of improving classification performance and reducing the computational complexity. Feature reduction is even more important when online detection, which means less computational power and fast real time delivery compared with offline detection, is needed. In this paper, independent component analysis approach is applied to feature extraction in online network intrusion detection problem. We use the KDD Cup 99 data and try to reduce its 41 features such that significant less number of features would be fed into kNN and SVM classifiers. Also, a decision fusion mathod is employed to aggregate the results from multiple classifiers to achieve higher accuracy.

We present an efficient approach for on-chip frequency response measurement, including phase and gain, of analog circuitry in mixed-signal systems. The approach uses direct digital synthesizer (DDS) to supply test sine waves with different frequencies and phases. The output response is analyzed using a multiplier and accumulator. The resultant phase delay measurement is used to correct other measurements such as gain and linearity for more accurate testing and characterization of the analog circuit under test. The approach was implemented in Verilog, synthesized in a field programmable gate array (FPGA), and used for frequency response measurements of an actual device under test.

We present a built-in self-test (BIST) approach based on a direct digital synthesizer (DDS) for functionality testing of analog circuitry in mixed-signal systems. A main contribution of this paper is the BIST-based hardware implementation and measurement of amplifier linearity (IP3) and frequency response, including both phase and gain. The approach has been implemented in Verilog and synthesized into a field-programmable gate array (FPGA), where it was used for functional testing of an actual device under test (DUT) and compared to simulation results.

We present a built-in self-test (BIST) approach based on direct digital synthesizer (DDS) for functionality testing of analog circuitry in mixed-signal systems. Of particular interest, and a main contribution of This work, is the BIST-based hardware implementation and measurement of amplifier linearity (IP3) test using DDS. The approach described in This work has been implemented in Verilog and synthesized into FPGAs where it was used for functional testing and compared to simulation results.