Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue

The emergence of large language models (LLMs) further improves the capabilities of open-domain dialogue systems and can generate fluent, coherent, and diverse responses. However, LLMs still lack an important ability: communication skills, which makes them more like information seeking tools than anthropomorphic chatbots. To make LLMs more anthropomorphic and proactive during the conversation, we add five communication skills to the response generation process: topic transition, proactively asking questions, concept guidance, empathy, and summarising often. The addition of communication skills increases the interest of users in the conversation and attracts them to chat for longer. To enable LLMs better understand and use communication skills, we design and add the inner monologue to LLMs. The complete process is achieved through prompt engineering and in-context learning. To evaluate communication skills, we construct a benchmark named Cskills for evaluating various communication skills, which can also more comprehensively evaluate the dialogue generation ability of the model. Experimental results show that the proposed CSIM strategy improves the backbone models and outperforms the baselines in both automatic and human evaluations

RegaVAE: A Retrieval-Augmented Gaussian Mixture Variational Auto-Encoder for Language Modeling

Retrieval-augmented language models show promise in addressing issues like outdated information and hallucinations in language models (LMs). However, current research faces two main problems: 1) determining what information to retrieve, and 2) effectively combining retrieved information during generation. We argue that valuable retrieved information should not only be related to the current source text but also consider the future target text, given the nature of LMs that model future tokens. Moreover, we propose that aggregation using latent variables derived from a compact latent space is more efficient than utilizing explicit raw text, which is limited by context length and susceptible to noise. Therefore, we introduce RegaVAE, a retrieval-augmented language model built upon the variational auto-encoder (VAE). It encodes the text corpus into a latent space, capturing current and future information from both source and target text. Additionally, we leverage the VAE to initialize the latent space and adopt the probabilistic form of the retrieval generation paradigm by expanding the Gaussian prior distribution into a Gaussian mixture distribution. Theoretical analysis provides an optimizable upper bound for RegaVAE. Experimental results on various datasets demonstrate significant improvements in text generation quality and hallucination removal.Comment: Accepted to the Findings of EMNLP 202

Effects of metal film on transmission characteristics of single-dielectric-slab THz waveguide

The effects of a symmetrical metal film on the transmission characteristics of TM mode in the thicker single-dielectric-slab THz waveguide is analyzed theoretically. We find that the coating of metal film results in huge difference in the attenuation coefficients of TM mode, and it is increasing with respect to increase in the THz frequency. In case of a thicker single-dielectric-slab THz waveguide with low absorption loss, the influence of metal film on the loss of TM mode can not be ignored. We further study the influence of metal film on the mode field distribution of TM mode and we find that the mode field distribution of TM mode in the thicker dielectric slab is varied significantly after coating

THz wave transmission within the metal-clad antiresonant reflecting hollow waveguides

We present the transmission characteristics of THz waves in the metal-clad antiresonant reflecting hollow waveguides. We have derived the equation for the blueshift of the resonance frequency. The effects of the waveguide structure on the blueshift of the resonance frequency are studied comprehensively. In particular, we find that the blueshift of the resonance frequency is strongly affected by the interval between two dielectric slabs. By changing the interval, we obtain that the maximum frequency-tuning-range is up to 2030 GHz, and the maximum sensitivity of the resonance frequency shift is up to 6950 GHz/mm at the resonance order of m = 1. When the THz wave is at the near-zero loss frequency, both the loss and the dispersion of the guide modes are very low

Broadband THz transmission within the symmetrical plastic film coated parallel-plate waveguide

We report the broadband THz transmission within the symmetrical plastic film coated parallel-plate waveguide. We theoretically study the antiresonant reflecting mechanism of the waveguide and we find that the broadband THz wave can transmit in this waveguide with ultra-low loss. The loss of TM mode in this waveguide can be 4 orders of magnitude lower than the uncoated parallel-plate waveguide. The transmission bandwidth of this waveguide is up to 5.12 THz. We further show the mode field distributions which explain the loss mechanism