A Self-enhancement Approach for Domain-specific Chatbot Training via Knowledge Mining and Digest

Large Language Models (LLMs), despite their great power in language generation, often encounter challenges when dealing with intricate and knowledge-demanding queries in specific domains. This paper introduces a novel approach to enhance LLMs by effectively extracting the relevant knowledge from domain-specific textual sources, and the adaptive training of a chatbot with domain-specific inquiries. Our two-step approach starts from training a knowledge miner, namely LLMiner, which autonomously extracts Question-Answer pairs from relevant documents through a chain-of-thought reasoning process. Subsequently, we blend the mined QA pairs with a conversational dataset to fine-tune the LLM as a chatbot, thereby enriching its domain-specific expertise and conversational capabilities. We also developed a new evaluation benchmark which comprises four domain-specific text corpora and associated human-crafted QA pairs for testing. Our model shows remarkable performance improvement over generally aligned LLM and surpasses domain-adapted models directly fine-tuned on domain corpus. In particular, LLMiner achieves this with minimal human intervention, requiring only 600 seed instances, thereby providing a pathway towards self-improvement of LLMs through model-synthesized training data.Comment: Work in progres

A Low-Profile Frequency Reconfigurable Grid-Slotted Patch Antenna

This paper presents a novel low-profile high gain frequency reconfigurable patch antenna with unidirectional radiation pattern by using a grid-slotted patch with tunable varactors loading. The antenna consists of two stacked substrates and three metal layers. A grid-slotted patch with two tunable varactors is placed on the top layer, a microstrip line is placed in the middle of two substrates, and the ground plane is on the bottom layer. A single dc voltage applied on two varactors is used to control the working frequencies of the proposed antenna. By altering the bias voltage, the working frequency of the proposed antenna can be continuously changed within a wide range from 2.45 to 3.55 GHz. The antenna maintains broadside radiation and stable radiation pattern in all the operating modes. The measured antenna gain of the proposed antenna rises from 4.25 to 8.49 dBi with the working frequency increases from 2.45 to 3.55 GHz. Compared to other frequency-reconfigurable antennas available in the literature, the proposed antenna has advantages of a wide frequency tuning range over a bandwidth of 1.45:1, high frequency selectivity, low profile (0.016 free-space wavelength at 2.45 GHz), high gain, stable unidirectional pattern, simple structure, and low cost. These advantages make it a promising candidate for cognitive radio and future wireless communication system

A Novel Ultrawideband Transmitarray Design Using Tightly Coupled Dipole Elements

One of the key challenges in transmitarray (TA) designs is its narrow bandwidth. To overcome this limitation, this paper proposes a novel design of ultrawideband TA antenna based on tightly coupled dipole arrays (TCDAs). The array consists of 20 × 10 unit cells, and each unit cell is composed of a pair of short dipoles connected with a section of meandered transmission line. The size of each unit cell is 20 mm × 10 mm, which is about 0.2λ × 0.1λ, where λ is the wavelength in free space at the lowest working frequency of the TA. By utilizing the strong coupling between adjacent units of the TA, the bandwidth of the TA is improved significantly. To verify the design concept, one prototype of the proposed TA is fabricated and measured. The prototype demonstrates good performance over a bandwidth of 104%, i.e., from 3.0 to 9.5 GHz, which is significantly wider than that of other TAs reported in the literature. Within the working band of the TA antenna, the radiation pattern is stable and no distortion or splitting of the main beam of the antenna is observed. This is the first time that the TA based on TCDA is reported