Size Dependence of the Magnetic and Electrical Properties of the Spin-Valve Transistor

The electrical and magnetic properties of the spin-valve transistor (SVT) are investigated as a function of transistor size. A new fabrication process, designed to study the size dependence of the SVT properties, uses: silicon-on-insulator (SOI) wafers, a combination of ion beam and wet etching and a negative tone photoresist (SU8) as an insulating layer. The Si/Pt emitter and Si/Au collector Schottky barrier height do not depend on the transistor dimensions. The parasitic leakage current of the Si/Au collector is, however, proportional to its area. The relative collector current change with magnetic field is 240%, independent of size, while the transfer ratio starts to decrease for SVTs with an emitter area below 25 × 25 ¿m2. The maximum input current is found to be limited by the maximum current density allowed in the base (1.7 × 107 A/cm2), which is in agreement with the maximum current density for spin valve

Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Data

Large language models (LLMs) provide a new way to build chatbots by accepting natural language prompts. Yet, it is unclear how to design prompts to power chatbots to carry on naturalistic conversations while pursuing a given goal, such as collecting self-report data from users. We explore what design factors of prompts can help steer chatbots to talk naturally and collect data reliably. To this aim, we formulated four prompt designs with different structures and personas. Through an online study (N = 48) where participants conversed with chatbots driven by different designs of prompts, we assessed how prompt designs and conversation topics affected the conversation flows and users' perceptions of chatbots. Our chatbots covered 79% of the desired information slots during conversations, and the designs of prompts and topics significantly influenced the conversation flows and the data collection performance. We discuss the opportunities and challenges of building chatbots with LLMs.Comment: 22 pages including Appendix, 7 figures, 7 tables. Accepted to PACM HCI (CSCW 2024

Revealing User Familiarity Bias in Task-Oriented Dialogue via Interactive Evaluation

Most task-oriented dialogue (TOD) benchmarks assume users that know exactly how to use the system by constraining the user behaviors within the system's capabilities via strict user goals, namely "user familiarity" bias. This data bias deepens when it combines with data-driven TOD systems, as it is impossible to fathom the effect of it with existing static evaluations. Hence, we conduct an interactive user study to unveil how vulnerable TOD systems are against realistic scenarios. In particular, we compare users with 1) detailed goal instructions that conform to the system boundaries (closed-goal) and 2) vague goal instructions that are often unsupported but realistic (open-goal). Our study reveals that conversations in open-goal settings lead to catastrophic failures of the system, in which 92% of the dialogues had significant issues. Moreover, we conduct a thorough analysis to identify distinctive features between the two settings through error annotation. From this, we discover a novel "pretending" behavior, in which the system pretends to handle the user requests even though they are beyond the system's capabilities. We discuss its characteristics and toxicity while emphasizing transparency and a fallback strategy for robust TOD systems

QADiver: Interactive Framework for Diagnosing QA Models

Question answering (QA) extracting answers from text to the given question in natural language, has been actively studied and existing models have shown a promise of outperforming human performance when trained and evaluated with SQuAD dataset. However, such performance may not be replicated in the actual setting, for which we need to diagnose the cause, which is non-trivial due to the complexity of model. We thus propose a web-based UI that provides how each model contributes to QA performances, by integrating visualization and analysis tools for model explanation. We expect this framework can help QA model researchers to refine and improve their models.Comment: AAAI 2019 Demonstratio

Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models

Questions in open-domain question answering are often ambiguous, allowing multiple interpretations. One approach to handling them is to identify all possible interpretations of the ambiguous question (AQ) and to generate a long-form answer addressing them all, as suggested by Stelmakh et al., (2022). While it provides a comprehensive response without bothering the user for clarification, considering multiple dimensions of ambiguity and gathering corresponding knowledge remains a challenge. To cope with the challenge, we propose a novel framework, Tree of Clarifications (ToC): It recursively constructs a tree of disambiguations for the AQ -- via few-shot prompting leveraging external knowledge -- and uses it to generate a long-form answer. ToC outperforms existing baselines on ASQA in a few-shot setup across the metrics, while surpassing fully-supervised baselines trained on the whole training set in terms of Disambig-F1 and Disambig-ROUGE. Code is available at https://github.com/gankim/tree-of-clarifications.Comment: Accepted to EMNLP 202

Design of a High Performance Earth Imaging Microsatellite

Multispectral Earth Imaging Satellite (MEISAT) is SaTReCi’s 100 kg micro satellite which will be adequate to support a high resolution multispectral Earth imaging camera. MEISAT, whose development is currently in progress, is capable of achieving a ground resolution of 8.5 meters with a 47 km swath width and 400 km scan length from its intended orbit of 730 km. The low-cost satellite is also capable of storing 8 Gbits of image data which will be transmitted to ground at 10 Mbps with an X-band transmitter. This paper provides an overview of MEISAT mission operation concept. The mission requirements and the system specifications are also discussed. The paper then describes the overall system configuration. The design of the command and data handling system, the power system, the attitude control system, the RF system, and the payload system is presented

FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets

Evaluation of Large Language Models (LLMs) is challenging because aligning to human values requires the composition of multiple skills and the required set of skills varies depending on the instruction. Recent studies have evaluated the performance of LLMs in two ways, (1) automatic evaluation on several independent benchmarks and (2) human or machined-based evaluation giving an overall score to the response. However, both settings are coarse-grained evaluations, not considering the nature of user instructions that require instance-wise skill composition, which limits the interpretation of the true capabilities of LLMs. In this paper, we introduce FLASK (Fine-grained Language Model Evaluation based on Alignment SKill Sets), a fine-grained evaluation protocol that can be used for both model-based and human-based evaluation which decomposes coarse-level scoring to an instance-wise skill set-level. Specifically, we define 12 fine-grained skills needed for LLMs to follow open-ended user instructions and construct an evaluation set by allocating a set of skills for each instance. Additionally, by annotating the target domains and difficulty level for each instance, FLASK provides a holistic view with a comprehensive analysis of a model's performance depending on skill, domain, and difficulty. Through using FLASK, we compare multiple open-sourced and proprietary LLMs and observe highly-correlated findings between model-based and human-based evaluations. FLASK enables developers to more accurately measure the model performance and how it can be improved by analyzing factors that make LLMs proficient in particular skills. For practitioners, FLASK can be used to recommend suitable models for particular situations through comprehensive comparison among various LLMs. We release the evaluation data and code implementation at https://github.com/kaistAI/FLASK