Prompt Optimization of Large Language Model for Interactive Tasks without Gradient and Demonstrations

Large language models (LLMs) have demonstrated remarkable language proficiency, but they face challenges when solving interactive tasks independently. Existing methods either rely on gradient access, which is often inaccessible in state-of-the-art LLMs like GPT-4, or necessitate diverse and high-quality in-context demonstrations. In this study, we propose LLM-PO, a novel approach that enables LLMs to address these tasks without gradient access or extensive demonstrations. The key idea is to maintain a text-based plan and ask LLMs to reflect on pros and cons of the current plan based on experience collected with it, to update the plan, and to collect more experiences with the new plan. Experiments on HotpotQA demonstrate that LLM-PO achieves higher or on par success rates compared to in-context learning (ICL) baselines while requiring less inference cost.Comment: Draft. Work in Progres

WACO: Word-Aligned Contrastive Learning for Speech Translation

End-to-end Speech Translation (E2E ST) aims to directly translate source speech into target text. Existing ST methods perform poorly when only extremely small speech-text data are available for training. We observe that an ST model's performance closely correlates with its embedding similarity between speech and source transcript. In this paper, we propose Word-Aligned COntrastive learning (WACO), a simple and effective method for extremely low-resource speech-to-text translation. Our key idea is bridging word-level representations for both speech and text modalities via contrastive learning. We evaluate WACO and other methods on the MuST-C dataset, a widely used ST benchmark, and on a low-resource direction Maltese-English from IWSLT 2023. Our experiments demonstrate that WACO outperforms the best baseline by 9+ BLEU points with only 1-hour parallel ST data. Code is available at https://github.com/owaski/WACO.Comment: ACL 2023 Poste

A Novel Accuracy and Similarity Search Structure Based on Parallel Bloom Filters

In high-dimensional spaces, accuracy and similarity search by low computing and storage costs are always difficult research topics, and there is a balance between efficiency and accuracy. In this paper, we propose a new structure Similar-PBF-PHT to represent items of a set with high dimensions and retrieve accurate and similar items. The Similar-PBF-PHT contains three parts: parallel bloom filters (PBFs), parallel hash tables (PHTs), and a bitmatrix. Experiments show that the Similar-PBF-PHT is effective in membership query and K-nearest neighbors (K-NN) search. With accurate querying, the Similar-PBF-PHT owns low hit false positive probability (FPP) and acceptable memory costs. With K-NN querying, the average overall ratio and rank-i ratio of the Hamming distance are accurate and ratios of the Euclidean distance are acceptable. It takes CPU time not I/O times to retrieve accurate and similar items and can deal with different data formats not only numerical values

Time Sequence Map for Interpreting the Thermal Runaway Mechanism of Lithium-Ion Batteries With LiNixCoyMnzO2 Cathode

Thermal runaway is one of the key failure reasons for the lithium-ion batteries. The potential of thermal runaway in applications increases when the industry starts to use high energy LiNixCoyMnzO2 cathode. The thermal runaway mechanism is still unclear, because the side reactions are complex. Heat generation during thermal runaway can be caused by the decomposition of individual cell components, or by interactive reactions between multiple components. This paper tries to comb the heat sources during thermal runaway using a novel method named the “Time Sequence Map” (TSM). The TSM tracks the heat sources according to the notion of thermodynamic systems. The thermodynamic system means a combination of materials that stay and react together, and generate heat independently without interruptions from other thermodynamic systems. With the help of the defined thermodynamic systems, researchers will be rescued from being trapped in the complex reactions, and the heat sources during thermal runaway can be clearly explained from bottom up. The thermal runaway results for two battery samples demonstrate the validity of the TSM. The TSM shows the heat sources including that: (1) fire, (2) internal short circuit, (3) oxidation-reduction reaction between the cathode and anode, etc. The contributions for the heat sources to the thermal runaway are further discussed. Conclusions come to: (1) the major heat source is the oxidation-reduction reaction; (2) the fire releases lots of heat, but most of the heat is not to heat the cell itself; (3) the internal short circuit is critical to trigger the oxidation-reduction reaction; (4) the internal short circuit is not the major heat source that heat the cell to 800°C or higher; (5) the oxidation-reduction reaction is triggered when the temperature reaches a critical temperature. The TSM helps depict the frontiers in the researches of battery thermal runaway. It suggests that we focus on: (1) the relationship between internal short circuit and thermal runaway; (2) the mechanism of the oxidation-reduction reaction between the cathode and anode; (3) the detailed reaction mechanisms for a specific thermodynamic system within the cell

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Specialization Project Final Report: Digital Nudge Chat Bot

19 pagesLife is full of choices and distractions especially in digital environments nowadays. It’s extremely common for individuals to interact with different applications to handle their daily tasks and jobs, such as making schedules or decisions based on different information. All these behaviors require large amount of decision making. Nowadays, it’s getting harder to focus on a particular task given the fact that mobile applications are distracting. How we could help individuals make better decisions and focus on a specific task are what we are interested in. In particular, we are considering the effects of nudges in digital choice environments. To narrow down the scope, we want to leverage the capabilities of bots that are programmed to interact with users automatically. We will be focusing on simple tasks including making weekly plans, writing online documents such as Google Doc, and solving coding problems on LeetCode where people - mostly software engineers - practice interview questions for software engineer positions. We have developed three different bots for these three particular activities. These bots will have interactions with users and send nudges to them. We will see how these chat bots would influence people’s behavior in these specific activities and see how different designs would affect people’s motivation and productivity

On the Impact of Noises in Crowd-Sourced Data for Speech Translation

Training speech translation (ST) models requires large and high-quality datasets. MuST-C is one of the most widely used ST benchmark datasets. It contains around 400 hours of speech-transcript-translation data for each of the eight translation directions. This dataset passes several quality-control filters during creation. However, we find that MuST-C still suffers from three major quality issues: audio-text misalignment, inaccurate translation, and unnecessary speaker's name. What are the impacts of these data quality issues for model development and evaluation? In this paper, we propose an automatic method to fix or filter the above quality issues, using English-German (En-De) translation as an example. Our experiments show that ST models perform better on clean test sets, and the rank of proposed models remains consistent across different test sets. Besides, simply removing misaligned data points from the training set does not lead to a better ST model.Comment: Accepted to IWSLT 2022 as a scientific pape

Density functional theory study of Ir atom deposited on gamma-Al2O3 (001) surface

Iridium adsorption on gamma-Al2O3 (001) surface has been studied using the ab initio calculation method and the electronic structures of the bare and the Ir adsorbed gamma-Al2O3 (001) surfaces have been analyzed. By modeling different adsorption sites, one can conclude that the energetically most favorable sites for the Ir are the top sites of the O atoms at the gamma-Al2O3 (001) surface terminated with octahedral Al. Charge redistribution around the Ir atom adsorbed on the surface improves file activity of the Ir atom as a catalyst. (C) 2008 Elsevier B.V. All rights reserved

Construction of a Multifunctional PCM@Catalyst Composite and Its Application in the Fluid Catalytic Cracking Process

Fluid catalytic cracking (FCC) is one of the most important processes in gasoline/diesel oil production, but the strong endothermic effect accompanied by this reaction often results in the deactivation of the catalyst. In this paper, a novel multifunctional phase change material (PCM)@Catalyst composite was designed and constructed, in which the PCM could be used to store waste heat and regulate the temperature for enhancing the catalytic efficiency of the FCC catalyst. Firstly, a core/shell Al-12wt%Si@Al2O3 was prepared via subsequent vapor treatment and high-temperature calcination of an Al-12wt%Si sphere. The Al species in the Al-12wt%Si served as the source of metal ions and was transformed in situ into a well-defined Al2O3 shell, which greatly improved the thermal stability and prevented the leaking of the Al-12wt% Si core in the high-temperature situation. The PCMs@Catalyst composite was then fabricated by casting the mixed powder of Al-12wt%Si@Al2O3 and Y zeolite into a granulated structure. The FCC results demonstrate that Al-12wt%Si@Al2O3/Y zeolite can optimize product distribution and reduce coke yield. This design concept and synthesis strategy can be extended to the production of a wide variety of hierarchical PCM@Catalyst composites for other reactions