Decision-Oriented Dialogue for Human-AI Collaboration

We describe a class of tasks called decision-oriented dialogues, in which AI assistants must collaborate with one or more humans via natural language to help them make complex decisions. We formalize three domains in which users face everyday decisions: (1) choosing an assignment of reviewers to conference papers, (2) planning a multi-step itinerary in a city, and (3) negotiating travel plans for a group of friends. In each of these settings, AI assistants and users have disparate abilities that they must combine to arrive at the best decision: assistants can access and process large amounts of information, while users have preferences and constraints external to the system. For each task, we build a dialogue environment where agents receive a reward based on the quality of the final decision they reach. Using these environments, we collect human-human dialogues with humans playing the role of assistant. To compare how current AI assistants communicate in these settings, we present baselines using large language models in self-play. Finally, we highlight a number of challenges models face in decision-oriented dialogues, ranging from efficient communication to reasoning and optimization, and release our environments as a testbed for future modeling work

InCoder: A Generative Model for Code Infilling and Synthesis

Code is seldom written in a single left-to-right pass and is instead repeatedly edited and refined. We introduce InCoder, a unified generative model that can perform program synthesis (via left-to-right generation) as well as editing (via infilling). InCoder is trained to generate code files from a large corpus of permissively licensed code, where regions of code have been randomly masked and moved to the end of each file, allowing code infilling with bidirectional context. Our model is the first generative model that is able to directly perform zero-shot code infilling, which we evaluate on challenging tasks such as type inference, comment generation, and variable re-naming. We find that the ability to condition on bidirectional context substantially improves performance on these tasks, while still performing comparably on standard program synthesis benchmarks in comparison to left-to-right only models pretrained at similar scale. The InCoder models and code are publicly released. https://sites.google.com/view/incoder-code-modelsComment: 25 pages, 13 figures. v2: added NeoX-20B results & StackOverflow corpus inf

UniMASK: Unified Inference in Sequential Decision Problems

Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same idea also applies naturally to sequential decision-making, where many well-studied tasks like behavior cloning, offline reinforcement learning, inverse dynamics, and waypoint conditioning correspond to different sequence maskings over a sequence of states, actions, and returns. We introduce the UniMASK framework, which provides a unified way to specify models which can be trained on many different sequential decision-making tasks. We show that a single UniMASK model is often capable of carrying out many tasks with performance similar to or better than single-task models. Additionally, after fine-tuning, our UniMASK models consistently outperform comparable single-task models. Our code is publicly available at https://github.com/micahcarroll/uniMASK.Comment: NeurIPS 2022 (Oral). A prior version was published at an ICML Workshop, available at arXiv:2204.1332

Efficacy and safety of low-dose IL-2 in the treatment of systemic lupus erythematosus: A randomised, double-blind, placebo-controlled trial

Objectives Open-labelled clinical trials suggested that low-dose IL-2 might be effective in treatment of systemic lupus erythematosus (SLE). A double-blind and placebocontrolled trial is required to formally evaluate the safety and efficacy of low-dose IL-2 therapy. Methods A randomised, double-blind and placebocontrolled clinical trial was designed to treat 60 patients with active SLE. These patients received either IL-2 (n=30) or placebo (n=30) with standard treatment for 12 weeks, and were followed up for additional 12 weeks. IL-2 at a dose of 1 million IU or placebo was administered subcutaneously every other day for 2 weeks and followed by a 2-week break as one treatment cycle. The primary endpoint was the SLE Responder Index-4 (SRI-4) at week 12. The secondary endpoints were other clinical responses, safety and dynamics of immune cell subsets. Results At week 12, the SRI-4 response rates were 55.17% and 30.00% for IL-2 and placebo, respectively (p=0.052). At week 24, the SRI-4 response rate of IL-2 group was 65.52%, compared with 36.67% of the placebo group (p=0.027). The primary endpoint was not met at week 12. Low-dose IL-2 treatment resulted in 53.85% (7/13) complete remission in patients with lupus nephritis, compared with 16.67% (2/12) in the placebo group (p=0.036). No serious infection was observed in the IL-2 group, but two in placebo group. Besides expansion of regulatory T cells, low-dose IL-2 may also sustain cellular immunity with enhanced natural killer cells. Conclusions Low-dose IL-2 might be effective and tolerated in treatment of SThe work was supported by the National Natural Science Foundation of China (31530020,31570880,81471601,81601417 and 81701598), Peking-Tsinghua Center for Life Sciences to ZG LI, Beijing Sci-Tech Committee Z171100000417007,Clinical Medicine Plus X-Young Scholars Project of Peking University (PKU2019LCXQ013) supported by the Fundamental Research Funds for the Central Universities, Beijing Nova Program Z171100001117025, National Key Research and Development Program of China (2017YFC0909003 to DY), BellberryViertel Senior Medical Research Fellowship to DY and Beijing SL PHARM

HBI Institutional Responsibility : Reading Nutritional Labels : Higher Health Awareness on Beverage Choice

In this study, we aimed to examine whether UBC students who read the nutritional labels on beverages are more likely to make a healthier beverage choice. Specifically, we were interested in finding out the relationship between reading labels and healthy beverages choice. Survey data accessed participants’ action of reading labels and other characteristics that may be relevant to healthy lifestyles. We intended to use recipient data to measure what labels do students normally read and their lifestyles. We predicted that students who read labels on beverages are more likely to choose healthy beverage than students who do not read labels. Among students who read labels, those who read ingredients labels are more likely to choose healthy beverages compare to students who only read nutrition facts labels. We collected data over 2 weeks; each participants filled out the survey based on their preferences and consumption on beverages. Our results did not support our hypothesis, as reading labels do not predict a healthier beverage choice among students on UBC campus. However, we have a number of limitations that may affect our survey results. In future research, experimental study can be conducted to further investigate why the act of reading labels does not predict the preference and consumption of healthy beverages. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”Arts, Faculty ofPsychology, Department ofUnreviewedUndergraduat