PESCO: Prompt-enhanced Self Contrastive Learning for Zero-shot Text Classification

We present PESCO, a novel contrastive learning framework that substantially improves the performance of zero-shot text classification. We formulate text classification as a neural text matching problem where each document is treated as a query, and the system learns the mapping from each query to the relevant class labels by (1) adding prompts to enhance label matching, and (2) using retrieved labels to enrich the training set in a self-training loop of contrastive learning. PESCO achieves state-of-the-art performance on four benchmark text classification datasets. On DBpedia, we achieve 98.5\% accuracy without any labeled data, which is close to the fully-supervised result. Extensive experiments and analyses show all the components of PESCO are necessary for improving the performance of zero-shot text classification.Comment: accepted by ACL 202

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

Whole genome sequencing of foodborne pathogens and global data sharing development

With the rapid development of molecular typing techniques for monitoring foodborne pathogens and outbreak investigations, whole genome sequencing (WGS) is gradually revealing its importance. In the context of the globalization of food trade, it’s urgent to establish details of the links between foodborne pathogens and human exposure in order to accurately monitor and reduce their occurrence. The accuracy of WGS is significantly better than prior analysis tools in the aspect. In this paper, we take Listeria monocytogenes as example to expound the monitoring of foodborne pathogens and the investigation of infection outbreaks, emphasizing the value of WGS in trace-back of foodborne diseases. The technologies for data generation and analysis are summarized, the practical application progress of WGS in the worldwide foodborne pathogen typing is emphasized, and the challenges in the future are prospected

Gadolinium‐Doped Iron Oxide Nanoprobe as Multifunctional Bioimaging Agent and Drug Delivery System

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116012/1/adfm201502868.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/116012/2/adfm201502868-sup-0001-S1.pd

SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents

Humans are social beings; we pursue social goals in our daily interactions, which is a crucial aspect of social intelligence. Yet, AI systems' abilities in this realm remain elusive. We present SOTOPIA, an open-ended environment to simulate complex social interactions between artificial agents and evaluate their social intelligence. In our environment, agents role-play and interact under a wide variety of scenarios; they coordinate, collaborate, exchange, and compete with each other to achieve complex social goals. We simulate the role-play interaction between LLM-based agents and humans within this task space and evaluate their performance with a holistic evaluation framework called SOTOPIA-Eval. With SOTOPIA, we find significant differences between these models in terms of their social intelligence, and we identify a subset of SOTOPIA scenarios, SOTOPIA-hard, that is generally challenging for all models. We find that on this subset, GPT-4 achieves a significantly lower goal completion rate than humans and struggles to exhibit social commonsense reasoning and strategic communication skills. These findings demonstrate SOTOPIA's promise as a general platform for research on evaluating and improving social intelligence in artificial agents.Comment: Preprint, 43 pages. The first two authors contribute equall

The Impact of Crop Price on Nitrous Oxide Emissions: A Dynamic Programming Approach

The use of N fertilizer in agriculture is a major source of Nitrous Oxide, an important greenhouse gases. Market-based instruments, such as incentives or taxes, may help reduce Nitrous Oxide emission by changing Nitrogen application rate. Using a dynamic programming approach, we found that changing corn price or fertilizer price have effects on both farm profit and Nitrogen application rate. However, farm profit and Nitrogen rate always change in the same direction when affected by either input or output prices. Furthermore, as the corn price is relatively higher than the fertilizer price, changing the corn price is more effective in influencing Nitrogen rate, and thus Nitrous Oxide emission. This analysis can provide policymakers with useful information when designing Market-based tools to help reduce Nitrous Oxide emissions and mitigate global warming