Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conversations to satisfy user needs. In this paper, we embark on an investigation into the utilization of ChatGPT for conversational recommendation, revealing the inadequacy of the existing evaluation protocol. It might over-emphasize the matching with the ground-truth items or utterances generated by human annotators, while neglecting the interactive nature of being a capable CRS. To overcome the limitation, we further propose an interactive Evaluation approach based on LLMs named iEvaLM that harnesses LLM-based user simulators. Our evaluation approach can simulate various interaction scenarios between users and systems. Through the experiments on two publicly available CRS datasets, we demonstrate notable improvements compared to the prevailing evaluation protocol. Furthermore, we emphasize the evaluation of explainability, and ChatGPT showcases persuasive explanation generation for its recommendations. Our study contributes to a deeper comprehension of the untapped potential of LLMs for CRSs and provides a more flexible and easy-to-use evaluation framework for future research endeavors. The codes and data are publicly available at https://github.com/RUCAIBox/iEvaLM-CRS.Comment: Accepted by EMNLP 202

Improving Conversational Recommendation Systems via Counterfactual Data Simulation

Conversational recommender systems (CRSs) aim to provide recommendation services via natural language conversations. Although a number of approaches have been proposed for developing capable CRSs, they typically rely on sufficient training data for training. Since it is difficult to annotate recommendation-oriented dialogue datasets, existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data. To address this issue, in this paper, we propose a CounterFactual data simulation approach for CRS, named CFCRS, to alleviate the issue of data scarcity in CRSs. Our approach is developed based on the framework of counterfactual data augmentation, which gradually incorporates the rewriting to the user preference from a real dialogue without interfering with the entire conversation flow. To develop our approach, we characterize user preference and organize the conversation flow by the entities involved in the dialogue, and design a multi-stage recommendation dialogue simulator based on a conversation flow language model. Under the guidance of the learned user preference and dialogue schema, the flow language model can produce reasonable, coherent conversation flows, which can be further realized into complete dialogues. Based on the simulator, we perform the intervention at the representations of the interacted entities of target users, and design an adversarial training method with a curriculum schedule that can gradually optimize the data augmentation strategy. Extensive experiments show that our approach can consistently boost the performance of several competitive CRSs, and outperform other data augmentation methods, especially when the training data is limited. Our code is publicly available at https://github.com/RUCAIBox/CFCRS.Comment: Accepted by KDD 2023. Code: https://github.com/RUCAIBox/CFCR

Molecular genetic analysis of phosphomannomutase genes in Triticum monococcum

AbstractIn higher plants, phosphomannomutase (PMM) is essential for synthesizing the antioxidant ascorbic acid through the Smirnoff–Wheeler pathway. Previously, we characterized six PMM genes (TaPMM-A1, A2, B1, B2, D1 and D2) in common wheat (Triticum aestivum, AABBDD). Here, we report a molecular genetic analysis of PMM genes in Triticum monococcum (AmAm), a diploid wheat species whose Am genome is closely related to the A genome of common wheat. Two distinct PMM genes, TmPMM-1 and TmPMM-2, were found in T. monococcum. The coding region of TmPMM-1 was intact and highly conserved. In contrast, two main TmPMM-2 alleles were identified, with TmPMM-2a possessing an intact coding sequence and TmPMM-2b being a pseudogene. The transcript level of TmPMM-2a was much higher than that of TmPMM-2b, and a bacterially expressed TmPMM-2a recombinant protein displayed relatively high PMM activity. In general, the total transcript level of PMM was substantially higher in accessions carrying TmPMM-1 and TmPMM-2a than those harboring TmPMM-1 and TmPMM-2b. However, total PMM protein and activity levels did not differ drastically between the two genotypes. This work provides new information on PMM genes in T. monococcum and expands our understanding on Triticeae PMM genes, which may aid further functional and applied studies of PMM in crop plants

Non-Hermitian Magnon-Photon Interference in an Atomic Ensemble

The interference of photons in a lossy beam splitter (BS) exhibits anticoalescence, which is surprising for bosons. Such a non-Hermitian system involving open quantum dynamics is of particular interest for quantum information processing and metrology. The Hermiticity of photonic devices is generally fixed according to the material, but is controllable at the interface of photons and atomic systems. Here, we demonstrate a tunable non-Hermitian BS for the interference between traveling photonic and localized magnonic modes. The crossover from a Hermitian to a non-Hermitian magnon-photon BS is achieved by controlling the coherent and incoherent interaction mediated by the excited levels of atoms, which is reconfigurable via the detuning of a control laser. A correlated interference pattern between the photons and magnons is demonstrated by such a non-Hermitian BS. Our system has the potential to operate with photons and magnons at the single-quanta level, and it provides a versatile quantum interface for studying the non-Hermitian quantum physics and parity-time symmetry

New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review

Lung cancer, the most frequently diagnosed cancer worldwide, is the leading cause of cancer-associated deaths. In recent years, significant progress has been achieved in basic and clinical research concerning the epidermal growth factor receptor (EGFR), and the treatment of lung adenocarcinoma has also entered a new era of individualized, targeted therapies. However, the detection of lung adenocarcinoma is usually invasive. 18F-FDG PET/CT can be used as a noninvasive molecular imaging approach, and radiomics can acquire high-throughput data from standard images. These methods play an increasingly prominent role in diagnosing and treating cancers. Herein, we reviewed the progress in applying 18F-FDG PET/CT and radiomics in lung adenocarcinoma clinical research and how these data are analyzed via traditional statistics, machine learning, and deep learning to predict EGFR mutation status, all of which achieved satisfactory results. Traditional statistics extract features effectively, machine learning achieves higher accuracy with complex algorithms, and deep learning obtains significant results through end-to-end methods. Future research should combine these methods to achieve more accurate predictions, providing reliable evidence for the precision treatment of lung adenocarcinoma. At the same time, facing challenges such as data insufficiency and high algorithm complexity, future researchers must continuously explore and optimize to better apply to clinical practice

WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus

In this paper, we introduce a new NLP task -- generating short factual articles with references for queries by mining supporting evidence from the Web. In this task, called WebBrain, the ultimate goal is to generate a fluent, informative, and factually-correct short article (e.g., a Wikipedia article) for a factual query unseen in Wikipedia. To enable experiments on WebBrain, we construct a large-scale dataset WebBrain-Raw by extracting English Wikipedia articles and their crawlable Wikipedia references. WebBrain-Raw is ten times larger than the previous biggest peer dataset, which can greatly benefit the research community. From WebBrain-Raw, we construct two task-specific datasets: WebBrain-R and WebBrain-G, which are used to train in-domain retriever and generator, respectively. Besides, we empirically analyze the performances of the current state-of-the-art NLP techniques on WebBrain and introduce a new framework ReGen, which enhances the generation factualness by improved evidence retrieval and task-specific pre-training for generation. Experiment results show that ReGen outperforms all baselines in both automatic and human evaluations.Comment: Codes in https://github.com/qhjqhj00/WebBrai

Effect of tea intake on genetic predisposition to gout and uric acid: a Mendelian randomization study

ObjectiveThe effect of tea on gout and uric acid is still controversial. This study aims to analyze the effect of tea intake on genetic predisposition to gout, idiopathic gout, gout due to impairment of renal function as well as uric acid by Mendelian randomization (MR).MethodsForty independent single nucleotide polymorphisms (SNPs) associated with tea intake were selected from UK Biobank. SNPs for uric acid were obtained from BioBank Japan, SNPs for gout were obtained from UK Biobank, and SNPs for gout due to impairment of renal function and idiopathic gout were derived from FinnGen. The causal relationship of exposure-outcome was tested using inverse variance weighted, MR-Egger and weighted median. MR-Egger intercept was employed to assess horizontal pleiotropy, Cochran’s Q test was used to assess heterogeneity, and leave-one-out sensitivity analysis was utilized to analyze the stability of the results.ResultsThe results of MR analysis showed that tea intake was negatively associated with gout due to impairment of renal function (OR 0.997, 95% CI 0.994 to 0.999, P = 0.017), whereas there was no causal association with gout, idiopathic gout, and uric acid (P > 0.05), for which sensitivity analysis suggested that these results were robust.ConclusionsThere was a genetic predisposition effect of increased tea intake on the reduced risk of gout due to impairment of renal function, whereas there was no such effect on gout, idiopathic gout, and uric acid. Tea intake may become an important option in the dietary treatment of gout due to impairment of renal function