A Test Collection of Synthetic Documents for Training Rankers:ChatGPT vs. Human Experts

We investigate the usefulness of generative large language models (LLMs) in generating training data for cross-encoder re-rankers in a novel direction: generating synthetic documents instead of synthetic queries. We introduce a new dataset, ChatGPT-RetrievalQA, and compare the effectiveness of strong models fine-tuned on both LLM-generated and human-generated data. We build ChatGPT-RetrievalQA based on an existing dataset, the human ChatGPT comparison corpus (HC3), consisting of multiple public question collections featuring both human- and ChatGPT-generated responses. We fine-tune a range of cross-encoder re-rankers on either human-generated or ChatGPT-generated data. Our evaluation on MS MARCO DEV, TREC DL'19, and TREC DL'20 demonstrates that cross-encoder re-ranking models trained on LLM-generated responses are significantly more effective for out-of-domain re-ranking than those trained on human responses. For in-domain re-ranking, however, the human-trained re-rankers outperform the LLM-trained re-rankers. Our novel findings suggest that generative LLMs have high potential in generating training data for neural retrieval models and can be used to augment training data, especially in domains with less labeled data. ChatGPT-RetrievalQA presents various opportunities for analyzing and improving rankers with both human- and LLM-generated data. Our data, code, and model checkpoints are publicly available.</p

The relation between cardiac 123I-mIBG scintigraphy and functional response 1 year after CRT implantation

Cardiac resynchronization therapy (CRT) is a disease-modifying therapy in patients with chronic heart failure (CHF). Current guidelines ascribe CRT eligibility on three parameters only: left ventricular ejection fraction (LVEF), QRS duration, and New York Heart Association (NYHA) functional class. However, one-third of CHF patients does not benefit from CRT. This study evaluated whether 123I-meta-iodobenzylguanidine (123I-mIBG) assessed cardiac sympathetic activity could optimize CRT patient selection

CLosER: Conversational Legal Longformer with Expertise-Aware Passage Response Ranker for Long Contexts

In this paper, we investigate the task of response ranking in conversational legal search. We propose a novel method for conversational passage response retrieval (ConvPR) for long conversations in domains with mixed levels of expertise. Conversational legal search is challenging because the domain includes long, multi-participant dialogues with domain-specific language. Furthermore, as opposed to other domains, there typically is a large knowledge gap between the questioner (a layperson) and the responders (lawyers), participating in the same conversation. We collect and release a large-scale real-world dataset called LegalConv with nearly one million legal conversations from a legal community question answering (CQA) platform. We address the particular challenges of processing legal conversations, with our novel Conversational Legal Longformer with Expertise-Aware Response Ranker, called CLosER. The proposed method has two main innovations compared to state-of-the-art methods for ConvPR: (i) Expertise-Aware Post-Training; a learning objective that takes into account the knowledge gap difference between participants to the conversation; and (ii) a simple but effective strategy for re-ordering the context utterances in long conversations to overcome the limitations of the sparse attention mechanism of the Longformer architecture. Evaluation on LegalConv shows that our proposed method substantially and significantly outperforms existing state-of-the-art models on the response selection task. Our analysis indicates that our Expertise-Aware Post-Training, i.e., continued pre-training or domain/task adaptation, plays an important role in the achieved effectiveness. Our proposed method is generalizable to other tasks with domain-specific challenges and can facilitate future research on conversational search in other domains.</p

Radial velocities from Gaia BP/RP spectra

The Gaia mission has provided us full astrometric solutions for over $1.5$B sources. However, only the brightest 34M of those have radial velocity measurements. As a proof of concept, this paper aims to close that gap, by obtaining radial velocity estimates from the low-resolution BP/RP spectra that Gaia now provides. These spectra are currently published for about 220M sources, with this number increasing to the full $\sim 2$B Gaia sources with Gaia Data Release 4. To obtain the radial velocity measurements, we fit Gaia BP/RP spectra with models based on a grid of synthetic spectra, with which we obtain the posterior probability on the radial velocity for each object. Our measured velocities show systematic biases that depend mainly on colours and magnitudes of stars. We correct for these effects by using external catalogues of radial velocity measurements. We present in this work a catalogue of about $6.4$M sources with our most reliable radial velocity measurements and uncertainties $<300$ km s$^{-1}$ obtained from the BP/RP spectra. About 23% of these have no previous radial velocity measurement in Gaia RVS. Furthermore, we provide an extended catalogue containing all 125M sources for which we were able to obtain radial velocity measurements. The latter catalogue, however, also contains a fraction of measurements for which the reported radial velocities and uncertainties are inaccurate. Although typical uncertainties in the catalogue are significantly higher compared to those obtained with precision spectroscopy instruments, the number of potential sources for which this method can be applied is orders of magnitude higher than any previous radial velocity catalogue. Further development of the analysis could therefore prove extremely valuable in our understanding of Galactic dynamics.Comment: 14 pages, 17 figures, submitted to A&A, comments welcom

Dietary Fat Intake and the Risk of Depression: The SUN Project

Emerging evidence relates some nutritional factors to depression risk. However, there is a scarcity of longitudinal assessments on this relationship. Objective: To evaluate the association between fatty acid intake or the use of culinary fats and depression incidence in a Mediterranean population. Material and Methods: Prospective cohort study (1999–2010) of 12,059 Spanish university graduates (mean age: 37.5 years) initially free of depression with permanently open enrolment. At baseline, a 136-item validated food frequency questionnaire was used to estimate the intake of fatty acids (saturated fatty acids (SFA), polyunsaturated fatty acids (PUFA), trans unsaturated fatty acids (TFA) and monounsaturated fatty acids (MUFA) and culinary fats (olive oil, seed oils, butter and margarine) During follow-up participants were classified as incident cases of depression if they reported a new clinical diagnosis of depression by a physician and/or initiated the use of antidepressant drugs. Cox regression models were used to calculate Hazard Ratios (HR) of incident depression and their 95% confidence intervals (CI) for successive quintiles of fats. Results: During follow-up (median: 6.1 years), 657 new cases of depression were identified. Multivariable-adjusted HR (95% CI) for depression incidence across successive quintiles of TFA intake were: 1 (ref), 1.08 (0.82–1.43), 1.17 (0.88–1.53), 1.28 (0.97–1.68), 1.42 (1.09–1.84) with a significant dose-response relationship (p for trend = 0.003). Results did not substantially change after adjusting for potential lifestyle or dietary confounders, including adherence to a Mediterranean Dietary Pattern. On the other hand, an inverse and significant dose-response relationship was obtained for MUFA (p for trend = 0.05) and PUFA (p for trend = 0.03) intake. Conclusions: A detrimental relationship was found between TFA intake and depression risk, whereas weak inverse associations were found for MUFA, PUFA and olive oil. These findings suggest that cardiovascular disease and depression may share some common nutritional determinants related to subtypes of fat intake