Auto Search Indexer for End-to-End Document Retrieval

Generative retrieval, which is a new advanced paradigm for document retrieval, has recently attracted research interests, since it encodes all documents into the model and directly generates the retrieved documents. However, its power is still underutilized since it heavily relies on the "preprocessed" document identifiers (docids), thus limiting its retrieval performance and ability to retrieve new documents. In this paper, we propose a novel fully end-to-end retrieval paradigm. It can not only end-to-end learn the best docids for existing and new documents automatically via a semantic indexing module, but also perform end-to-end document retrieval via an encoder-decoder-based generative model, namely Auto Search Indexer (ASI). Besides, we design a reparameterization mechanism to combine the above two modules into a joint optimization framework. Extensive experimental results demonstrate the superiority of our model over advanced baselines on both public and industrial datasets and also verify the ability to deal with new documents.Comment: EMNLP 202

Calibrating LLM-Based Evaluator

Recent advancements in large language models (LLMs) on language modeling and emergent capabilities make them a promising reference-free evaluator of natural language generation quality, and a competent alternative to human evaluation. However, hindered by the closed-source or high computational demand to host and tune, there is a lack of practice to further calibrate an off-the-shelf LLM-based evaluator towards better human alignment. In this work, we propose AutoCalibrate, a multi-stage, gradient-free approach to automatically calibrate and align an LLM-based evaluator toward human preference. Instead of explicitly modeling human preferences, we first implicitly encompass them within a set of human labels. Then, an initial set of scoring criteria is drafted by the language model itself, leveraging in-context learning on different few-shot examples. To further calibrate this set of criteria, we select the best performers and re-draft them with self-refinement. Our experiments on multiple text quality evaluation datasets illustrate a significant improvement in correlation with expert evaluation through calibration. Our comprehensive qualitative analysis conveys insightful intuitions and observations on the essence of effective scoring criteria.Comment: 22 pages,11 figure

Intermediate-mass black holes and the fundamental plane of black hole accretion

We present new 5 GHz VLA observations of a sample of 8 active intermediate-mass black holes with masses $10^{4.9} < M < 10^{6.1}\ M_{\odot}$ found in galaxies with stellar masses $M_{*} < 3 \times 10^{9}\ M_{\odot}$. We detected 5 of the 8 sources at high significance. Of the detections, 4 were consistent with a point source, and one (SDSS J095418.15+471725.1, with black hole mass $M < 10^{5}\ M_{\odot}$) clearly shows extended emission that has a jet morphology. Combining our new radio data with the black hole masses and literature X-ray measurements, we put the sources on the fundamental plane of black hole accretion. We find that the extent to which the sources agree with the fundamental plane depends on their star-forming/composite/AGN classification based on optical narrow emission line ratios. he single star-forming source is inconsistent with the fundamental plane. The three composite sources are consistent, and three of the four AGN sources are inconsistent with the fundamental plane. We argue that this inconsistency is genuine and not a result of misattributing star-formation to black hole activity. Instead, we identify the sources in our sample that have AGN-like optical emission line ratios as not following the fundamental plane and thus caution the use of the fundamental plane to estimate masses without additional constraints, such as radio spectral index, radiative efficiency, or the Eddington fraction.Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society. 9 pages, 2 figures. Images can be accessed in fits format from https://doi.org/10.7302/3100-6e6

Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning

Learning-based Adaptive Bit Rate~(ABR) method, aiming to learn outstanding strategies without any presumptions, has become one of the research hotspots for adaptive streaming. However, it typically suffers from several issues, i.e., low sample efficiency and lack of awareness of the video quality information. In this paper, we propose Comyco, a video quality-aware ABR approach that enormously improves the learning-based methods by tackling the above issues. Comyco trains the policy via imitating expert trajectories given by the instant solver, which can not only avoid redundant exploration but also make better use of the collected samples. Meanwhile, Comyco attempts to pick the chunk with higher perceptual video qualities rather than video bitrates. To achieve this, we construct Comyco's neural network architecture, video datasets and QoE metrics with video quality features. Using trace-driven and real-world experiments, we demonstrate significant improvements of Comyco's sample efficiency in comparison to prior work, with 1700x improvements in terms of the number of samples required and 16x improvements on training time required. Moreover, results illustrate that Comyco outperforms previously proposed methods, with the improvements on average QoE of 7.5% - 16.79%. Especially, Comyco also surpasses state-of-the-art approach Pensieve by 7.37% on average video quality under the same rebuffering time.Comment: ACM Multimedia 201