89 research outputs found
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
found in galaxies with stellar masses . 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 ) 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
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