42 research outputs found
Knowledge is Power: Understanding Causality Makes Legal judgment Prediction Models More Generalizable and Robust
Legal judgment Prediction (LJP), aiming to predict a judgment based on fact
descriptions, serves as legal assistance to mitigate the great work burden of
limited legal practitioners. Most existing methods apply various large-scale
pre-trained language models (PLMs) finetuned in LJP tasks to obtain consistent
improvements. However, we discover the fact that the state-of-the-art (SOTA)
model makes judgment predictions according to wrong (or non-casual)
information, which not only weakens the model's generalization capability but
also results in severe social problems like discrimination. Here, we analyze
the causal mechanism misleading the LJP model to learn the spurious
correlations, and then propose a framework to guide the model to learn the
underlying causality knowledge in the legal texts. Specifically, we first
perform open information extraction (OIE) to refine the text having a high
proportion of causal information, according to which we generate a new set of
data. Then, we design a model learning the weights of the refined data and the
raw data for LJP model training. The extensive experimental results show that
our model is more generalizable and robust than the baselines and achieves a
new SOTA performance on two commonly used legal-specific datasets
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads
LSM-trees are widely adopted as the storage backend of key-value stores.
However, optimizing the system performance under dynamic workloads has not been
sufficiently studied or evaluated in previous work. To fill the gap, we present
RusKey, a key-value store with the following new features: (1) RusKey is a
first attempt to orchestrate LSM-tree structures online to enable robust
performance under the context of dynamic workloads; (2) RusKey is the first
study to use Reinforcement Learning (RL) to guide LSM-tree transformations; (3)
RusKey includes a new LSM-tree design, named FLSM-tree, for an efficient
transition between different compaction policies -- the bottleneck of dynamic
key-value stores. We justify the superiority of the new design with theoretical
analysis; (4) RusKey requires no prior workload knowledge for system
adjustment, in contrast to state-of-the-art techniques. Experiments show that
RusKey exhibits strong performance robustness in diverse workloads, achieving
up to 4x better end-to-end performance than the RocksDB system under various
settings.Comment: 25 pages, 13 figure
Sub-Character Tokenization for Chinese Pretrained Language Models
Tokenization is fundamental to pretrained language models (PLMs). Existing
tokenization methods for Chinese PLMs typically treat each character as an
indivisible token. However, they ignore the unique feature of the Chinese
writing system where additional linguistic information exists below the
character level, i.e., at the sub-character level. To utilize such information,
we propose sub-character (SubChar for short) tokenization. Specifically, we
first encode the input text by converting each Chinese character into a short
sequence based on its glyph or pronunciation, and then construct the vocabulary
based on the encoded text with sub-word tokenization. Experimental results show
that SubChar tokenizers have two main advantages over existing tokenizers: 1)
They can tokenize inputs into much shorter sequences, thus improving the
computational efficiency. 2) Pronunciation-based SubChar tokenizers can encode
Chinese homophones into the same transliteration sequences and produce the same
tokenization output, hence being robust to all homophone typos. At the same
time, models trained with SubChar tokenizers perform competitively on
downstream tasks. We release our code at
https://github.com/thunlp/SubCharTokenization to facilitate future work.Comment: This draft supersedes the previous version named "SHUOWEN-JIEZI:
Linguistically Informed Tokenizers For Chinese Language Model Pretraining
An unprecedented synergy of high-temperature tensile strength and ductility in a NiCoCrAlTi high-entropy alloy
The present work reported a novel L12-strengthening NiCoCrAlTi high entropy
alloy (HEA) with an outstanding synergy of tensile strength and ductility at
both ambient and high temperatures. Transmission electron microscopy (TEM)
characterization revealed a high density of rod-like and spheroidal L12
precipitates distributing in the micro/nanograins and non-recrystallized
regions in the annealed specimens. The tremendously high yield stress, ultimate
tensile stress (UTS), and ductility of the HEA at 600 C were ~1060 MPa, 1271
MPa, and 25%, respectively, which were significantly superior to most reported
HEAs and Co- and Ni-based superalloys to date. Systematic TEM analysis unveiled
that the cooperation among L12 precipitation, extensive stacking faults (SFs),
deformation twins (DTs), immobile Lomer-Cottrell (L-C) locks formed from
interactions between SFs and SFs/DTs, hierarchical SFs/DTs networks, as well as
hetero-deformation-induced strengthening dominated the plastic deformation at
600 C. Such a unique deformation mechanism enabled extremely high tensile
strength and sustained ductility of the HEA at a high temperature
Single molybdenum center supported on N-doped black phosphorus as an efficient electrocatalyst for nitrogen fixation
Ammonia (NH3) is one of the most significant industrial chemical products due to its wide applications in various fields. However, the production of NH3 from the electrochemical nitrogen (N-2) reduction reaction (NRR) under ambient conditions is one of the most important issues that remain challenging for chemists. Herein, the candidacy of a series of molybdenum (Mo)-based single-atom catalysts (SACs) supported on N-doped black phosphorus (BP) as the electrocatalyst for the NRR has been evaluated by means of density functional theory (DFT) calculations. In particular, Mo1N3 has been found to chemically adsorb N-2, and it exhibits the highest catalytic activity toward the NRR with an ultralow overpotential of 0.02 V via the associative distal mechanism, indicative of catalyzing the NRR under ambient conditions. Additionally, Mo1N3 shows the fast removal of the produced NH3 with a free energy uphill of only 0.56 eV and good stability of NRR intermediates. Moreover, the Mo-based SACs were demonstrated to be more selective to the NRR over the competing hydrogen evolution reaction (HER) process. These excellent features render Mo1N3 on BP as a compelling highly efficient and durable catalyst for electrochemical N-2 fixation. Our results provide a rational paradigm for catalytic nitrogen fixation by SACs in two-dimensional (2D) materials under ambient conditions