186 research outputs found
Zero-Shot Cross-Lingual Summarization via Large Language Models
Given a document in a source language, cross-lingual summarization (CLS) aims
to generate a summary in a different target language. Recently, the emergence
of Large Language Models (LLMs), such as GPT-3.5, ChatGPT and GPT-4, has
attracted wide attention from the computational linguistics community. However,
it is not yet known the performance of LLMs on CLS. In this report, we
empirically use various prompts to guide LLMs to perform zero-shot CLS from
different paradigms (i.e., end-to-end and pipeline), and provide a preliminary
evaluation on the generated summaries. We find that ChatGPT and GPT-4
originally prefer to produce lengthy summaries with detailed information. These
two LLMs can further balance informativeness and conciseness with the help of
an interactive prompt, significantly improving their CLS performance.
Experimental results on three widely-used CLS datasets show that GPT-4 achieves
state-of-the-art zero-shot CLS performance, and performs competitively compared
with the fine-tuned mBART-50. Moreover, we also find some multi-lingual and
bilingual LLMs (i.e., BLOOMZ, ChatGLM-6B, Vicuna-13B and ChatYuan) have limited
zero-shot CLS ability. Due to the composite nature of CLS, which requires
models to perform summarization and translation simultaneously, accomplishing
this task in a zero-shot manner is even a challenge for LLMs. Therefore, we
sincerely hope and recommend future LLM research could use CLS as a testbed.Comment: Technical Report, 11 page
Snowman: A Million-scale Chinese Commonsense Knowledge Graph Distilled from Foundation Model
Constructing commonsense knowledge graphs (CKGs) has attracted wide research
attention due to its significant importance in cognitive intelligence.
Nevertheless, existing CKGs are typically oriented to English, limiting the
research in non-English languages. Meanwhile, the emergence of foundation
models like ChatGPT and GPT-4 has shown promising intelligence with the help of
reinforcement learning from human feedback. Under the background, in this
paper, we utilize foundation models to construct a Chinese CKG, named Snowman.
Specifically, we distill different types of commonsense head items from
ChatGPT, and continue to use it to collect tail items with respect to the head
items and pre-defined relations. Based on the preliminary analysis, we find the
negative commonsense knowledge distilled by ChatGPT achieves lower human
acceptance compared to other knowledge. Therefore, we design a simple yet
effective self-instruct filtering strategy to filter out invalid negative
commonsense. Overall, the constructed Snowman covers more than ten million
Chinese commonsense triples, making it the largest Chinese CKG. Moreover, human
studies show the acceptance of Snowman achieves 90.6\%, indicating the
high-quality triples distilled by the cutting-edge foundation model. We also
conduct experiments on commonsense knowledge models to show the usability and
effectiveness of our Snowman.Comment: tech repor
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation
Sequential Recommendation (SR) has received increasing attention due to its
ability to capture user dynamic preferences. Recently, Contrastive Learning
(CL) provides an effective approach for sequential recommendation by learning
invariance from different views of an input. However, most existing data or
model augmentation methods may destroy semantic sequential interaction
characteristics and often rely on the hand-crafted property of their
contrastive view-generation strategies. In this paper, we propose a
Meta-optimized Seq2Seq Generator and Contrastive Learning (Meta-SGCL) for
sequential recommendation, which applies the meta-optimized two-step training
strategy to adaptive generate contrastive views. Specifically, Meta-SGCL first
introduces a simple yet effective augmentation method called
Sequence-to-Sequence (Seq2Seq) generator, which treats the Variational
AutoEncoders (VAE) as the view generator and can constitute contrastive views
while preserving the original sequence's semantics. Next, the model employs a
meta-optimized two-step training strategy, which aims to adaptively generate
contrastive views without relying on manually designed view-generation
techniques. Finally, we evaluate our proposed method Meta-SGCL using three
public real-world datasets. Compared with the state-of-the-art methods, our
experimental results demonstrate the effectiveness of our model and the code is
available
Is ChatGPT a Good NLG Evaluator? A Preliminary Study
Recently, the emergence of ChatGPT has attracted wide attention from the
computational linguistics community. Many prior studies have shown that ChatGPT
achieves remarkable performance on various NLP tasks in terms of automatic
evaluation metrics. However, the ability of ChatGPT to serve as an evaluation
metric is still underexplored. Considering assessing the quality of natural
language generation (NLG) models is an arduous task and NLG metrics notoriously
show their poor correlation with human judgments, we wonder whether ChatGPT is
a good NLG evaluation metric. In this report, we provide a preliminary
meta-evaluation on ChatGPT to show its reliability as an NLG metric. In detail,
we regard ChatGPT as a human evaluator and give task-specific (e.g.,
summarization) and aspect-specific (e.g., relevance) instruction to prompt
ChatGPT to evaluate the generated results of NLG models. We conduct experiments
on five NLG meta-evaluation datasets (including summarization, story generation
and data-to-text tasks). Experimental results show that compared with previous
automatic metrics, ChatGPT achieves state-of-the-art or competitive correlation
with human judgments in most cases. In addition, we find that the effectiveness
of the ChatGPT evaluator might be influenced by the creation method of the
meta-evaluation datasets. For the meta-evaluation datasets which are created
greatly depending on the reference and thus are biased, the ChatGPT evaluator
might lose its effectiveness. We hope our preliminary study could prompt the
emergence of a general-purposed reliable NLG metric.Comment: Both first authors contributed equally. Technical Report, 11 pages.
Accepted to the 4th New Frontiers in Summarization Workshop (NewSumm@EMNLP
2023
Distant supervision for neural relation extraction integrated with word attention and property features
Distant supervision for neural relation extraction is an efficient approach to extracting massive relations with reference to plain texts. However, the existing neural methods fail to capture the critical words in sentence encoding and meanwhile lack useful sentence information for some positive training instances. To address the above issues, we propose a novel neural relation extraction model. First, we develop a word-level attention mechanism to distinguish the importance of each individual word in a sentence, increasing the attention weights for those critical words. Second, we investigate the semantic information from word embeddings of target entities, which can be developed as a supplementary feature for the extractor. Experimental results show that our model outperforms previous state-of-the-art baselines
Uniform Stability of a Class of Fractional-Order Nonautonomous Systems with Multiple Time Delays
In mathematics, to a large extent, control theory addresses the stability of solutions of differential equations, which can describe the behavior of dynamic systems. In this paper, a class of fractional-order nonautonomous systems with multiple time delays modeled by differential equations is considered. A
sufficient condition is established for the existence and uniqueness of solutions for such systems involving Caputo fractional derivative, and the uniform stability of solution is studied. At last, two examples are given to demonstrate the applicability of our results
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