97 citations · 444 across the 41 of their papers we have counts for
43 papers
Probing Semantic Alignment, Lexical Invariance, and Syntactic Influence in LLM Metaphor Processing
Fengying Ye, Shanshan Wang, Lidia S. Chao +1
Large language models (LLMs) achieve strong performance on metaphor detection and interpretation tasks, yet it remains unclear what such behavioral success reveals about metaphor p…
SGIC: A Self-Guided Iterative Calibration Framework for RAG
Guanhua Chen, Yutong Yao, Lidia S. Chao +2
Recent research in retrieval-augmented generation (RAG) has concentrated on retrieving useful information from candidate documents. However, numerous methodologies frequently negle…
LLMCL-GEC: Advancing Grammatical Error Correction with LLM-Driven Curriculum Learning
Tao Fang, Derek F. Wong, Lusheng Zhang +5
While large-scale language models (LLMs) have demonstrated remarkable capabilities in specific natural language processing (NLP) tasks, they may still lack proficiency compared to…
DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios
Junchao Wu, Runzhe Zhan, Derek F. Wong +4
Detecting text generated by large language models (LLMs) is of great recent interest. With zero-shot methods like DetectGPT, detection capabilities have reached impressive levels.…
VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning
Jingkun Ma, Runzhe Zhan, Yang Li +4
A hallmark of advanced artificial intelligence is the capacity to progress from passive visual perception to the strategic modification of visual information to facilitate complex…
What is the Best Way for ChatGPT to Translate Poetry?
Shanshan Wang, Derek F. Wong, Jingming Yao +1
Machine translation (MT) has historically faced significant challenges when applied to literary works, particularly in the domain of poetry translation. The advent of Large Languag…
FOCUS: Forging Originality through Contrastive Use in Self-Plagiarism for Language Models
Kaixin Lan, Tao Fang, Derek F. Wong +3
Pre-trained Language Models (PLMs) have shown impressive results in various Natural Language Generation (NLG) tasks, such as powering chatbots and generating stories. However, an e…
A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU
Guanhua Chen, Yutong Yao, Derek F. Wong +1
Multi-intent natural language understanding (NLU) presents a formidable challenge due to the model confusion arising from multiple intents within a single utterance. While previous…
Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model
Runzhe Zhan, Xinyi Yang, Derek F. Wong +2
While supervised fine-tuning (SFT) has been a straightforward approach for tailoring the output of foundation large language model (LLM) to specific preferences, concerns have been…
Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model
Haoyun Xu, Runzhe Zhan, Derek F. Wong +1
Large Language Models (LLMs) are composed of neurons that exhibit various behaviors and roles, which become increasingly diversified as models scale. Recent studies have revealed t…
Human-in-the-loop Machine Translation with Large Language Model
Xinyi Yang, Runzhe Zhan, Derek F. Wong +2
The large language model (LLM) has garnered significant attention due to its in-context learning mechanisms and emergent capabilities. The research community has conducted several…
A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions
Junchao Wu, Shu Yang, Runzhe Zhan +3
The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at…
How Does Pretraining Improve Discourse-Aware Translation?
Zhihong Huang, Longyue Wang, Siyou Liu +1
Pretrained language models (PLMs) have produced substantial improvements in discourse-aware neural machine translation (NMT), for example, improved coherence in spoken language tra…
Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization
Chi Seng Cheang, Hou Pong Chan, Derek F. Wong +5
Recent pre-trained language models (PLMs) achieve promising results in existing abstractive summarization datasets. However, existing summarization benchmarks overlap in time with…
Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluation
Tao Fang, Shu Yang, Kaixin Lan +4
ChatGPT, a large-scale language model based on the advanced GPT-3.5 architecture, has shown remarkable potential in various Natural Language Processing (NLP) tasks. However, there…
Toward Human-Like Evaluation for Natural Language Generation with Error Analysis
Qingyu Lu, Liang Ding, Liping Xie +3
The state-of-the-art language model-based automatic metrics, e.g. BARTScore, benefiting from large-scale contextualized pre-training, have been successfully used in a wide range of…
ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation
Zhaocong Li, Xuebo Liu, Derek F. Wong +2
Transfer learning is a simple and powerful method that can be used to boost model performance of low-resource neural machine translation (NMT). Existing transfer learning methods f…
Alibaba-Translate China's Submission for WMT 2022 Metrics Shared Task
Yu Wan, Keqin Bao, Dayiheng Liu +5
In this report, we present our submission to the WMT 2022 Metrics Shared Task. We build our system based on the core idea of UNITE (Unified Translation Evaluation), which unifies s…
Attention Mechanism with Energy-Friendly Operations
Yu Wan, Baosong Yang, Dayiheng Liu +5
Attention mechanism has become the dominant module in natural language processing models. It is computationally intensive and depends on massive power-hungry multiplications. In th…
RoBLEURT Submission for the WMT2021 Metrics Task
Yu Wan, Dayiheng Liu, Baosong Yang +6
In this paper, we present our submission to Shared Metrics Task: RoBLEURT (Robustly Optimizing the training of BLEURT). After investigating the recent advances of trainable metrics…
UniTE: Unified Translation Evaluation
Yu Wan, Dayiheng Liu, Baosong Yang +4
Translation quality evaluation plays a crucial role in machine translation. According to the input format, it is mainly separated into three tasks, i.e., reference-only, source-onl…
Variance-Aware Machine Translation Test Sets
Runzhe Zhan, Xuebo Liu, Derek F. Wong +1
We release 70 small and discriminative test sets for machine translation (MT) evaluation called variance-aware test sets (VAT), covering 35 translation directions from WMT16 to WMT…
On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation
Xuebo Liu, Longyue Wang, Derek F. Wong +4
Pre-training (PT) and back-translation (BT) are two simple and powerful methods to utilize monolingual data for improving the model performance of neural machine translation (NMT).…
Difficulty-Aware Machine Translation Evaluation
Runzhe Zhan, Xuebo Liu, Derek F. Wong +1
The high-quality translation results produced by machine translation (MT) systems still pose a huge challenge for automatic evaluation. Current MT evaluation pays the same attentio…