Papers by Derek Wong
Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model (2024.findings-acl)
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| Challenge: | supervised fine-tuning (SFT) has been a straightforward approach for tailoring the output of foundation large language models (LLMs) to specific preferences. |
| Approach: | They propose a training-free alignment method that uses minimal prior tokens to bridge the foundation LLM and the SFT LLM. |
| Outcome: | The proposed method achieves comparable performance without training on machine translation and part-of-speech tagging across seven languages. |
Attention Mechanism with Energy-Friendly Operations (2022.findings-acl)
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| Challenge: | Empirical results show that attention mechanism can be improved from the energy consumption aspects. |
| Approach: | They propose to replace multiplications with either selective operations or additions to reduce energy consumption. |
| Outcome: | The proposed model achieves competitable accuracy while saving 99% and 66% energy during alignment calculation and the whole attention procedure. |
Anchor-based Large Language Models (2024.findings-acl)
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| Challenge: | Large language models (LLMs) require massive GPU memory due to their size and parameter count. |
| Approach: | They propose to use anchor-based self-attention network and anchor-basic inference strategy to compress sequence information into an anchor token, reducing the keys/values cache and enhancing inference efficiency. |
| Outcome: | The proposed model reduces the key/value cache and improves inference efficiency by 99% while maintaining similar accuracy levels. |
Large Language Model for Multi-Domain Translation: Benchmarking and Domain CoT Fine-tuning (2024.findings-emnlp)
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| Challenge: | Achieving consistent high-quality machine translation across diverse domains remains a challenge due to limited and imbalanced parallel training data available in various domains. |
| Approach: | They propose a domain Chain of Thought technique that uses the multi-domain intelligence of LLMs to improve translation performance. |
| Outcome: | The proposed method achieves significant improvements in translation accuracy and domain robustness over traditional fine-tuning on a small dataset of four domains. |
AnyTrans: Translate AnyText in the Image with Large Scale Models (2024.findings-emnlp)
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| Challenge: | Recent advances in natural language processing and computer vision have made it possible to translate images with text in one language into equivalent images displaying that text translated into another language. |
| Approach: | They propose an all-encompassing framework for the task–In-Image Machine Translation (IIMT) that incorporates contextual cues from both textual and visual elements during translation. |
| Outcome: | The proposed framework can be constructed using open-source models and requires no training, making it highly accessible and expandable. |
Can LLMs Learn Uncertainty on Their Own? Expressing Uncertainty Effectively in A Self-Training Manner (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) exhibit excessive, random, and uninformative uncertainty rendering them unsuitable for decision-making in human-computer interactions. |
| Approach: | They propose an uncertainty-aware instruction tuning method that aligns LLMs’ perception with the probabilistic uncertainty of the generation. |
| Outcome: | The proposed method improves LLMs' performance by 45.2%, with reasonably good out-of-domain generalization capabilities. |
What is the Best Way for ChatGPT to Translate Poetry? (2024.acl-long)
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| Challenge: | Despite promising results, our analysis reveals persistent issues in the translations generated by ChatGPT that warrant attention. |
| Approach: | They propose an Explanation-Assisted Poetry Machine Translation method which leverages monolingual poetry explanation as a guiding information for the translation process. |
| Outcome: | The proposed method outperforms traditional translation methods of ChatGPT and the existing online systems in English-Chinese poetry translation. |
Domain-Aware k-Nearest-Neighbor Knowledge Distillation for Machine Translation (2024.findings-acl)
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| Challenge: | Existing methods to transfer knowledge from kNN datastore into new models are expensive and arbitrarily transfer knowledge. |
| Approach: | They propose a domain-aware method which filters out domain-relevant neighborhood knowledge for learning in the distillation process. |
| Outcome: | The proposed method achieves state-of-the-art on four domain translation tasks. |
Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization (2023.emnlp-main)
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| Challenge: | Existing summarization benchmarks overlap in time with pre-training corpora and fine-tuning datasets. |
| Approach: | They propose a temporal generalization benchmark that contains data samples from 2010 to 2022 to understand the temporal ability of abstractive summarization models. |
| Outcome: | The proposed benchmark analyzes data samples from 2010 to 2022 to understand the temporal generalization ability of abstractive summarization models. |
Benchmarking and Improving Long-Text Translation with Large Language Models (2024.findings-acl)
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Longyue Wang, Zefeng Du, Wenxiang Jiao, Chenyang Lyu, Jianhui Pang, Leyang Cui, Kaiqiang Song, Derek Wong, Shuming Shi, Zhaopeng Tu
| Challenge: | Recent studies have illuminated the promising capabilities of large language models (LLMs) in handling long texts. |
| Approach: | They construct a benchmark dataset specifically designed for the finetuning and evaluation of large language models (LLMs) they compare LLMs with MT models and find they exhibit shortcomings in long-text domains . |
| Outcome: | The proposed model performs better in long-text translation, and its performance diminishes as document size increases. |
CoEvol: Constructing Better Responses for Instruction Finetuning through Multi-Agent Cooperation (2024.emnlp-main)
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| Challenge: | Recent studies have focused on constructing substantial quantities of IFT data with minimal human effort. |
| Approach: | They propose a multi-agent cooperation framework for the improvement of IFT responses for large language models using a debate-advise-edit-judge paradigm. |
| Outcome: | The proposed framework outperforms baseline models on unseen tasks and shows that it can improve instruction-following capabilities on large language models. |
UniTE: Unified Translation Evaluation (2022.acl-long)
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| Challenge: | Recent methods for evaluation of translation quality are focused on one task, ignoring commonalities . |
| Approach: | They propose a unified framework engaged with abilities to handle all three evaluation tasks. |
| Outcome: | The proposed framework can universally surpass state-of-the-art or winner methods across tasks. |
Towards Demonstration-Aware Large Language Models for Machine Translation (2024.findings-acl)
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| Challenge: | Large language models for machine translation often face difficulties in leveraging demonstrations to further improve their performance. |
| Approach: | They propose a novel approach that integrates demonstration-aware training and inference strategies within the framework of tuning-based LTMs. |
| Outcome: | The proposed model integrates demonstration-aware training and inference strategies within tuning-based LTMs. |
FOCUS: Forging Originality through Contrastive Use in Self-Plagiarism for Language Models (2024.findings-acl)
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| Challenge: | Existing methods to generate original text using pre-trained language models are problematic as they are trained on corpora constructed by human authors. |
| Approach: | They propose a unique “self-plagiarism” contrastive decoding strategy that modifies prompts in LLMs to develop an amateur model and a professional model. |
| Outcome: | The proposed method enables the development of an amateur model and a professional model while maintaining its standard language model status. |