Papers by Yung-Sung Chuang
Lifelong Language Knowledge Distillation (2020.emnlp-main)
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| Challenge: | Existing methods to perform lifelong language learning (LLL) on stream of different tasks are challenging . Existing models face catastrophic forgetting problem, which can be mitigated by lifelong learning . |
| Approach: | They propose a method that can be easily applied to existing LLL architectures to mitigate degradation. |
| Outcome: | The proposed method improves state-of-the-art models and reduces degradation compared to multi-task models. |
Search Augmented Instruction Learning (2023.findings-emnlp)
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Hongyin Luo, Tianhua Zhang, Yung-Sung Chuang, Yuan Gong, Yoon Kim, Xixin Wu, Helen Meng, James Glass
| Challenge: | Large language models (LLMs) have been significantly improved by instruction fine-tuning, but still lack transparency and the ability to utilize up-to-date knowledge and information. |
| Approach: | They propose a search-augmented instruction learning model which grounds the language generation and instruction following abilities on complex search results generated by in-house and external search engines. |
| Outcome: | The proposed model outperforms plain LLMs on zero-shot language tasks and can generate both natural and programming languages following natural language guidance and requests. |
Found in the middle: Calibrating Positional Attention Bias Improves Long Context Utilization (2024.findings-acl)
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Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li, Zifeng Wang, Long Le, Abhishek Kumar, James Glass, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister
| Challenge: | Large language models struggle to capture relevant information located in the middle of their input. |
| Approach: | They propose a calibration mechanism that allows the model to attend to contexts faithfully according to their relevance even when they are in the middle. |
| Outcome: | The proposed calibration mechanism mitigates this positional bias and improves retrieval-augmented generation performance. |
Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points . dense retrievers are limited by their inability to perform semantic matching for relevant passages that have low lexical overlap with the query. |
| Approach: | They propose a query expansion and reranking approach for improving passage retrieval with the application to open-domain question answering. |
| Outcome: | Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points when compared to a vanilla query expansion model and a dense retrieval model. |
Natural Language Embedded Programs for Hybrid Language Symbolic Reasoning (2024.findings-naacl)
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Tianhua Zhang, Jiaxin Ge, Hongyin Luo, Yung-Sung Chuang, Mingye Gao, Yuan Gong, Yoon Kim, Xixin Wu, Helen Meng, James Glass
| Challenge: | Existing methods for surfacing symbolic reasoning capabilities are limited to narrow tasks . arithmetic computations are unnatural to perform in pure language space, and hence present difficulties for LLMs. |
| Approach: | They propose a natural language embedded program framework for solving symbolic reasoning tasks. |
| Outcome: | The proposed framework improves on strong baselines across math and symbolic reasoning, text classification, question answering, and instruction following tasks. |
DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings (2022.naacl-main)
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Yung-Sung Chuang, Rumen Dangovski, Hongyin Luo, Yang Zhang, Shiyu Chang, Marin Soljacic, Shang-Wen Li, Scott Yih, Yoon Kim, James Glass
| Challenge: | Recent work shows that finetuning pretrained models with contrastive learning makes it possible to learn good sentence embeddings without labeled data. |
| Approach: | They propose an unsupervised contrastive learning framework for learning sentence embeddings . they use a masked language model to mask out the edited sentence . |
| Outcome: | The proposed framework outperforms SimCSE on semantic textual similarity tasks by 2.3 absolute points. |
Joint Inference of Retrieval and Generation for Passage Re-ranking (2024.findings-eacl)
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| Challenge: | Existing methods for re-ranking documents are sparse and do not require training. |
| Approach: | They propose a method that optimizes mutual information between query and passage distributions by integrating cross-encoders and generative models in the re-ranking process. |
| Outcome: | The proposed method outperforms conventional re-rankers and language model scorers in open-domain QA retrieval settings and diverse retrieval benchmarks under zero-shot settings. |
Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation (D19-62)
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| Challenge: | Using machine learning to interpret large amounts of data can be over-whelming for clinicians. |
| Approach: | They propose to use PubMed 200k RCT sentence classification dataset to generate RCT conclusion generation task. |
| Outcome: | The proposed model improves quality and correctness in generated conclusions compared to baseline model . the proposed model is not suitable for all RCTs, but it could be improved . |
Dual Inference for Improving Language Understanding and Generation (2020.findings-emnlp)
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| Challenge: | Existing studies have exploited the duality of the task pairs in machine translation and speech recognition. |
| Approach: | They propose to leverage the duality in the inference stage without retraining whole models. |
| Outcome: | The proposed method is effective in both NLU and NLG tasks, providing the great potential of practical use. |
Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translation (2021.findings-acl)
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| Challenge: | Using connectionist temporal classification (CTC) for speech-to-text translation is counter-intuitive due to its monotonicity assumption. |
| Approach: | They propose to build a non-autoregressive speech-to-text translation model using connectionist temporal classification (CTC) their work shows transformer encoders can change the word order and points out the future research direction that needs to be explored more on non-Autoregressives speech translation. |
| Outcome: | The proposed model improves translation performance by using transformer encoders. |
Recent Advances in Pre-trained Language Models: Why Do They Work and How Do They Work (2022.aacl-tutorials)
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| Challenge: | Pre-trained language models are language models that are pre-taught on large-scaled corpora in a self-supervised fashion. |
| Approach: | This tutorial provides a broad and comprehensive introduction to pre-trained language models . it focuses on emerging methods that enable PLMs to perform diverse downstream tasks . |
| Outcome: | This tutorial focuses on the benefits of pre-trained language models and how to use them in NLP tasks. |
SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation (2024.naacl-long)
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Abe Hou, Jingyu Zhang, Tianxing He, Yichen Wang, Yung-Sung Chuang, Hongwei Wang, Lingfeng Shen, Benjamin Van Durme, Daniel Khashabi, Yulia Tsvetkov
| Challenge: | Existing watermarked generation algorithms employ token-level designs and are vulnerable to paraphrase attacks. |
| Approach: | They propose a sentence-level watermarking algorithm that uses locality-sensitive hashing to partition the semantic space of sentences. |
| Outcome: | The proposed algorithm is more robust than the existing state-of-the-art method on paraphrasers and domains, while posing only minor degradations to SemStamp. |
Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps (2024.emnlp-main)
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| Challenge: | Despite the utility and impressive capabilities of large language models, their tendency to generate hallucinations presents a significant challenge in their deployment. |
| Approach: | They propose a simple hallucination detection model based on the ratio of attention weights on the context versus newly generated tokens. |
| Outcome: | The proposed model reduces the amount of hallucinations by 9.6% in a summarization task. |