Papers by Michael Lyu
On the Reliability of Psychological Scales on Large Language Models (2024.emnlp-main)
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| Challenge: | Recent research has focused on examining Large Language Models’ characteristics from a psychological standpoint, acknowledging the necessity of understanding their behavioral characteristics. |
| Approach: | They propose to examine the reliability of personality tests to LLMs by using psychological scales. |
| Outcome: | The proposed model can represent diverse personalities with specific prompt instructions. |
VD-BERT: A Unified Vision and Dialog Transformer with BERT (2020.emnlp-main)
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| Challenge: | Prior work focused on attention mechanisms to model complex interactions in visual dialog . a new framework for visual dialog is based on pretrained BERT language models . |
| Approach: | They propose a framework for a vision-dialog Transformer that leverages pretrained BERT language models for Visual Dialog tasks. |
| Outcome: | The proposed framework achieves the top position on the visual dialog leaderboard without pretraining on external vision-language data. |
Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation (2021.naacl-main)
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| Challenge: | Existing non-autoregressive machine translation models have shown significant inference speedup but suffer from inferior translation accuracy. |
| Approach: | They propose to use AT as an auxiliary task to transfer AT knowledge to NAT models by knowledge distillation. |
| Outcome: | The proposed method achieves significant improvements over baseline non-Autoregressive machine translation models on WMT14 En-De and WMT16 En-Ro datasets. |
Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings (2020.emnlp-main)
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| Challenge: | Existing studies focus on text modeling, ignoring the rich features embedded in the matching images. |
| Approach: | They propose a novel multi-modal multi-head attention model to capture cross-media interactions and image wordings to bridge the two modalities. |
| Outcome: | The proposed model outperforms the current state of the art based on text modeling and image matching . |
LogicAsker: Evaluating and Improving the Logical Reasoning Ability of Large Language Models (2024.emnlp-main)
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Yuxuan Wan, Wenxuan Wang, Yiliu Yang, Youliang Yuan, Jen-tse Huang, Pinjia He, Wenxiang Jiao, Michael Lyu
| Challenge: | LogicAsker examines and improves the reasoning abilities of large language models such as ChatGPT and GPT-4. |
| Approach: | They propose a set of atomic reasoning skills grounded in propositional and predicate logic to examine and improve the reasoning abilities of large language models such as ChatGPT and GPT-4. |
| Outcome: | The proposed approach improves reasoning abilities in large language models such as ChatGPT and GPT-4 by up to 5%. |
Beyond Embeddings: The Promise of Visual Table in Visual Reasoning (2024.emnlp-main)
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| Challenge: | Visual representation learning has been a cornerstone in computer vision for decades. |
| Approach: | They propose a visual representation tailored for visual reasoning that provides instance-level world knowledge and detailed attributes that are essential for visual reason. |
| Outcome: | The proposed visual tables outperform existing models on 11 visual reasoning benchmarks. |
Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning (2020.findings-emnlp)
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| Challenge: | Sentence function is an important linguistic feature indicating the communicative purpose of a sentence in a conversation. |
| Approach: | They propose a structured meta-learning approach for dialogue generation on infrequent sentence functions. |
| Outcome: | The proposed approach improves informativeness and relevance of dialogue generation on infrequent sentence functions while preserving knowledge generalization for similar sentence functions. |
BinaryBERT: Pushing the Limit of BERT Quantization (2021.acl-long)
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| Challenge: | Recent pre-trained language models have achieved remarkable performance improvement in various tasks, but the improvement generally comes at the cost of increasing model size and computation. |
| Approach: | They propose a binary quantization technique which initializes binaryBERT by splitting from a ternary network. |
| Outcome: | The proposed model achieves state-of-the-art performance on the GLUE and SQUAD benchmarks while being 24x smaller. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading (2020.acl-main)
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Yifan Gao, Chien-Sheng Wu, Shafiq Joty, Caiming Xiong, Richard Socher, Irwin King, Michael Lyu, Steven C.H. Hoi
| Challenge: | Existing approaches to answer user questions are limited in their decision making due to struggles in extracting question-related rules and reasoning about them. |
| Approach: | They propose a conversational machine reading framework that uses a Explicit Memory Tracker to track whether conditions in the rule text have already been satisfied to make a decision. |
| Outcome: | The proposed framework achieves state-of-the-art on the ShARC benchmark and is more interpretable by visualizing the entailment-oriented reasoning process as the conversation flows. |
Data Rejuvenation: Exploiting Inactive Training Examples for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Large-scale training datasets make training neural machine translation models difficult. |
| Approach: | They propose to identify inactive training examples which contribute less to the model performance and introduce data rejuvenation to improve NMT models' training. |
| Outcome: | The proposed framework stabilizes and accelerates the training process of NMT models, resulting in models with better generalization capability. |
Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading (2020.emnlp-main)
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Yifan Gao, Chien-Sheng Wu, Jingjing Li, Shafiq Joty, Steven C.H. Hoi, Caiming Xiong, Irwin King, Michael Lyu
| Challenge: | Document interpretation and dialog understanding are the two major challenges for conversational machine reading. |
| Approach: | They propose a discourse-aware entailment reasoning network to strengthen the connection and enhance the understanding of document and dialog. |
| Outcome: | The proposed model improves document interpretation and dialog understanding on the ShARC benchmark. |
Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models (2024.acl-long)
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| Challenge: | e.g., ChatGPT often provides inappropriate English-culture-related answers when users ask in non-English languages. |
| Approach: | They build a benchmark of concrete and abstract cultural objects to evaluate the cultural dominance issue in large language models. |
| Outcome: | The proposed model can significantly mitigate cultural dominance issue in large language models . the model can provide accurate answers in English, while the model is ethically sound . |
Towards Understanding Neural Machine Translation with Word Importance (D19-1)
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| Challenge: | Neural machine translation (NMT) has advanced the state-of-the-art on various language pairs, but the interpretability of NMT remains unsatisfactory. |
| Approach: | They propose to attribute NMT output to every input word using a gradient-based method to measure word importance. |
| Outcome: | The proposed method is superior on identifying input words with higher influence on translation performance. |
Exploiting Unsupervised Data for Emotion Recognition in Conversations (2020.findings-emnlp)
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| Challenge: | Existing models for Emotion Recognition in Conversations lack supervised data, which prevents them from playing their maximum effect. |
| Approach: | They propose a Conversation Completion task which uses unsupervised conversation data to leverage unsupervised data. |
| Outcome: | The proposed model improves on the minority emotion classes on the ERC datasets. |
Accelerating Code Search with Deep Hashing and Code Classification (2022.acl-long)
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| Challenge: | Code search is to search reusable code snippets from source code corpus based on natural languages queries. |
| Approach: | They propose a method to accelerate code search with deep hashing and code classification by using deep hashes and code hash. |
| Outcome: | The proposed method can save 90% of retrieval time while preserving at least 99% of retrievals accuracy. |
Enhancing Temporal Modeling of Video LLMs via Time Gating (2024.findings-emnlp)
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| Challenge: | Existing Video Large Language Models neglect temporal information in video data, leading to struggles with temporal-aware video understanding. |
| Approach: | They propose a Time Gating Video LLM (TG-Vid) that employs a time gating module to enhance temporal modeling. |
| Outcome: | The proposed model outperforms existing Large Language Models on video-and-language tasks and ablation studies show that the model outpersforms the existing models. |
FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows (2022.emnlp-main)
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| Challenge: | Despite recent progress in dialogue evaluation, how to develop automatic metrics remains an open problem. |
| Approach: | They propose a consensus-based framework for dialog evaluation using segment act flows . they propose to crowdsource a large-scale dataset for it to be evaluated . |
| Outcome: | The proposed framework can reach the best or comparable correlation with human evaluation. |
Making Long-Context Language Models Better Multi-Hop Reasoners (2024.acl-long)
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| Challenge: | Recent advances in long-context modeling have enhanced language models for complex tasks, but they struggle with multi-hop reasoning and noisy contexts. |
| Approach: | They propose an approach that prompts LMs to supply attributions for each assertion during reasoning. |
| Outcome: | The proposed model achieves competitive performance on multi-hop reasoning benchmarks, closely paralleling proprietary LMs such as ChatGPT and Claude-instant. |
Self-Training Sampling with Monolingual Data Uncertainty for Neural Machine Translation (2021.acl-long)
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| Challenge: | Experimental results show that enhancing the learning on uncertain monolingual sentences improves the translation quality of high-uncertainty sentences and also benefits the prediction of low-frequency words at the target side. |
| Approach: | They propose to use monolingual data to augment model training with synthetic parallel data by selecting the most informative monolingual sentences to complement the parallel data. |
| Outcome: | The proposed approach improves the performance of natural language models by selecting the most informative monolingual sentences. |
Photon: A Robust Cross-Domain Text-to-SQL System (2020.acl-demos)
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Jichuan Zeng, Xi Victoria Lin, Steven C.H. Hoi, Richard Socher, Caiming Xiong, Michael Lyu, Irwin King
| Challenge: | Existing natural language interfaces to databases are ambiguous or untranslatable . we present a robust, modular cross-domain text-to-SQL system . |
| Approach: | They propose a system that flags natural language input to which a SQL mapping cannot be immediately determined. |
| Outcome: | The proposed system can flag natural language input to which a SQL mapping cannot be determined. |
Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing studies on self-supervised pretraining for machine translation have focused on the jointly pretrained decoder . |
| Approach: | They propose a method to improve neural machine translation by jointly pretrained decoder . they propose two strategies to remedy the domain and objective discrepancies . |
| Outcome: | The proposed approach improves translation performance and model robustness on three language pairs. |
Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation (2022.emnlp-main)
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| Challenge: | Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text. |
| Approach: | They propose a posterior-based reweighing and noisy training strategy to exploit generated knowledge in dialogue generation. |
| Outcome: | Empirical results show that the proposed methods outperform the state-of-the-art methods in unsupervised knowledge-grounded conversation. |
All Languages Matter: On the Multilingual Safety of LLMs (2024.findings-acl)
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| Challenge: | Existing safety benchmarks only concern the safety in one language, e.g. the majority language in the pretraining data such as English. |
| Approach: | They propose a prompting method to improve multilingual safety of ChatGPT by enhancing cross-lingual generalization of safety alignment. |
| Outcome: | The proposed method can significantly reduce the ratio of unsafe responses by 42% for non-English queries. |
Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training (2022.findings-naacl)
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| Challenge: | Existing studies on keyphrase generation on non-English languages haven’t been vastly investigated. |
| Approach: | They propose a retrieval-augmented method for multilingual keyphrase generation that leverages keyphrase annotations in English datasets to facilitate generating keyphrases in low-resource languages. |
| Outcome: | The proposed model outperforms baselines on non-English keyphrase generation datasets and the proposed model is scalable. |
CLEVA: Chinese Language Models EVAluation Platform (2023.emnlp-demo)
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Yanyang Li, Jianqiao Zhao, Duo Zheng, Zi-Yuan Hu, Zhi Chen, Xiaohui Su, Yongfeng Huang, Shijia Huang, Dahua Lin, Michael Lyu, Liwei Wang
| Challenge: | Large language models (LLMs) have revolutionized natural language processing. |
| Approach: | They propose a Chinese-based platform that assesses Chinese LLMs using a standardized workflow and a unique sampling strategy. |
| Outcome: | CLEVA evaluates Chinese LLMs on a standardized workflow and a competitive leaderboard with minimal coding. |