Search Results for author: Hongyuan Lu

Found 15 papers, 4 papers with code

On Controlling Fallback Responses for Grounded Dialogue Generation

no code implementations Findings (ACL) 2022 Hongyuan Lu, Wai Lam, Hong Cheng, Helen Meng

We propose a novel framework that automatically generates a control token with the generator to bias the succeeding response towards informativeness for answerable contexts and fallback for unanswerable contexts in an end-to-end manner.

Dialogue Generation Informativeness

Unveiling the Generalization Power of Fine-Tuned Large Language Models

1 code implementation14 Mar 2024 Haoran Yang, Yumeng Zhang, Jiaqi Xu, Hongyuan Lu, Pheng Ann Heng, Wai Lam

While Large Language Models (LLMs) have demonstrated exceptional multitasking abilities, fine-tuning these models on downstream, domain-specific datasets is often necessary to yield superior performance on test sets compared to their counterparts without fine-tuning.

In-Context Learning

Consecutive Model Editing with Batch alongside HooK Layers

no code implementations8 Mar 2024 Shuaiyi Li, Yang Deng, Deng Cai, Hongyuan Lu, Liang Chen, Wai Lam

As the typical retraining paradigm is unacceptably time- and resource-consuming, researchers are turning to model editing in order to seek an effective, consecutive, and batch-supportive way to edit the model behavior directly.

Model Editing

TPE: Towards Better Compositional Reasoning over Conceptual Tools with Multi-persona Collaboration

no code implementations28 Sep 2023 Hongru Wang, Huimin Wang, Lingzhi Wang, Minda Hu, Rui Wang, Boyang Xue, Hongyuan Lu, Fei Mi, Kam-Fai Wong

Large language models (LLMs) have demonstrated exceptional performance in planning the use of various functional tools, such as calculators and retrievers, particularly in question-answering tasks.

Question Answering Response Generation

Not All Metrics Are Guilty: Improving NLG Evaluation with LLM Paraphrasing

1 code implementation24 May 2023 Tianyi Tang, Hongyuan Lu, Yuchen Eleanor Jiang, Haoyang Huang, Dongdong Zhang, Wayne Xin Zhao, Furu Wei

Most research about natural language generation (NLG) relies on evaluation benchmarks with limited references for a sample, which may result in poor correlations with human judgements.

Machine Translation nlg evaluation +2

Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models

1 code implementation17 May 2023 Hanxu Hu, Hongyuan Lu, Huajian Zhang, Yun-Ze Song, Wai Lam, Yue Zhang

To this end, we propose a novel method called CoS (Chain-of-Symbol Prompting) that represents the complex environments with condensed symbolic spatial representations during the chained intermediate thinking steps.

Chain-of-Dictionary Prompting Elicits Translation in Large Language Models

no code implementations11 May 2023 Hongyuan Lu, Haoyang Huang, Dongdong Zhang, Haoran Yang, Wai Lam, Furu Wei

Large language models (LLMs) have shown surprisingly good performance in multilingual neural machine translation (MNMT) even when trained without parallel data.

In-Context Learning Machine Translation +1

Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models

no code implementations15 Dec 2022 Hongyuan Lu, Haoyang Huang, Shuming Ma, Dongdong Zhang, Wai Lam, Furu Wei

Despite the success of multilingual sequence-to-sequence pre-training, most existing approaches rely on document-level monolingual corpora in many different languages, sentence-level bilingual corpora,\footnote{In this paper, we use `bilingual corpora' to denote parallel corpora with `bilingual translation pairs' in many different language pairs, each consisting of two sentences/documents with the same meaning written in different languages.

Abstractive Text Summarization Cross-Lingual Abstractive Summarization +4

Revamping Multilingual Agreement Bidirectionally via Switched Back-translation for Multilingual Neural Machine Translation

no code implementations28 Sep 2022 Hongyuan Lu, Haoyang Huang, Shuming Ma, Dongdong Zhang, Furu Wei, Wai Lam

Despite the fact that multilingual agreement (MA) has shown its importance for multilingual neural machine translation (MNMT), current methodologies in the field have two shortages: (i) require parallel data between multiple language pairs, which is not always realistic and (ii) optimize the agreement in an ambiguous direction, which hampers the translation performance.

Document Level Machine Translation Document Translation +2

PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation

1 code implementation17 Aug 2022 Hongyuan Lu, Wai Lam

This paper presents \textbf{PCC}: \textbf{P}araphrasing with Bottom-k Sampling and \textbf{C}yclic Learning for \textbf{C}urriculum Data Augmentation, a novel CDA framework via paraphrasing, which exploits the textual paraphrase similarity as the curriculum difficulty measure.

Data Augmentation Dialogue Generation +3

Partner Personas Generation for Diverse Dialogue Generation

no code implementations27 Nov 2021 Hongyuan Lu, Wai Lam, Hong Cheng, Helen M. Meng

We incorporate reinforcement learning with a dedicatedly designed critic network for reward judgement.

Dialogue Generation Response Generation

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