Publications

2024

1. NAACL 2024

   Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences

   Hongyi Liu, Qingyun Wang, Payam Karisani, and 1 more author

   In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

   Abs arXiv

   Named entity recognition is a key component of Information Extraction (IE), particularly in scientific domains such as biomedicine and chemistry, where large language models (LLMs), e.g., ChatGPT, fall short. We investigate the applicability of transfer learning for enhancing a named entity recognition model trained in the biomedical domain (the source domain) to be used in the chemical domain (the target domain). A common practice for training such a model in a few-shot learning setting is to pretrain the model on the labeled source data, and then, to finetune it on a hand-full of labeled target examples. In our experiments, we observed that such a model is prone to mislabeling the source entities, which can often appear in the text, as the target entities. To alleviate this problem, we propose a model to transfer the knowledge from the source domain to the target domain, but, at the same time, to project the source entities and target entities into separate regions of the feature space. This diminishes the risk of mislabeling the source entities as the target entities. Our model consists of two stages: 1) entity grouping in the source domain, which incorporates knowledge from annotated events to establish relations between entities, and 2) entity discrimination in the target domain, which relies on pseudo labeling and contrastive learning to enhance discrimination between the entities in the two domains. We conduct our extensive experiments across three source and three target datasets, demonstrating that our method outperforms the baselines by up to 5% absolute value. Code, data, and resources are publicly available for research purposes: https://github.com/Lhtie/Bio-Domain-Transfer .

2. ICML 2024

   Open-Domain Text Evaluation via Contrastive Distribution Methods

   Sidi Lu, Hongyi Liu, Asli Celikyilmaz, and 2 more authors

   In Forty-first International Conference on Machine Learning, 2024

   Abs arXiv

   Recent advancements in open-domain text generation, driven by the power of large pre-trained language models (LLMs), have demonstrated remarkable performance. However, assessing these models’ generation quality remains a challenge. In this paper, we introduce a novel method for evaluating open-domain text generation called Contrastive Distribution Methods (CDM). Leveraging the connection between increasing model parameters and enhanced LLM performance, CDM creates a mapping from the contrast of two probabilistic distributions – one known to be superior to the other – to quality measures. We investigate CDM for open-domain text generation evaluation under two paradigms: 1) Generative CDM, which harnesses the contrast of two language models’ distributions to generate synthetic examples for training discriminator-based metrics; 2) Discriminative CDM, which directly uses distribution disparities between two language models for evaluation. Our experiments on coherence evaluation for multi-turn dialogue and commonsense evaluation for controllable generation demonstrate CDM’s superior correlate with human judgment than existing automatic evaluation metrics, highlighting the strong performance and generalizability of our approach.

3. NAACL 2024

   Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis

   Wenhao Zhu, Hongyi Liu, Qingxiu Dong, and 5 more authors

   2024

   Abs arXiv

   Large language models (LLMs) have demonstrated remarkable potential in handling multilingual machine translation (MMT). In this paper, we systematically investigate the advantages and challenges of LLMs for MMT by answering two questions: 1) How well do LLMs perform in translating massive languages? 2) Which factors affect LLMs’ performance in translation? We thoroughly evaluate eight popular LLMs, including ChatGPT and GPT-4. Our empirical results show that translation capabilities of LLMs are continually improving. GPT-4 has beat the strong supervised baseline NLLB in 40.91% of translation directions but still faces a large gap towards the commercial translation system, especially on low-resource languages. Through further analysis, we discover that LLMs exhibit new working patterns when used for MMT. First, instruction semantics can surprisingly be ignored when given in-context exemplars. Second, cross-lingual exemplars can provide better task guidance for low-resource translation than exemplars in the same language pairs. Third, LLM can acquire translation ability in a resource-efficient way and generate moderate translation even on zero-resource languages.

2022

1. Go-tuning: Improving Zero-shot Learning Abilities of Smaller Language Models

   Jingjing Xu, Qingxiu Dong, Hongyi Liu, and 1 more author

   2022

   Abs arXiv

   With increasing scale, large language models demonstrate both quantitative improvement and new qualitative capabilities, especially as zero-shot learners, like GPT-3. However, these results rely heavily on delicate prompt design and large computation. In this work, we explore whether the strong zero-shot ability could be achieved at a smaller model scale without any external supervised data. To achieve this goal, we revisit masked language modeling and present a geometry-guided self-supervised learning method (Go-tuningfor short) by taking a small number of task-aware self-supervised data to update language models further. Experiments show that Go-tuning can enable T5-small (80M) competitive zero-shot results compared with large language models, such as T5-XL (3B). We also apply Go-tuning on multi-task settings and develop a multi-task model, mgo-T5 (250M). It can reach the average performance of OPT (175B) on 9 datasets.