Publications

2024

1. ECML-PKDD 2024

   A Unified Data Augmentation Framework for Low-Resource Multi-Domain Dialogue Generation

   Yongkang Liu*, Ercong Nie*, Shi Feng, Zheng Hua, Zifeng Ding, and 3 more authors

   In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Sep 2024

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   Current state-of-the-art dialogue systems heavily rely on extensive training datasets. However, challenges arise in domains where domain-specific training datasets are insufficient or entirely absent. To tackle this challenge, we propose a novel data Augmentation framework for Multi-Domain Dialogue Generation, referred to as AMD2G. The AMD2G framework consists of a data augmentation process and a two-stage training approach: domain-agnostic training and domain adaptation training. We posit that domain corpora are a blend of domain-agnostic and domain-specific features, with certain representation patterns shared among diverse domains. Domain-agnostic training aims to enable models to learn these common expressive patterns. To construct domain-agnostic dialogue corpora, we employ a de-domaining data processing technique used to remove domain-specific features. By mitigating the impact of domain-specific features, the model trained on the de-domained corpora can effectively learn shared expressive patterns across various domains. Subsequently, we adapt the learned domain-agnostic features to the target domain through domain adaptation training. We conduct experiments on Chinese dialogue datasets from five different domains, demonstrating that AMD2G achieves superior performance compared to both direct training on the target domain corpus and training on all five domain corpora collectively. Our work underscores AMD2G as a viable alternative solution for low-resource multi-domain dialogue generation.

2. ACL 2024

   GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Network

   Shuzhou Yuan, Ercong Nie, Michael Färber, Helmut Schmid, and Hinrich Schütze

   In Findings of the Association for Computational Linguistics: ACL 2024 Aug 2024

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   Large Language Models (LLMs) exhibit strong In-Context Learning (ICL) capabilities when prompts with demonstrations are applied to them. However, fine-tuning still remains crucial to further enhance their adaptability. Prompt-based fine-tuning proves to be an effective fine-tuning method in low-data scenarios, but high demands on computing resources limit its practicality. We address this issue by introducing a prompt-based parameter-efficient fine-tuning (PEFT) approach. GNNavi leverages insights into ICL’s information flow dynamics, which indicates that label words act in prompts as anchors for information propagation. GNNavi employs a Graph Neural Network (GNN) layer to precisely guide the aggregation and distribution of information flow during the processing of prompts by hardwiring the desired information flow into the GNN. Our experiments on text classification tasks with GPT-2 and Llama2 shows GNNavi surpasses standard prompt-based fine-tuning methods in few-shot settings by updating just 0.2% to 0.5% of parameters. We compare GNNavi with prevalent PEFT approaches, such as prefix tuning, LoRA and Adapter in terms of performance and efficiency. Our analysis reveals that GNNavi enhances information flow and ensures a clear aggregation process.

3. SemEval@NAACL 2024

   Team MGTD4ADL at SemEval-2024 Task 8: Leveraging (Sentence) Transformer Models with Contrastive Learning for Identifying Machine-Generated Text

   Huixin Chen, Jan Büssing, David Rügamer, and Ercong Nie†

   In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024) Jun 2024

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   This paper outlines our approach to SemEval-2024 Task 8 (Subtask B), which focuses on discerning machine-generated text from human-written content, while also identifying the text sources, i.e., from which Large Language Model (LLM) the target text is generated. Our detection system is built upon Transformer-based techniques, leveraging various pre-trained language models (PLMs), including sentence transformer models. Additionally, we incorporate Contrastive Learning (CL) into the classifier to improve the detecting capabilities and employ Data Augmentation methods. Ultimately, our system achieves a peak accuracy of 76.96% on the test set of the competition, configured using a sentence transformer model integrated with CL methodology.

4. LREC-COLING 2024

   Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models using Minimal Pairs

   Linyang He, Peilin Chen, Ercong Nie, Yuanning Li, and Jonathan R. Brennan

   In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation May 2024

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   Inspired by cognitive neuroscience studies, we introduce a novel ‘decoding probing’ method that uses minimal pairs benchmark (BLiMP) to probe internal linguistic characteristics in neural language models layer by layer. By treating the language model as the brain and its representations as ’neural activations’, we decode grammaticality labels of minimal pairs from the intermediate layers’ representations. This approach reveals: 1) Self-supervised language models capture abstract linguistic structures in intermediate layers that GloVe and RNN language models cannot learn. 2) Information about syntactic grammaticality is robustly captured through the first third layers of GPT-2 and also distributed in later layers. As sentence complexity increases, more layers are required for learning grammatical capabilities. 3) Morphological and semantics/syntax interface-related features are harder to capture than syntax, which also aligns with cognitive neuroscience studies. 4) For Transformer-based models, both embeddings and attentions capture grammatical features but show distinct patterns. Different attention heads exhibit similar tendencies toward various linguistic phenomena, but with varied contributions. Notably, specific heads consistently offer the most contributions.

5. EACL 2024

   ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks

   Bolei Ma*, Ercong Nie*, Shuzhou Yuan, Helmut Schmid, Färber Michael, and 2 more authors

   In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics Mar 2024

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   Prompt-based methods have been successfully applied to multilingual pretrained language models for zero-shot cross-lingual understanding. However, most previous studies primarily focused on sentence-level classification tasks, and only a few considered token-level labeling tasks such as Named Entity Recognition (NER) and Part-of-Speech (POS) tagging. In this paper, we propose Token-Level Prompt Decomposition (ToPro), which facilitates the prompt-based method for token-level sequence labeling tasks. The ToPro method decomposes an input sentence into single tokens and applies one prompt template to each token. Our experiments on multilingual NER and POS tagging datasets demonstrate that ToPro-based fine-tuning outperforms Vanilla fine-tuning and Prompt-Tuning in zero-shot cross-lingual transfer, especially for languages that are typologically different from the source language English. Our method also attains state-of-the-art performance when employed with the mT5 model. Besides, our exploratory study in multilingual large language models shows that ToPro performs much better than the current in-context learning method. Overall, the performance improvements show that ToPro could potentially serve as a novel and simple benchmarking method for sequence labeling tasks.

6. preprint

   Decomposed Prompting: Unveiling Multilingual Linguistic Structure Knowledge in English-Centric Large Language Models

   Ercong Nie, Shuzhou Yuan, Bolei Ma, Helmut Schmid, Michael Färber, and 2 more authors

   preprint Mar 2024

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   Despite the predominance of English in their training data, English-centric Large Language Models (LLMs) like GPT-3 and LLaMA display a remarkable ability to perform multilingual tasks, raising questions about the depth and nature of their cross-lingual capabilities. This paper introduces the decomposed prompting approach to probe the linguistic structure understanding of these LLMs in sequence labeling tasks. Diverging from the single text-to-text prompt, our method generates for each token of the input sentence an individual prompt which asks for its linguistic label. We assess our method on the Universal Dependencies part-of-speech tagging dataset for 38 languages, utilizing both English-centric and multilingual LLMs. Our findings show that decomposed prompting surpasses the iterative prompting baseline in efficacy and efficiency under zero- and few-shot settings. Further analysis reveals the influence of evaluation methods and the use of instructions in prompts. Our multilingual investigation shows that English-centric language models perform better on average than multilingual models. Our study offers insights into the multilingual transferability of English-centric LLMs, contributing to the understanding of their multilingual linguistic knowledge.

7. preprint

   Why Lift so Heavy? Slimming Large Language Models by Cutting Off the Layers

   Shuzhou Yuan*, Ercong Nie*, Bolei Ma, and Michael Färber

   preprint Feb 2024

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   Large Language Models (LLMs) possess outstanding capabilities in addressing various natural language processing (NLP) tasks. However, the sheer size of these models poses challenges in terms of storage, training and inference due to the inclusion of billions of parameters through layer stacking. While traditional approaches such as model pruning or distillation offer ways for reducing model size, they often come at the expense of performance retention. In our investigation, we systematically explore the approach of reducing the number of layers in LLMs. Surprisingly, we observe that even with fewer layers, LLMs maintain similar or better performance levels, particularly in prompt-based fine-tuning for text classification tasks. Remarkably, in certain cases, models with a single layer outperform their fully layered counterparts. These findings offer valuable insights for future work aimed at mitigating the size constraints of LLMs while preserving their performance, thereby opening avenues for significantly more efficient use of LLMs.

2023

1. Instruction@NeurIPS 2023

   From Classification to Generation: Insights into Crosslingual Retrieval Augmented ICL

   Xiaoqian Li, Ercong Nie, and Sheng Liang

   In NeurIPS 2023 Workshop on Instruction Tuning and Instruction Following Dec 2023

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   The remarkable ability of Large Language Models (LLMs) to understand and follow instructions has sometimes been limited by their in-context learning (ICL) performance in low-resource languages. To address this, we introduce a novel approach that leverages cross-lingual retrieval-augmented in-context learning (CREA-ICL). By extracting semantically similar prompts from high-resource languages, we aim to improve the zero-shot performance of multilingual pre-trained language models (MPLMs) across diverse tasks. Though our approach yields steady improvements in classification tasks, it faces challenges in generation tasks, with Bangla serving as a key case study. Our evaluation offers insights into the performance dynamics of retrieval-augmented in-context learning across both classification and generation domains.

2. EMNLP 2023

   Unleashing the Multilingual Encoder Potential: Boosting Zero-Shot Performance via Probability Calibration

   Ercong Nie, Helmut Schmid, and Hinrich Schütze

   In Findings of the Association for Computational Linguistics: EMNLP 2023 Dec 2023

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   Pretraiend multilingual encoder models can directly perform zero-shot multilingual tasks or linguistic probing by reformulating the input examples into cloze-style prompts. This is accomplished by predicting the probabilities of the label words at the masked token position, without requiring any updates to the model parameters. However, the performance of this pattern is limited by the model’s bias toward predicting label words which frequently occurred during the pretraining. These words typically receive high probabilities. To address this issue, we combine the models with various calibration techniques which modify the probabilities of label words predicted by the models. We evaluate the effectiveness of these calibration methods on monolingual encoders as well as multilingual encoders. Across a diverse range of tasks, we achieve substantial performance gains through calibration. Furthermore, with only very few training samples, the trained calibration parameters are able to yield additional enhancements.

3. BLP@EMNLP 2023

   Crosslingual Retrieval Augmented In-context Learning for Bangla

   Xiaoqian Li, Ercong Nie, and Sheng Liang

   In Proceedings of the First Workshop on Bangla Language Processing Dec 2023

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   The promise of Large Language Models (LLMs) in Natural Language Processing has often been overshadowed by their limited performance in low-resource languages such as Bangla. To address this, our paper presents a pioneering approach that utilizes cross-lingual retrieval augmented in-context learning. By strategically sourcing semantically similar prompts from high-resource language, we enable multilingual pretrained language models (MPLMs), especially the generative model BLOOMZ, to successfully boost performance on Bangla tasks. Our extensive evaluation highlights that the cross-lingual retrieval augmented prompts bring steady improvements to MPLMs over the zero-shot performance.

4. CMCL@CoNLL 2023

   Baby’s CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models

   Zheyu Zhang*, Han Yang*, Bolei Ma*, David Rügamer, and Ercong Nie†

   In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics Dec 2023

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   Large Language Models (LLMs) demonstrate remarkable performance on a variety of natural language understanding (NLU) tasks, primarily due to their in-context learning ability. This ability could be applied to building babylike models, i.e. models at small scales, improving training efficiency. In this paper, we propose a ’CoThought’ pipeline, which efficiently trains smaller ’baby’ language models (BabyLMs) by leveraging the Chain of Thought prompting of LLMs. Our pipeline restructures a dataset of less than 100M in size using GPT-3.5-turbo, transforming it into task-oriented, human-readable texts that are comparable to the school texts for language learners. The BabyLM is then pretrained on this restructured dataset in a RoBERTa fashion. In evaluations across 4 benchmarks, our BabyLM outperforms the vanilla RoBERTa in 10 linguistic, NLU, and question-answering tasks by more than 3 points, showing a superior ability to extract contextual information. These results suggest that compact LMs pretrained on small, LLM-restructured data can better understand tasks and achieve improved performance.

5. KONVENS 2023

   Is Prompt-Based Finetuning Always Better than Vanilla Finetuning? Insights from Cross-Lingual Language Understanding

   Bolei Ma, Ercong Nie*, Helmut Schmid, and Hinrich Schuetze

   In Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023) Sep 2023

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   Multilingual pretrained language models (MPLMs) have demonstrated substantial performance improvements in zero-shot cross-lingual transfer across various natural language understanding tasks by finetuning MPLMs on taskspecific labelled data of a source language (e.g. English) and evaluating on a wide range of target languages. Recent studies show that prompt-based finetuning surpasses regular finetuning in few-shot scenarios. However, the exploration of prompt-based learning in multilingual tasks remains limited. In this study, we propose the PROFIT pipeline to investigate the cross-lingual capabilities of Promptbased Finetuning. We conduct comprehensive experiments on diverse cross-lingual language understanding tasks (sentiment classification, paraphrase identification, and natural language inference) and empirically analyze the variation trends of prompt-based finetuning performance in cross-lingual transfer across different few-shot and full-data settings. Our results reveal the effectiveness and versatility of promptbased finetuning in cross-lingual language understanding. Our findings indicate that promptbased finetuning outperforms vanilla finetuning in full-data scenarios and exhibits greater advantages in few-shot scenarios, with different performance patterns dependent on task types. Additionally, we analyze underlying factors such as language similarity and pretraining data size that impact the cross-lingual performance of prompt-based finetuning. Overall, our work provides valuable insights into the cross-lingual prowess of prompt-based finetuning.

6. ALP@RANLP 2023

   Cross-Lingual Constituency Parsing for Middle High German: A Delexicalized Approach

   Ercong Nie, Helmut Schmid, and Hinrich Schütze

   In Proceedings of the Ancient Language Processing Workshop Sep 2023

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   Constituency parsing plays a fundamental role in advancing natural language processing (NLP) tasks. However, training an automatic syntactic analysis system for ancient languages solely relying on annotated parse data is a formidable task due to the inherent challenges in building treebanks for such languages. It demands extensive linguistic expertise, leading to a scarcity of available resources. To overcome this hurdle, cross-lingual transfer techniques which require minimal or even no annotated data for low-resource target languages offer a promising solution. In this study, we focus on building a constituency parser for Middle High German (MHG) under realistic conditions, where no annotated MHG treebank is available for training. In our approach, we leverage the linguistic continuity and structural similarity between MHG and Modern German (MG), along with the abundance of MG treebank resources. Specifically, by employing the delexicalization method, we train a constituency parser on MG parse datasets and perform cross-lingual transfer to MHG parsing. Our delexicalized constituency parser demonstrates remarkable performance on the MHG test set, achieving an F1-score of 67.3%. It outperforms the best zero-shot cross-lingual baseline by a margin of 28.6% points. These encouraging results underscore the practicality and potential for automatic syntactic analysis in other ancient languages that face similar challenges as MHG.

7. ACL 2023

   Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages

   Ercong Nie*, Sheng Liang*, Helmut Schmid, and Hinrich Schütze

   In Findings of the Association for Computational Linguistics: ACL 2023 Jul 2023

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   Multilingual Pretrained Language Models (MPLMs) perform strongly in cross-lingual transfer. We propose Prompts Augmented by Retrieval Crosslingually (PARC) to improve zero-shot performance on low-resource languages (LRLs) by augmenting the context with prompts consisting of semantically similar sentences retrieved from a high-resource language (HRL). PARC improves zero-shot performance on three downstream tasks (sentiment classification, topic categorization, natural language inference) with multilingual parallel test sets across 10 LRLs covering 6 language families in unlabeled (+5.1%) and labeled settings (+16.3%). PARC also outperforms finetuning by 3.7%. We find a significant positive correlation between cross-lingual transfer performance on one side, and the similarity between high- and low-resource languages as well as the amount of low-resource pretraining data on the other side. A robustness analysis suggests that PARC has the potential to achieve even stronger performance with more powerful MPLMs.

2022

1. LMRL@NeurIPS 2022

   What cleaves? Is proteasomal cleavage prediction reaching a ceiling?

   Ingo Ziegler, Bolei Ma, Ercong Nie, Bernd Bischl, David Rügamer, and 2 more authors

   In NeurIPS 2022 Workshop on Learning Meaningful Representations of Life Dec 2022

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   Epitope vaccines are a promising direction to enable precision treatment for cancer, autoimmune diseases, and allergies. Effectively designing such vaccines requires accurate prediction of proteasomal cleavage in order to ensure that the epitopes in the vaccine are presented to T cells by the major histocompatibility complex (MHC). While direct identification of proteasomal cleavage \emphin vitro is cumbersome and low throughput, it is possible to implicitly infer cleavage events from the termini of MHC-presented epitopes, which can be detected in large amounts thanks to recent advances in high-throughput MHC ligandomics. Inferring cleavage events in such a way provides an inherently noisy signal which can be tackled with new developments in the field of deep learning that supposedly make it possible to learn predictors from noisy labels. Inspired by such innovations, we sought to modernize proteasomal cleavage predictors by benchmarking a wide range of recent methods, including LSTMs, transformers, CNNs, and denoising methods, on a recently introduced cleavage dataset. We found that increasing model scale and complexity appeared to deliver limited performance gains, as several methods reached about 88.5% AUC on C-terminal and 79.5% AUC on N-terminal cleavage prediction. This suggests that the noise and/or complexity of proteasomal cleavage and the subsequent biological processes of the antigen processing pathway are the major limiting factors for predictive performance rather than the specific modeling approach used. While biological complexity can be tackled by more data and better models, noise and randomness inherently limit the maximum achievable predictive performance. All our datasets and experiments are available at https://github.com/ziegler-ingo/cleavage_prediction.