GenAI4ELab/papercli-papers huggingface dataset:
Tracking Universal Features Through Fine-Tuning and Model Merging
Authors: Niels Nielsen Horn; Desmond Elliott
Venue: NAACL 2025 (Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025))

Abstract: We study how features emerge, disappear, and persist across models fine-tuned on different domains of text. More specifically, we start from a base one-layer Transformer language model that is trained on a combination of the BabyLM corpus, and a collection of Python code from The Stack. This base model is adapted to two new domains of text: TinyStories, and the Lua programming language, respectively; and then these two models are merged using these two models using spherical linear interpolation. Our exploration aims to provide deeper insights into the stability and transformation of features across typical transfer-learning scenarios using small-scale models and sparse auto-encoders.
Prompt Tuning Can Simply Adapt Large Language Models to Text Encoders
Authors: Kaiyan Zhao; Qiyu Wu; Zhongtao Miao; Yoshimasa Tsuruoka
Venue: NAACL 2025 (Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025))

Abstract: Recently, many works have been attempting to adapt Large Language Models (LLMs) for sentence embedding, with most of them fine-tuning LLMs towards the contrastive objective and enabling bi-directional attention for better performance, using LoRA to address the large model scale.In this work, we suggest that this adaptation can also be simply and effectively achieved using causal attention and with even fewer trainable parameters through soft prompt tuning, as an alternative to fine-tuning with LoRA and other methods with extra post-training tasks.Our method only optimizes a few learnable tokens while keeping the rest of the model frozen.Through experiments on a diverse set of evaluation tasks, we find that simply tuning only a few tokens can achieve a competitive performance with that of fine-tuning with LoRA. The percentage of trainable parameters can be reduced to less than 0.001%. Moreover, we also demonstrate that turning causal attention to bi-directional attention with or without extra post-training tasks does not provide additional benefit when soft prompt tuning is applied, suggesting that causal attention can be naturally used in decoder-only LLMs for sentence embedding adaptation.
Cross-Modal Learning for Music-to-Music-Video Description Generation
Authors: Zhuoyuan Mao; Mengjie Zhao; Qiyu Wu; Zhi Zhong; Wei-Hsiang Liao; Hiromi Wakaki; Yuki Mitsufuji
Venue: NAACL 2025 (Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025))

Abstract: Music-to-music-video generation is a challenging task due to the intrinsic differences between the music and video modalities. The advent of powerful text-to-video diffusion models has opened a promising pathway for music-video (MV) generation by first addressing the music-to-MV description task and subsequently leveraging these models for video generation. In this study, we focus on the MV description generation task and propose a comprehensive pipeline encompassing training data construction and multimodal model fine-tuning. We fine-tune existing pre-trained multimodal models on our newly constructed music-to-MV description dataset based on the Music4All dataset, which integrates both musical and visual information. Our experimental results demonstrate that music representations can be effectively mapped to textual domains, enabling the generation of meaningful MV description directly from music inputs. We also identify key components in the dataset construction pipeline that critically impact the quality of MV description and highlight specific musical attributes that warrant greater focus for improved MV description generation.
A Comparative Study of Learning Paradigms in Large Language Models via Intrinsic Dimension
Authors: Saahith Janapati; Yangfeng Ji
Venue: NAACL 2025 (Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025))

Abstract: The performance of Large Language Models (LLMs) on natural language tasks can be improved through both supervised fine-tuning (SFT) and in-context learning (ICL), which operate via distinct mechanisms. SFT updates the model’s weights by minimizing loss on training data, whereas ICL leverages task demonstrations embedded in the prompt, without changing the model’s parameters. This study investigates the effects of these learning paradigms on the hidden representations of LLMs using Intrinsic Dimension (ID). We use ID to estimate the number of degrees of freedom between representations extracted from LLMs as they perform specific natural language tasks. We first explore how the ID of LLM representations evolves during SFT and how it varies due to the number of demonstrations in ICL. We then compare the IDs induced by SFT and ICL and find that ICL consistently induces a higher ID compared to SFT, suggesting that representations generated during ICL reside in higher dimensional manifolds in the embedding space.
Choose Your Words Wisely: Domain-adaptive Masking Makes Language Models Learn Faster
Authors: Vanshpreet S. Kohli; Aaron Monis; Radhika Mamidi
Venue: NAACL 2025 (Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025))

Abstract: Foundational Language Models perform significantly better on downstream tasks in specialised domains (such as law, computer science, and medical science) upon being further pre-trained on extensive domain-specific corpora, but this continual pre-training incurs heavy computational costs. Indeed, some of the most performant specialised language models such as BioBERT incur even higher computing costs during domain-specific training than the pre-training cost of the foundational models they are initialised from. In this paper, we argue that much of the extended pre-training is redundant, with models seemingly wasting valuable resources re-learning lexical and semantic patterns already well-represented in their foundational models such as BERT, T5 and GPT. Focusing on Masked Language Models, we introduce a novel domain-specific masking strategy that is designed to facilitate continual learning while minimizing the training cost. Using this approach, we train and present a BERT-based model trained on a biomedical corpus that matches or surpasses traditionally trained biomedical language models in performance across several downstream classification tasks while incurring up to 11 times lower training costs.
Efficient Document-level Event Relation Extraction
Authors: Ruochen Li; Zimu Wang; Xinya Du
Venue: NAACL 2025 (Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025))

Abstract: Event Relation Extraction (ERE) predicts temporal and causal relationships between events, playing a crucial role in constructing comprehensive event knowledge graphs. However, existing approaches based on pairwise comparisons often suffer from computational inefficiency, particularly at the document level, due to the quadratic operations required. Additionally, the predominance of unrelated events also leads to largely skewed data distributions. In this paper, we propose an innovative two-stage framework to tackle the challenges, consisting of a retriever to identify the related event pairs and a cross-encoder to classify the relationships between the retrieved pairs. Evaluations across representative benchmarks demonstrate our approach achieves better efficiency and significantly better performance. We also investigate leveraging event coreference chains for ERE and demonstrate their effectiveness.
Investigating Adapters for Parameter-efficient Low-resource Automatic Speech Recognition
Authors: Ahnaf Mozib Samin; Shekhar Nayak; Andrea De Marco; Claudia Borg
Venue: NAACL 2025 (Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025))

Abstract: Recent years have witnessed the adoption of parameter-efficient adapters in pre-trained language models for natural language processing. Yet, their application in speech processing remains less studied. In this work, we explore the adapters for low-resource speech recognition, introducing a novel technique - ConvAdapt into pre-trained speech models. We investigate various aspects such as data requirements, transfer learning within adapters, and scaling of feed-forward layers in adapters. Our findings reveal that bottleneck adapters offer competitiveness with full fine-tuning with at least 10 hours of data, but they are not as effective in few-shot learning scenarios. Notably, ConvAdapt demonstrates improved performance in such cases. In addition, transfer learning in adapters shows promise, necessitating research in related languages. Furthermore, employing larger speech models for adapter-tuning surpasses fine-tuning with ample data, potentially due to reduced overfitting than fine-tuning.
Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution
Authors: Tatiana Anikina; Arne Binder; David Harbecke; Stalin Varanasi; Leonhard Hennig; Simon Ostermann; Sebastian Möller; Josef Van Genabith
Venue: NAACL 2025 (Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025))

Abstract: In this work, we reimagine classical probing to evaluate knowledge transfer from simple source to more complex target tasks. Instead of probing frozen representations from a complex source task on diverse simple target probing tasks (as usually done in probing), we explore the effectiveness of embeddings from multiple simple source tasks on a single target task. We select coreference resolution, a linguistically complex problem requiring contextual understanding, as focus target task, and test the usefulness of embeddings from comparably simpler tasks tasks such as paraphrase detection, named entity recognition, and relation extraction. Through systematic experiments, we evaluate the impact of individual and combined task embeddings. Our findings reveal that task embeddings vary significantly in utility for coreference resolution, with semantic similarity tasks (e.g., paraphrase detection) proving most beneficial. Additionally, representations from intermediate layers of fine-tuned models often outperform those from final layers. Combining embeddings from multiple tasks consistently improves performance, with attention-based aggregation yielding substantial gains. These insights shed light on relationships between task-specific representations and their adaptability to complex downstream tasks, encouraging further exploration of embedding-level task transfer. Our source code is publicly available under https://github.com/Cora4NLP/multi-task-knowledge-transfer.
Punctuation Restoration Improves Structure Understanding without Supervision
Authors: Junghyun Min; Minho Lee; Woochul Lee; Yeonsoo Lee
Venue: NAACL 2025 (Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025))

Abstract: Unsupervised learning objectives like autoregressive and masked language modeling constitute a significant part in producing pre-trained representations that perform various downstream applications from natural language understanding to conversational tasks. However, despite impressive generative capabilities of recent large language models, their abilities to capture syntactic or semantic structure within text lag behind. We hypothesize that the mismatch between linguistic performance and competence in machines is attributable to insufficient learning of linguistic structure knowledge via currently popular pre-training objectives. Working with English, we show that punctuation restoration as a learning objective improves performance on structure-related tasks like named entity recognition, open information extraction, chunking, and part-of-speech tagging. Punctuation restoration results in ▲≥2%p improvement in 16 out of 18 experiments, across 6 out of 7 tasks. Our results show that punctuation restoration is an effective learning objective that can improve structure understanding and yield a more robust structure-aware representations of natural language in base-sized models.
Amuro & Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models
Authors: Kaiser Sun; Mark Dredze
Venue: NAACL 2025 (Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025))

Abstract: Large language model development relies on the pre-train-then-align paradigm, in which the model is typically pre-trained on a large text corpus and undergoes a tuning stage to align the model with human preference or downstream tasks. We investigate the relationship between pre-training and supervised fine-tuning by considering multiple tasks as well as different pre-trained model checkpoints. Our results on 18 datasets and two models suggest that i) although the model benefits significantly through supervised fine-tuning, it may forget previously known domain knowledge and tasks that are not seen during fine-tuning; ii) the model exhibits high sensitivity to evaluation prompts after supervised fine-tuning, but this sensitivity can be alleviated through further pre-training; iii) continual pre-training improves the model in a latent way that manifests after fine-tuning; iv) The model can already solve some tasks after pre-training while fine-tuning most benefits datasets where the model does not show capability during pre-training.
State Space Models are Strong Text Rerankers
Authors: Zhichao Xu; Jinghua Yan; Ashim Gupta; Vivek Srikumar
Venue: NAACL 2025 (Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025))

Abstract: Transformers dominate NLP and IR; but their inference inefficiencies and challenges in extrapolating to longer contexts have sparked interest in alternative model architectures. Among these, state space models (SSMs) like Mamba offer promising advantages, particularly time complexity in inference. Despite their potential, SSMs’ effectiveness at text reranking — a task requiring fine-grained query-document interaction and long-context understanding — remains underexplored.This study benchmarks SSM-based architectures (specifically, Mamba-1 and Mamba-2) against transformer-based models across various scales, architectures, and pre-training objectives, focusing on performance and efficiency in text reranking tasks. We find that (1) Mamba architectures achieve competitive text ranking performance, comparable to transformer-based models of similar size; (2) they are less efficient in training and inference compared to transformers with flash attention; and (3) Mamba-2 outperforms Mamba-1 in both performance and efficiency. These results underscore the potential of state space models as a transformer alternative and highlight areas for improvement in future IR applications.
Large Language Models Are Overparameterized Text Encoders
Authors: Thennal D K; Tim Fischer; Chris Biemann
Venue: NAACL 2025 (Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025))

Abstract: Large language models (LLMs) demonstrate strong performance as text embedding models when finetuned with supervised contrastive training. However, their large size balloons inference time and memory requirements. In this paper, we show that by pruning the last % layers of an LLM before supervised training for only 1000 steps, we can achieve a proportional reduction in memory and inference time. We evaluate four different state-of-the-art LLMs on text embedding tasks and find that our method can prune up to 30% of layers with negligible impact on performance and up to 80% with only a modest drop. With only three lines of code, our method is easily implemented in any pipeline for transforming LLMs to text encoders. We also propose L3Prune, a novel layer-pruning strategy based on the model’s initial loss that provides two optimal pruning configurations: a large variant with negligible performance loss and a small variant for resource-constrained settings. On average, the large variant prunes 21% of the parameters with a performance drop, and the small variant only suffers from a decrease while pruning 74% of the model. We consider these results strong evidence that LLMs are overparameterized for text embedding tasks, and can be easily pruned.
Vocabulary-level Memory Efficiency for Language Model Fine-tuning
Authors: Miles Williams; Nikolaos Aletras
Venue: NAACL 2025 (Proceedings of the 10th Workshop on Representation Learning for NLP (RepL4NLP-2025))

Abstract: The extensive memory footprint of language model (LM) fine-tuning poses a challenge for both researchers and practitioners. LMs use an embedding matrix to represent extensive vocabularies, forming a substantial proportion of the model parameters. While previous work towards memory-efficient fine-tuning has focused on minimizing the number of trainable parameters, reducing the memory footprint of the embedding matrix has yet to be explored. We first demonstrate that a significant proportion of the vocabulary remains unused during fine-tuning. We then propose a simple yet effective approach that leverages this finding to minimize memory usage. We show that our approach provides substantial reductions in memory usage across a wide range of models and tasks. Notably, our approach does not impact downstream task performance, while allowing more efficient use of computational resources.
Prompt and circumstance: A word-by-word LLM prompting approach to interlinear glossing for low-resource languages
Authors: Micha Elsner; David Liu
Venue: NAACL 2025 (Proceedings of the 22nd SIGMORPHON workshop on Computational Morphology, Phonology, and Phonetics)

Abstract: This paper presents VeLePa, an inflected verbal lexicon of Central Pame (pbs, cent2154), an Otomanguean language from Mexico. This resource contains 12528 words in phonological form representing the complete inflectional paradigms of 216 verbs, supplemented with use frequencies. Computer-operable (CLDF) inflected lexicons of non-WEIRD underresourced languages are urgently needed to expand digital capacities in this languages (e.g. in NLP). VeLePa contributes to this, and does so with data from a language which is morphologically extraordinary, with unusually high levels of irregularity and multiple conjugations at various loci within the word”:" prefixes, stems, tone, and suffixes constitute different albeit interrelated subsystems of inflection. Partly automated creation of interlinear glossed text (IGT) has the potential to assist in linguistic documentation. We argue that LLMs can make this process more accessible to linguists because of their capacity to follow natural-language instructions. We investigate the effectiveness of a retrieval-based LLM prompting approach to glossing, applied to the seven languages from the SIGMORPHON 2023 shared task. Our system beats the BERTbased shared task baseline for every language in the morpheme-level score category, and we show that a simple 3-best oracle has higher word-level scores than the challenge winner (a tuned sequence model) in five languages. In a case study on Tsez, we ask the LLM to automatically create and follow linguistic instructions, reducing errors on a confusing grammatical feature. Our results thus demonstrate the potential contributions which LLMs can make in interactive systems for glossing, both in making suggestions to human annotators and following directions.
West Germanic noun-noun compounds and the morphology-syntax trade-off
Authors: Pablo Mosteiro; Damián Blasi; Denis Paperno
Venue: NAACL 2025 (Proceedings of the 22nd SIGMORPHON workshop on Computational Morphology, Phonology, and Phonetics)

Abstract: This paper examines the linguistic distinction between syntax and morphology, focusing on noun-noun compounds in three West Germanic languages (English, Dutch, and German). Previous studies using the Parallel Bible Corpus have found a trade-off between word order (syntax) and word structure (morphology), with languages optimizing information conveyance through these systems. Our research question is whether manipulating English noun-noun compounds to resemble Dutch and German constructions can reproduce the observed distance between these languages in the order-structure plane. We extend a word-pasting procedure to merge increasingly common noun-noun pairs in English Bible translations. After each merge, we estimate the information contained in word order and word structure using entropy calculations. Our results show that pasting noun-noun pairs reduces the difference between English and the other languages, suggesting that orthographic conventions defining word boundaries play a role in this distinction. However, the effect is not pronounced, and results are statistically inconclusive.
The Impact of Dialect Variation on Robust Automatic Speech Recognition for Catalan
Authors: Zachary Hopton; Eleanor Chodroff
Venue: NAACL 2025 (Proceedings of the 22nd SIGMORPHON workshop on Computational Morphology, Phonology, and Phonetics)

Abstract: To accurately transcribe a speech signal, automatic speech recognition (ASR) systems must show robustness to a wide range of task independent variation, such as speaker factors, recording quality, or even ädversarial noisedesigned to disrupt performance.We manipulated the dialect composition of fine-tuning data for ASR to study whether balancing the relative proportion of dialects had an impact on models robustness to two such sources of variation”:" dialect variation and adversarial perturbations. We fine-tuned XLSR-53 for Catalan ASR using four different dialect compositions, each containing the Central Catalan dialect. These were defined as 100%, 80%, 50%, and 20% Central Catalan, with the remaining portions split evenly between four other Catalan dialects. While increasing the relative proportion of dialect variants improved models’ dialect robustness, this did not have a meaningful impact on adversarial robustness. These findings suggest that while improvements to ASR can be made by diversifying the training data, such changes do not sufficiently counteract adversarial attacks, leaving the technology open to security threats.
Probing Neural Network Generalization using Default Patterns
Authors: Brandon Prickett; Tianyi Niu; Katya Pertsova
Venue: NAACL 2025 (Proceedings of the 22nd SIGMORPHON workshop on Computational Morphology, Phonology, and Phonetics)

Abstract: Whether neural-net models can learn minoritydefault patterns has been a matter of some controversy. Results based on modeling real human language data are hard to interpret due to complexity. Therefore, we examine the learning of a simple artificial language pattern involving defaults using three computational models”:" an Encoder-Decoder RNN, a Transformer Encoder, and a Logistic Regression. Overall, we find that the models have the hardest time with minority defaults, but can eventually learn them and apply them to novel words (although not always extend them to completely novel segments or novel CV-sequences). Typefrequency has the largest effect on learning in all models, trumping the effect of distribution. We examine the weights of two models to provide further insights into how defaults are represented inside the models.