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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.

NAACL 2025

A Comparative Study of Learning Paradigms in Large Language Models via Intrinsic Dimension

pre-training efficiency

biomedical language models

transfer learning

workshop paper

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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 casual 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.

Prompt Tuning Can Simply Adapt Large Language Models to Text Encoders

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.

Tracking Universal Features Through Fine-Tuning and Model Merging

Language Models (LMs) struggle with linguistic understanding at the discourse level, even though discourse patterns such as coherence, cohesion, and narrative flow are prevalent in their pre-training data. To improve the discourse capabilities of LMs already at the pre-training stage, we introduce DEPTH, an encoder-decoder model that learns latent representations for sentences using a discourse-oriented pre-training objective. DEPTH combines hierarchical sentence representations with two objectives: (1) *Sentence Un-Shuffling*, and (2) *Span-Corruption*. Our approach trains the model to represent both sub-word-level and sentence-level dependencies over a pre-training corpora. When trained either from scratch or continuing from a pre-trained T5 checkpoint, DEPTH learns semantic and discourse-level representations faster than T5, outperforming it in span-corruption loss despite the additional sentence-un-shuffling objective. Evaluations on the GLUE, DiscoEval, and NI benchmarks demonstrate DEPTH's ability to quickly learn diverse downstream tasks, which require syntactic, semantic, and discourse capabilities. Our approach extends the discourse capabilities of T5, while minimally impacting other natural language understanding (NLU) capabilities in the resulting LM. Our codebase is available at https://anonymous.4open.science/r/depth-BCFF/README.md

DEPTH: Discourse Education through Pre-Training Hierarchically

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.

Vocabulary-level Memory Efficiency for Language Model Fine-tuning

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.

Amuro & Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models

In this work in progress, we explore the use of large language models (LLMs) to develop linguistic tools that benefit Guarani-Jopara, a variant of the indigenous Guarani language influenced by Spanish and widely spoken in Paraguay as well as in Guarani-speaking communities in other regions. Despite its cultural and social importance, Guarani-Jopara has been underrepresented in modern natural language processing (NLP) applications due to the scarcity of digital resources available in this language. To mitigate this gap, we employed the {Google Translate} API to translate the Alpaca dataset into Guarani-Jopara, thereby generating a synthetic corpus. Training was carried out using tools such as {HuggingFace's AutoTrain} and {UnsloothAI's unsloth}, allowing for fine-tuning on several pre-trained models. The preliminary results indicate that the trained model can capture some of the characteristics of Guarani-Jopara, suggesting a considerable margin for improvement. These initial experiments represent another step toward the inclusion of Guarani-Jopara in NLP applications, with future efforts directed to expanding the corpus and optimizing the model for specific tasks such as machine translation or sentiment analysis.

Building a Large Language Model for Guarani-Jopara? Methodology, Challenges, and Preliminary Results

Machine translation is a tool that can help teachers, learners, and users of low-resourced languages. However, there are significant challenges in developing these tools, such as the lack of large-scale parallel corpora and complex morphology. We propose a novel hybrid system that combines LLM and rule-based methods in two distinct stages to translate inflected Ojibwe verbs into English. We use an LLM to automatically annotate dictionary data to build translation templates. Then, our rule-based module performs translation using inflection and slot-filling processes built on top of an FST-based analyzer. We test the system with a set of automated tests. Thanks to the ahead-of-time nature of the template-building process and the light-weight rule-based translation module, the end-to-end translation process has an average translation speed of 70 milliseconds per word. The approach has the potential to be extended to other low-resource Indigenous languages with dictionary data.

A hybrid Approach to low-resource machine translation for Ojibwe verbs

Quechua is a low-resource language spoken by more than 7 million people in South America. It is a spoken language without writing. Quechua's writing does not follow any grammatical or lexical rules; its current writing is based on how it is uttered and how the sound is written. Quechua is a family of languages with similarities among the seven variants. The lack of a parallel dataset has reduced the opportunities for developing machine translation. We investigated whether increasing the current Quechua Parallel dataset with synthetic sentences and using a pre-trained large language model improves the performance of a Quechua machine translation. A Large language model has been used to generate synthetic sentences to extend the current parallel dataset. We use the mt5 model to fine-tune it to develop a machine translation for Quechua to Spanish and vice versa. Our survey identified the gaps in the state of the art of Quechua machine translation, and our BLEU/Chrf++ results show an improvement over the state of the art.

UCSP Submission to the AmericasNLP 2025 Shared Task

Language is a cornerstone of cultural identity, yet globalization and the dominance of major languages have placed nearly 3,000 languages at risk of extinction. Existing AI-driven translation models prioritize efficiency but often fail to capture cultural nuances, idiomatic expressions, and historical significance, leading to translations that marginalize linguistic diversity. To address these challenges, we propose a multi-agent AI framework designed for culturally adaptive translation in underserved language communities. Our approach leverages specialized agents for translation, interpretation, content synthesis, and bias evaluation, ensuring that linguistic accuracy and cultural relevance are preserved. Using CrewAI and LangChain, our system enhances contextual fidelity while mitigating biases through external validation. Comparative analysis shows that our framework outperforms GPT-4o, producing contextually rich and culturally embedded translations—a critical advancement for Indigenous, regional, and low-resource languages. This research underscores the potential of multi-agent AI in fostering equitable, sustainable, and culturally sensitive NLP technologies, aligning with the AI Governance, Cultural NLP, and Sustainable NLP pillars of Language Models for Underserved Communities. Our full experimental codebase is publicly avail able at: github.com/ciol-researchlab/Context-Aware_Translation_MAS.

Preserving Cultural Identity with Context-Aware Translation Through Multi-Agent AI Systems

This study investigates zero-shot and few-shot cross-lingual transfer effects in Part-of-Speech (POS) tagging and Named Entity Recognition (NER) for Hamshentsnag, an endangered Western Armenian dialect. We examine how different source languages, Western Armenian (contact cognate), Eastern Armenian (ancestral cognate), Turkish (substrate or contact-induced), and English (non-cognate), affect the task performance using multilingual BERT and BERTurk. Results show that cognate varieties improved POS tagging by 8% F1, while the substrate source enhanced NER by 15% F1. BERTurk outperformed mBERT on NER but not on POS. We attribute this to task-specific advantages of different source languages. We also used script conversion and phonetic alignment with the target for non-Latin scripts, which alleviated transfer.

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Prompt Tuning Can Simply Adapt Large Language Models to Text Encoders

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Next from NAACL 2025

Prompt Tuning Can Simply Adapt Large Language Models to Text Encoders

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads