United States

Evaluating an LLM&#39;s robustness against numerical perturbation is a good way to know if the LLM actually performs reasoning or just replicates patterns learned. We propose a novel method to augment math word problems (MWPs), producing numerical variations at a large scale utilizing templates. We also propose an automated error classification framework for scalable error analysis, distinguishing calculation errors from reasoning errors. Our experiments using the methods show LLMs are weak against numerical variations, suggesting they are not fully capable of generating valid reasoning steps, often failing in arithmetic operations.

NAACL 2025

Evaluating Robustness of LLMs to Numerical Variations in Mathematical Reasoning

dynamic dataset

gsm8k

mathematical reasoning

evaluation

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EMNLP 2023

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Evaluating an LLM's robustness against numerical perturbation is a good way to know if the LLM actually performs reasoning or just replicates patterns learned. We propose a novel method to augment math word problems (MWPs), producing numerical variations at a large scale utilizing templates. We also propose an automated error classification framework for scalable error analysis, distinguishing calculation errors from reasoning errors. Our experiments using the methods show LLMs are weak against numerical variations, suggesting they are not fully capable of generating valid reasoning steps, often failing in arithmetic operations.

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. 
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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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.

Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution

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 atleast 10 hours of data, but is 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.

Investigating Adapters for Parameter-efficient Low-resource Automatic Speech Recognition

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.

Efficient Document-level Event Relation Extraction

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.

Choose Your Words Wisely: Domain-adaptive Masking Makes Language Models Learn Faster

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.

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

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.

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Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution

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Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data

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