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In-context learning (ICL) has transformed the use of large language models (LLMs) for NLP tasks, enabling few-shot learning by conditioning on labeled examples without finetuning. Despite its effectiveness, ICL is prone to errors, especially for challenging examples. With the goal of improving the performance of ICL, we propose *corrective in-context learning* (CICL), an approach that incorporates a model&#39;s incorrect predictions alongside ground truth corrections into the prompt, aiming to enhance classification accuracy through self-correction. However, contrary to our hypothesis, extensive experiments on text classification tasks demonstrate that CICL consistently underperforms standard ICL, with performance degrading as the proportion of corrections in the prompt increases. Our findings indicate that CICL introduces confusion by disrupting the model&#39;s task understanding, rather than refining its predictions. Additionally, we observe that presenting harder examples in standard ICL does not improve performance, suggesting that example difficulty alone may not be a reliable criterion for effective selection. By presenting these negative results, we provide important insights into the limitations of self-corrective mechanisms in LLMs and offer directions for future research.

NAACL 2025

Corrective In-Context Learning: Evaluating Self-Correction in Large Language Models

in-context learning

large language models

few-shot learning

## Welcome to EMNLP 2024! 
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In-context learning (ICL) has transformed the use of large language models (LLMs) for NLP tasks, enabling few-shot learning by conditioning on labeled examples without finetuning. Despite its effectiveness, ICL is prone to errors, especially for challenging examples. With the goal of improving the performance of ICL, we propose *corrective in-context learning* (CICL), an approach that incorporates a model's incorrect predictions alongside ground truth corrections into the prompt, aiming to enhance classification accuracy through self-correction. However, contrary to our hypothesis, extensive experiments on text classification tasks demonstrate that CICL consistently underperforms standard ICL, with performance degrading as the proportion of corrections in the prompt increases. Our findings indicate that CICL introduces confusion by disrupting the model's task understanding, rather than refining its predictions. Additionally, we observe that presenting harder examples in standard ICL does not improve performance, suggesting that example difficulty alone may not be a reliable criterion for effective selection. By presenting these negative results, we provide important insights into the limitations of self-corrective mechanisms in LLMs and offer directions for future research.

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

Multilingual large language models (LLMs) aim towards robust natural language understanding across diverse languages, yet their performance significantly degrades on low-resource languages. This work explores whether existing techniques to identify language-specific neurons can be leveraged to enhance cross-lingual task performance of low-resource languages. We conduct detailed experiments covering existing language-specific neuron identification techniques (such as Language
Activation Probability Entropy and activation probability-based thresholding) and
neuron-specific LoRA fine-tuning with models like Llama 3.1 and Mistral Nemo. We find that such neuron-specific interventions are insufficient to yield cross-lingual improvements on downstream tasks (XNLI, XQuAD) in low-resource languages. This study highlights the challenges in achieving cross-lingual generalization and provides critical insights for multilingual LLMs.

Language-Specific Neurons Do Not Facilitate Cross-Lingual Transfer

Intermediate Layer Distillation (ILD) is a variant of Knowledge Distillation (KD), a method for compressing neural networks.
ILD requires mapping to align the intermediate layer sizes of the teacher and student models to compute the loss function in training, while this mapping is not used during inference.
This inconsistency may reduce the effectiveness of learning in intermediate layers.
In this study, we propose LoRAILD, which uses LoRA adapters to eliminate the inconsistency.
However, our experimental results show that LoRAILD does not outperform existing methods.
Furthermore, contrary to previous studies, we observe that conventional ILD does not outperform vanilla KD.
Our analysis of the distilled models' intermediate layers suggests that ILD does not improve language models' performance.

Aligning Sizes of Intermediate Layers by LoRA Adapter for Knowledge Distillation

Sustainability metrics have increasingly become a crucial non-financial criterion in investment decision-making. Organizations worldwide are recognizing the importance of sustainability and are proactively highlighting their efforts through specialized sustainability reports. Unlike traditional annual reports, these sustainability disclosures are typically text-heavy and are often expressed as infographics, complex tables, and charts. The non-machine-readable nature of these reports presents a significant challenge for efficient information extraction. The rapid advancement of Vision Language Models (VLMs) has raised the question whether these VLMs can address such challenges in domain specific task. In this study, we demonstrate the application of VLMs for extracting sustainability information from dedicated sustainability reports. Our experiments highlight the limitations in the performance of several open-source VLMs in extracting information about sustainability disclosures from different type of pages.

Exploring Multimodal Language Models for Sustainability Disclosure Extraction: A Comparative Study

Large Language Models (LLMs) enhanced with tool use and APIs improve task performance but often misuse them, leading to inefficiency and unnecessary cost. We propose Self Knowledge-Tracing for Tool Use (SKT-Tool), a method enabling LLMs to assess their capabilities and make informed API usage decisions using knowledge tracing (KT). Our teacher-student framework helps LLMs optimize API calls in real-time without fine-tuning. Experiments across multiple datasets show that SKT-Tool significantly reduces API calls while maintaining accuracy, offering a scalable and cost-effective solution for tool-augmented LLMs. We conclude by analyzing shortcomings in this method and identifying directions for future work.

Self Knowledge-Tracing for Tool Use (SKT-Tool): Helping LLM Agents Understand Their Capabilities in Tool Use

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.

Evaluating Robustness of LLMs to Numerical Variations in Mathematical Reasoning

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.

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