United States

Large language models (LLMs) frequently make errors when handling even simple numerical problems, such as comparing two small numbers. A natural hypothesis is that these errors stem from how LLMs represent numbers, and specifically, whether their representations of numbers capture their numeric values. We tackle this question from the observation that LLM errors on numerical tasks are often distributed across the digits of the answer rather than normally around its numeric value. Through a series of probing experiments and causal interventions, we show that LLMs internally represent numbers with individual circular representations per-digit in base 10. This digit-wise representation, as opposed to a value representation, sheds light on the error patterns of models on tasks involving numerical reasoning and could serve as a basis for future studies on analyzing numerical mechanisms in LLMs.

NAACL 2025

Language Models Encode Numbers Using Digit Representations in Base 10

mechanistic interpretability

interpretability

language models

poster

### 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* 

[![](https://assets.underline.io/markdown_image/1/image/b087f8a4dc5816d6e1a6514e59c59ac3.png)](https://drive.google.com/file/d/1T96GzPqObXrMln2BMByCSXTSizjTg69P/view?usp=sharing)

You need to log in with the email address you registered with. Access credentials have been sent to your email. 

Please be sure to check your spam and other email folders if you do not see an email confirmation right away.

Please log in to explore this event.

To access NAACL 2025 event page you are required to register. Please follow [**this link**](https://2025.naacl.org/registration/registration/) to register. Access will depend on your registration type.

Please register!

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

Large language models (LLMs) have been widely adopted in mathematical optimization in scientific scenarios for their extensive knowledge and advanced reasoning capabilities. Existing methods mainly focus on utilizing LLMs to solve optimization problems in a prompt-based manner, which takes observational feedback as additional textual descriptions. However, due to LLM's **high sensitivity to the prompts** and **tendency to get lost in lengthy prompts**, these methods struggle to effectively utilize the observational feedback from each optimization step, which severely hinders the applications for real-world scenarios. To address these challenges, we propose a conceptually simple and general bi-level optimization method, namely **G**eneral **S**cientific **O**ptimizers (GSO). Specifically, GSO first utilizes inner-level simulators as experimental platforms to evaluate the current solution and provide observational feedback. Then, LLMs serve as knowledgeable and versatile scientists, generating new solutions by refining potential errors from the feedback as the outer-level optimization. Finally, simulations together with the expert knowledge in LLMs are jointly updated with bi-level interactions via model editing. Extensive experiments show that GSO consistently outperforms existing state-of-the-art methods using *six* different LLM backbone on *seven* different tasks, demonstrating the effectiveness and a wide range of applications.

Exploiting Edited Large Language Models as General Scientific Optimizers

Dense passage retrieval enhances Information Retrieval (IR) by encoding queries and passages into representation space. However, passage representations often fail to be referenced by their gold queries under domain shifts, revealing a weakness in representation space. One desirable concept for representations is ''argmaxable''. Being argmaxable ensures that no representations are theoretically excluded from selection due to geometric constraints. To be argmaxable, a notable approach is to increase isotropy, where representations are evenly spread out in all directions. These findings, while desirable also for IR, focus on passage representation and not on query, making it challenging to directly apply their findings to IR. In contrast, we introduce a novel query-focused concept of ''referentiable'' tailored for IR tasks, which ensures that passage representations are referenced by their gold queries. Building on this, we propose Learning Referentiable Representation (LRR), and two strategic metrics, Self-P and Self-Q, quantifying how the representations are referentiable. Our experiments compare three dense model versions: Naive, Isotropic, and Referentiable, demonstrating that LRR leads to enhanced zero-shot performance, surpassing existing naive and isotropic versions.

Query-focused Referentiability Learning for Zero-shot Retrieval

Large Language Models (LLMs) have achieved impressive results across numerous domains, yet they experience notable deficiencies in legal question-answering tasks. LLMs often generate generalized responses that lack the logical specificity required for expert legal advice and are prone to hallucination, providing answers that appear correct but are unreliable. Retrieval-Augmented Generation (RAG) techniques offer partial solutions to address this challenge, but existing approaches typically focus only on semantic similarity, neglecting the logical structure essential to legal reasoning. In this paper, we propose the Logical-Semantic Integration Model (LSIM), a novel supervised framework that bridges semantic and logical coherence. LSIM comprises three components: reinforcement learning predicts a structured fact-rule chain for each question, a trainable Deep Structured Semantic Model (DSSM) retrieves the most relevant candidate questions by integrating semantic and logical features, and in-context learning generates the final answer using the retrieved content. Our experiments on a real-world legal QA dataset-validated through both automated metrics and human evaluation-demonstrate that LSIM significantly enhances accuracy and reliability compared to existing methods.

Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning

Consistency in the output of language models is critical for their reliability and practical utility. Due to their training objective, language models learn to model the full space of possible continuations, leading to outputs that can vary significantly in style, content, and tone, even for similar inputs. To address this, we propose a novel decoding algorithm that enhances response consistency across different prompts with no degradation in response quality. By incorporating a latent variable into the next-token sampling process based on the Gumbel reparametrisation trick, our method outperforms standard sampling by up to 10% across semantic and stylistic consistency benchmarks. Additionally, our approach integrates seamlessly with existing sampling methods with negligible computational overhead, providing a practical solution for improving the reliability of language model outputs.

Waste Not, Want Not; Recycled Gumbel Noise Improves Consistency in Natural Language Generation

Complex question-answering (QA) systems face significant challenges in retrieving and reasoning over information that addresses multifaceted queries. While large language models (LLMs) have advanced the reasoning capabilities of these systems, the bounded-recall problem persists, where procuring all relevant documents in first-stage retrieval remains a challenge. Missing pertinent documents at this stage leads to performance degradation that cannot be remedied in later stages, especially given the limited context windows of LLMs which necessitate high recall at smaller retrieval depths. In this paper, we introduce SUNAR, a novel approach that leverages LLMs to guide a Neighborhood Aware Retrieval process. SUNAR iteratively explores a neighborhood graph of documents, dynamically promoting or penalizing documents based on uncertainty estimates from interim LLM-generated answer candidates. We validate our approach through extensive experiments on two complex QA datasets. Our results show that SUNAR significantly outperforms existing retrieve-and-reason baselines, achieving up to a 31.84% improvement in performance over existing state-of-the-art methods for complex QA. Our code and data are anonymously available at https://anonymous.4open.science/r/SUNAR-8D36/.

SUNAR: Semantic Uncertainty based Neighborhood Aware Retrieval for Complex QA

Open relation extraction (OpenRE) aims to identify relational facts within open-domain corpora without relying on predefined relation types. A significant limitation of current state-of-the-art OpenRE approaches is their inability to accurately self-assess their performance. Which is caused by the reliance on pseudo-labels, that treats all points within a cluster equally, regardless of their actual relative position according to the cluster center. This leads to models that are often overconfident in their incorrect predictions , significantly undermining their reliability. In this paper, we introduce an approach that addresses this challenge by effectively modeling a part of the epistemic uncertainty within OpenRE. Instead of using pseudo labels that mask uncertainty, our approach is built to train a classifier directly with the clustering distribution. Our experimental results across various datasets demonstrate that the suggested approach improves reliability of OpenRE by preventing overconfident errors. Furthermore we show that by improving the reliability of the predictions, UOREX operates more efficiently in a generative active learning context where an LLM is the oracle, doubling the performance gain compared to the state-of-the-art.

UOREX: Towards Uncertainty-Aware Open Relation Extraction

Text simplification aims to enhance the clarity and comprehensibility of a complex text while preserving its original meaning. Previous research on the automatic evaluation of text simplification has primarily focused on sentence simplification, with commonly used metrics such as SARI and advanced metrics such as LENS being trained and evaluated at the sentence level. However, these metrics often underperform on longer texts. In our study, we propose a novel approach to adapt existing sentence-level metrics for paragraph- or document-level simplification. We benchmark our approach against a wide variety of existing reference-based and reference-less metrics across multiple domains. Empirical results demonstrate that our approach outperforms traditional sentence-level metrics in terms of correlation with human judgment. Furthermore, we evaluate the sensitivity and robustness of various metrics to different types of errors produced by existing text simplification systems.

Adapting Sentence-level Automatic Metrics for Document-level Simplification Evaluation

Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks. Despite the successes of chain-of-thought and tree-based search methods, they mainly depend on the internal knowledge of LLMs to search over intermediate reasoning steps, limited to dealing with simple tasks involving fewer reasoning steps. In this paper, we propose \textbf{RAG-Star}, a novel RAG approach that integrates the retrieved information to guide the tree-based deliberative reasoning process that relies on the inherent knowledge of LLMs. By leveraging Monte Carlo Tree Search, RAG-Star iteratively plans intermediate sub-queries and answers for reasoning based on the LLM itself. To consolidate internal and external knowledge, we propose a retrieval-augmented verification that utilizes query- and answer-aware reward modeling to provide feedback for the inherent reasoning of LLMs. Our experiments involving Llama-3.1-8B-Instruct and GPT-4o demonstrate that RAG-Star significantly outperforms previous RAG and reasoning methods. Our codes and data are publicly available at https://github.com/RUCAIBox/RAG-Star.

RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement

Neural Machine Translation (NMT) models have traditionally used Sinusoidal Positional Embeddings (PEs), which often struggle to capture long-range dependencies and are inefficient for handling extended context or document-level translation tasks. This work addresses the challenge of transitioning pre-trained NMT models from absolute Sinusoidal PEs to Relative PEs, such as RoPE and ALiBi, without compromising performance. We demonstrate that parameter-efficient fine-tuning, using only a small amount of high-quality data, can successfully facilitate this transition. Experimental results indicate that switching from Sinusoidal to Relative PEs results in competitive translation quality on sentence-level evaluation benchmarks. Additionally, models trained with RoPE consistently outperform those using ALiBi and Sinusoidal PEs on document-level benchmarks across both string-based metrics and qualitative evaluations. Moreover, we find that a small amount of long-context data in a few languages is sufficient for cross-lingual length generalization, thereby inducing long-context capabilities.

Towards Inducing Long-Context Abilities in Multilingual Neural Machine Translation Models

Realignment techniques are often employed to enhance cross-lingual transfer in multilingual language models, still, they can sometimes degrade performance in languages that differ significantly from the fine-tuned source language. This paper introduces AlignFreeze, a method that freezes either the layers' lower half or upper half during realignment. Through controlled experiments on 4 tasks, 3 models, and in 35 languages, we find that realignment affects all the layers but can be the most detrimental to the lower ones. Freezing the lower layers can prevent performance degradation. Particularly, AlignFreeze improves Part-of-Speech (PoS) tagging performances in languages where full realignment fails: with XLM-R, it provides improvements of more than one standard deviation in accuracy in seven more languages than full realignment.

Language Models Encode Numbers Using Digit Representations in Base 10

Downloads

Next from NAACL 2025

Exploiting Edited Large Language Models as General Scientific Optimizers

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES