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A critical challenge in deploying Large Language Models (LLMs) is developing reliable mechanisms to estimate their confidence, enabling systems to determine when to trust model outputs and when to seek human intervention. In this paper, we present a Calibrated Reflection Approach for Enhancing Confidence Estimation in LLMs, a framework that combines structured reasoning with distance-aware calibration techniques. Our approach introduces three key innovations: (1) a Maximum Confidence Selection (MCS) method that comprehensively evaluates confidence across all possible labels, (2) a reflection-based prompting mechanism that enhances reasoning reliability, and (3) a distance-aware calibration technique that accounts for ordinal relationships between labels. We evaluate our framework across diverse datasets, including HelpSteer2, Llama T-REx, and an internal conversational dataset, demonstrating its effectiveness across both conversational and fact-based classification tasks. This work contributes to the broader goal of developing reliable and well-calibrated confidence estimation methods for LLMs, enabling informed decisions about when to trust model outputs and when to defer to human judgement.

NAACL 2025

A Calibrated Reflection Approach for Enhancing Confidence Estimation in LLMs

large language models (llms)

automated evaluation

self-reflection

trustworthy ai

model calibration

uncertainty quantification

confidence estimation

reasoning

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

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

Transformer-based pretrained large language models (PLM) such as BERT and GPT have achieved remarkable success in NLP tasks. However, PLMs are prone to encoding stereotypical biases. Although a burgeoning literature has emerged on stereotypical bias mitigation in PLMs, such as work on debiasing gender and racial stereotyping, how such biases manifest and behave internally within PLMs remains largely unknown. Understanding the internal stereotyping mechanisms may allow better assessment of model fairness and guide the development of effective mitigation strategies. In this work, we focus on attention heads, a major component of the Transformer architecture, and propose a bias analysis framework to explore and identify a small set of biased heads that are found to contribute to a PLM's stereotypical bias. We conduct extensive experiments to validate the existence of these biased heads and to better understand how they behave. We investigate gender and racial bias in the English language in two types of Transformer-based PLMs: the encoder-based BERT model and the decoder-based autoregressive GPT model, LLaMA-2 (7B), and LLaMA-2-Chat (7B). Overall, the results shed light on understanding the bias behavior in pretrained language models.

Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Heads

Language Model Models (LLMs) have improved dramatically in the past few years, increasing their adoption and the scope of their capabilities over time. A significant amount of work is dedicated to "model alignment", i.e., preventing LLMs to generate unsafe responses when deployed into customer-facing applications. One popular method to evaluate safety risks is red-teaming, where agents attempt to bypass alignment by crafting elaborate prompts that trigger unsafe responses from a model. Standard human-driven red-teaming is costly, time-consuming and rarely covers all the recent features (e.g., multi-lingual, multi-modal aspects), while proposed automation methods only cover a small subset of LLMs capabilities (i.e., English or single-turn). We present Multi-lingual Multi-turn Automated Red Teaming (MM-ART), a method to fully automate conversational, multi-lingual red-teaming operations and quickly identify prompts leading to unsafe responses. Through extensive experiments on different languages, we show the studied LLMs are on average 71% more vulnerable after a 5-turn conversation in English than after the initial turn. For conversations in non-English languages, models display up to 195% more safety vulnerabilities than the standard single-turn English approach, confirming the need for automated red-teaming methods matching LLMs capabilities.

Multi-lingual Multi-turn Automated Red Teaming for LLMs

The rapid growth of Large Language Models (LLMs) presents significant privacy, security, and ethical concerns. While much research has proposed methods for defending LLM systems against misuse by malicious actors, researchers have recently complemented these efforts with an offensive approach that involves red teaming, i.e., proactively attacking LLMs with the purpose of identifying their vulnerabilities. This paper provides a concise and practical overview of the LLM red teaming literature, structured so as to describe a multi-component system end-to-end. To motivate red teaming we survey the initial safety needs of some high-profile LLMs, and then dive into the different components of a red teaming system as well as software packages for implementing them. We cover various attack methods, strategies for attack-success evaluation, metrics for assessing experiment outcomes, as well as a host of other considerations. Our survey will be useful for any reader who wants to rapidly obtain a grasp of the major red teaming concepts for their own use in practical applications.

Building Safe GenAI Applications: An End-to-End Overview of Red Teaming for Large Language Models

Uncertainty quantification (UQ) in Large Language Models (LLMs) is essential for their safe and reliable deployment, particularly in critical applications where incorrect outputs can have serious consequences. Current UQ methods typically rely on querying the model multiple times using non-zero temperature sampling to generate diverse outputs for uncertainty estimation. However, the impact of selecting a given temperature parameter is understudied, and our analysis reveals that temperature plays a fundamental role in the quality of uncertainty estimates. The conventional approach of identifying optimal temperature values requires expensive hyperparameter optimization (HPO) that must be repeated for each new model-dataset combination. We propose Monte Carlo Temperature (MCT), a robust sampling strategy that eliminates the need for temperature calibration. Our analysis reveals that: 1) MCT provides more robust uncertainty estimates across a wide range of temperatures, 2) MCT improves the performance of UQ methods by replacing fixed-temperature strategies that do not rely on HPO, and 3) MCT achieves statistical parity with oracle temperatures, which represent the ideal outcome of a well-tuned but computationally expensive HPO process. These findings demonstrate that effective UQ can be achieved without the computational burden of temperature parameter calibration.

Monte Carlo Temperature: a robust sampling strategy for LLM's uncertainty quantification methods

The rapid rise of Language Models (LMs) has expanded their use in several applications. Yet, due to constraints of model size, associated cost, or proprietary restrictions, utilizing state-of-the-art (SOTA) LLMs is not always feasible. With open, smaller LMs emerging, more applications can leverage their capabilities, but selecting the right LM can be challenging as smaller LMs don’t perform well universally. This work tries to bridge this gap by proposing a framework to experimentally evaluate small, open LMs in practical settings through measuring semantic correctness of outputs across three practical aspects: task types, application domains and reasoning types, using diverse prompt styles. It also conducts an in-depth comparison of 10 small, open LMs to identify best LM and prompt style depending on specific application requirement using the proposed framework. We also show that if selected appropriately, they can outperform SOTA LLMs like DeepSeek-v2, GPT-4o-mini, Gemini-1.5-Pro, and even compete with GPT-4o.

Are Small Language Models Ready to Compete with Large Language Models for Practical Applications?

Multi-Aspect Controllable Text Generation (MCTG) introduces fine-grained multiple constraints in natural language generation, i.e. control attributes in topics, sentiments, and detoxification. MCTG demonstrates application prospects for trustworthy generation of Large Language Models (LLMs) but is limited by generalization issues. Existing work exploits additional structures and strategies for solutions, requiring LLMs' modifications. To activate LLMs' MCTG ability, we propose a lightweight MCTG pipeline based on data augmentation and instruction tuning. We analyze aspect bias and correlations in traditional datasets and address these concerns with augmented control attributes and sentences. Augmented datasets are feasible for instruction tuning. We conduct experiments for various LLMs backbone and parameter sizes, demonstrating general effectiveness on MCTG performance.

A Lightweight Multi Aspect Controlled Text Generation Solution For Large Language Models

Large Language Models (LLMs) are increasingly acting as autonomous agents, with function calling (FC) capabilities enabling them to invoke specific tools for tasks. While prior research has primarily focused on improving FC accuracy, little attention has been given to the robustness of these agents to perturbations in their input. We introduce a benchmark assessing FC robustness in two key areas: resilience to naturalistic query variations, and stability in function calling when the toolkit expands with semantically related tools. Evaluating best-performing FC models on a carefully expanded subset of the Berkeley function calling leaderboard (BFCL), we identify critical weaknesses in existing evaluation methodologies, and highlight areas for improvement in real-world agentic deployments.

On the Robustness of Agentic Function Calling

Machine learning models have proven to be useful in various key applications such as autonomous driving or diagnosis prediction. When a model is implemented under real-world conditions, it is thus essential to detect potential errors with a trustworthy approach. This monitoring practice will render decision-making safer by avoiding catastrophic failures. In this paper, the focus is on multimodal classification. We introduce a method that addresses error detection based on unlabeled data. It leverages fused representations and computes the probability that a model will fail based on detected fault patterns in validation data. To improve transparency, we employ a sampling-based approximation of Shapley values in multimodal settings in order to explain why a prediction is assessed as erroneous in terms of feature values. Further, as explanation methods can sometimes disagree, we suggest evaluating the consistency of explanations produced by different value functions and algorithms. To show the relevance of our method, we measure it against a selection of 9 baselines from various domains on tabular-text and text-image datasets, and 2 multimodal fusion strategies for the classification models. Lastly, we show the usefulness of our explanation algorithm on misclassified samples.

Error Detection for Multimodal Classification

When verifying a claim in real-world settings, e.g. against a large collection of candidate evidence text retrieved from the web, a model is typically expected to identify and aggregate a complete set of evidence pieces that collectively provide full support to a claim. The problem becomes particularly challenging as there might exist different sets of evidence that could be used to verify the claim from different perspectives. In this paper, we formally define and study the problem of identifying such minimal evidence groups (MEGs) for fact verification. We show that MEG identification can be reduced to a Set Cover-like problem, based on an entailment model which estimates whether a given evidence group provides full or partial support to a claim. Our proposed approach achieves 18.4% & 34.8% absolute improvements on WiCE and SciFact datasets over LLM prompting. Finally, we demonstrate the downstream benefit of MEGs in applications such as claim generation.

Minimal Evidence Group Identification for Claim Verification

Previous studies have established that language models manifest stereotyped biases. Existing debiasing strategies, such as retraining a model with counterfactual data, representation projection, and prompting often fail to efficiently eliminate bias or directly alter the models' biased internal representations. To address these issues, we propose BiasEdit, an efficient model editing method to remove stereotypical bias from language models through lightweight networks that act as editors to generate parameter updates. BiasEdit employs a *debiasing loss* guiding editor networks to conduct local edits on partial parameters of a language model for debiasing while preserving the language modeling abilities during editing through a *retention loss*. Experiments on StereoSet and Crows-Pairs demonstrate the effectiveness, efficiency, and robustness of BiasEdit in eliminating bias compared to tangental debiasing baselines, and little to no impact on the language models' general capabilities. In addition, we conduct bias tracing to probe bias in various modules and explore bias editing impacts on different components of language models.

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