Austria

Hallucination, the generation of factually incorrect information, remains a significant challenge for large language models (LLMs), especially in open-domain long-form generation. Existing approaches for detecting hallucination in long-form tasks either focus on limited domains or rely heavily on external fact-checking tools, which may not always be available.

In this work, we systematically investigate reference-free hallucination detection in open-domain long-form responses. Our findings reveal that internal states (e.g., model&#39;s output probability and entropy) alone are insufficient for reliably (i.e., better than random guessing) distinguishing between factual and hallucinated content. To enhance detection, we explore various existing approaches, including prompting-based methods, probing, and fine-tuning, with fine-tuning proving the most effective. To further improve the accuracy, we introduce a new paradigm, named RATE-FT, that augments fine-tuning with an auxiliary task for the model to jointly learn with the main task of hallucination detection. With extensive experiments and analysis using a variety of model families &amp; datasets, we demonstrate the effectiveness and generalizability of our method, e.g., +3% over general fine-tuning methods on LongFact.

ACL 2025

Learning Auxiliary Tasks Improves Reference-Free Hallucination Detection in Open-Domain Long-Form Generation

hallucination detection

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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Hallucination, the generation of factually incorrect information, remains a significant challenge for large language models (LLMs), especially in open-domain long-form generation. Existing approaches for detecting hallucination in long-form tasks either focus on limited domains or rely heavily on external fact-checking tools, which may not always be available.

In this work, we systematically investigate reference-free hallucination detection in open-domain long-form responses. Our findings reveal that internal states (e.g., model's output probability and entropy) alone are insufficient for reliably (i.e., better than random guessing) distinguishing between factual and hallucinated content. To enhance detection, we explore various existing approaches, including prompting-based methods, probing, and fine-tuning, with fine-tuning proving the most effective. To further improve the accuracy, we introduce a new paradigm, named RATE-FT, that augments fine-tuning with an auxiliary task for the model to jointly learn with the main task of hallucination detection. With extensive experiments and analysis using a variety of model families & datasets, we demonstrate the effectiveness and generalizability of our method, e.g., +3% over general fine-tuning methods on LongFact.

technical paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Despite the remarkable successes of Large Language Models (LLMs), the underlying Transformer architecture has inherent limitations in handling complex reasoning tasks. {Chain-of-Thought} (CoT) prompting has emerged as a practical workaround, but most CoT-based methods rely on a single generic prompt like “think step by step,” with no task-specific adaptation. These approaches expect the model to discover an effective reasoning path on its own, forcing it to search through a vast prompt space. 
In contrast, many work has explored task-specific prompt designs to boost performance. However, these designs are typically developed through trial and error, lacking a theoretical ground. As a result, prompt engineering remains largely ad hoc and unguided.
In this paper, we provide a theoretical framework that explains why some prompts succeed while others fail. We show that prompts function as selectors, extracting specific task-relevant information from the model’s full hidden state during CoT reasoning. Each prompt defines a unique trajectory through the answer space, and the choice of this trajectory is crucial for task performance and future navigation in the answer space.
We analyze the complexity of finding optimal prompts and the size of the prompt space for a given task. Our theory reveals principles behind effective prompt design and shows that naive CoT—using model-self-guided prompt like “think step by step” —can severely hinder performance. Showing that optimal prompt search can lead to over a 50% improvement on reasoning tasks through experiments, our work provide a theoretical foundation for prompt engineering.

Navigating the Prompt Space: Supervision Matters in CoT When Reasoning Misleads

Large language models (LLMs) have shown impressive few-shot generalization on many tasks via in-context learning (ICL). Despite their success in showing such emergent abilities, the scale and complexity of larger models also lead to unprecedentedly high computational demands and deployment challenges. In reaction, researchers explore transferring the powerful capabilities of larger models to more efficient and compact models by typically aligning the output of smaller (student) models with that of larger (teacher) models. Existing methods either train student models on the generated outputs of teacher models or imitate their token-level probability distributions. However, these distillation methods pay little to no attention to the input, which also plays a crucial role in ICL. Based on the finding that the performance of ICL is highly sensitive to the selection of demonstration examples, we propose Bidirectional Alignment (BiAlign) to fully leverage the models' preferences for ICL examples to improve the ICL abilities of student models. Specifically, we introduce the alignment of input preferences between student and teacher models by incorporating a novel ranking loss, in addition to aligning the token-level output distribution. With extensive experiments and analysis, we demonstrate that BiAlign can consistently outperform existing baselines on a variety of tasks involving language understanding, reasoning, and coding.

Beyond Output Matching: Bidirectional Alignment for Enhanced In-Context Learning

Accents play a pivotal role in shaping human communication, enhancing our ability to convey and comprehend messages with clarity and cultural nuance. While there has been significant progress in Automatic Speech Recognition (ASR), African-accented English ASR has been understudied due to a lack of training datasets, which are often expensive to create and demand colossal human labor. Combining several active learning paradigms and the core-set approach, we propose a new multi-round adaptation process that uses epistemic uncertainty to automate annotation, significantly reducing the associated costs and human labor. This novel method streamlines data annotation and strategically selects data samples that contribute most to model uncertainty, enhancing training efficiency. We define a new U-WER metric to track model adaptation to hard accents. We evaluate our approach across several domains, datasets, and high-performing speech models. Our results show that our approach leads to a 27\% WER relative average improvement while requiring, on average, 45\% less data than established baselines. Our approach also improves out-of-distribution generalization for very low-resource accents, demonstrating its viability for building generalizable ASR models in the context of accented African ASR. We open-source the code \href{}{here}.

Advancing African-Accented English Speech Recognition: Epistemic Uncertainty-Driven Data Selection for Generalizable ASR Models

Active learning (AL) aims to reduce annotation effort by iteratively selecting the most informative samples for labeling. The dominant strategy in AL involves fully finetuning the model on all acquired data after each round, which is computationally expensive in multilingual and low-resource settings. This paper investigates \textit{continual finetuning} (CF), an alternative update strategy where the model is updated only on newly acquired samples at each round. We evaluate CF against full finetuning (FA) across 28 African languages using MasakhaNEWS and SIB-200. Our analysis reveals three key findings. First, CF matches or outperforms FA for languages included in the model's pretraining, achieving up to 35\% reductions in GPU memory, FLOPs, and training time. Second, CF performs comparably even for languages not seen during pretraining when they are typologically similar to those that were. Third, CF's effectiveness depends critically on uncertainty-based acquisition; without it, performance deteriorates significantly. While FA remains preferable for some low-resource languages, the overall results establish CF as a robust, cost-efficient alternative for active learning in multilingual NLP. These findings motivate developing hybrid AL strategies that adapt fine-tuning behavior based on pretraining coverage, language typology, and acquisition dynamics.

Rethinking Full Finetuning from Pretraining Checkpoints in Active Learning for African Languages

Music has long served as a vehicle for political expression, with protest songs playing a central role in articulating dissent and mobilizing collective action. Yet, despite their cultural significance, the linguistic and acoustic signatures that define protest music remain understudied. We present a multimodal computational analysis of protest and non-protest songs spanning multiple decades. Using NLP and audio analysis, we identify the linguistic and musical features that differentiate protest songs. Instead of focusing on classification performance, we treat classification as a diagnostic tool to investigate these features and reveal broader patterns. Protest songs are not just politically charged they are acoustically and linguistically distinct, and we quantify how.

Voices of Dissent: A Multimodal Analysis of Protest Songs through Lyrics and Audio

This paper introduces CausalGraphBench, a benchmark designed to evaluate the ability of large language models (LLMs) to construct Causal Graphs (CGs), a critical component of reasoning models like Bayesian Networks. The benchmark comprises 35 CGs sourced from publicly available repositories and academic papers, each enriched with detailed metadata to facilitate systematic and consistent evaluation. We explore various LLM-driven methods for CG discovery, analyzing their performance across different graph sizes and complexity levels. Additionally, we examine the effects of data contamination on the quality of the generated CGs.

Our findings reveal that methods relying on approaches with a limited number of queries to LLM, particularly those leveraging the full graph context, consistently outperform query-intensive and exhaustive approaches, which tend to overemphasize local relationships. Across all methods, performance declines as graph size increases, emphasizing scalability as a key challenge.

CausalGraphBench: a Benchmark for Evaluating Language Models capabilities of Causal Graph discovery

This paper presents a novel geometric interpretation of LayerNorm and explores how LayerNorm influences the norm and orientation of hidden vectors in the representation space. We show that the definition of LayerNorm is innately linked to the uniform vector, defined as $\boldsymbol{1} = [1, 1, 1, 1, \cdots, 1]^T \in \mathbb{R}^d$. We then show that the standardization step in LayerNorm can be understood in three simple steps: (i) remove the component of a vector along the uniform vector, (ii) normalize the remaining vector, and (iii) scale the resultant vector by $\sqrt{d}$, where $d$ is the dimensionality of the representation space. Finally, we compare the hidden representations of LayerNorm-based LLMs with models trained using RMSNorm and show that all LLMs naturally operate orthogonal to the uniform vector both during training and inference, that is, on average they do not have a component along the uniform vector during training or inference. This presents the first mechanistic evidence that removing the component along the uniform vector in LayerNorm is a redundant step. These results advocate for using RMSNorm over LayerNorm which is also more computationally efficient.

LayerNorm vs RMSNorm: A Geometric Perspective and the Case Against Mean Subtraction

In Recent years, advances in Neural Machine Translation (NMT) heavily rely on large-scale parallel corpora. Within the context of China's Belt and Road Initiative, there is increasing demand for improving translation quality from agglutinative languages (e.g., Mongolian, Arabic) to Chinese. However, the translation scenarios for agglutinative languages (which form words by concatenating morphemes with clear boundaries) face significant challenges including data sparsity, quality imbalance, and inactive sample proliferation due to their morphological complexity and syntactic flexibility. This study presents a systematic analysis of data distribution characteristics in agglutinative languages and proposes a dual-module framework combining fine-grained inactive sample identification with target-side rejuvenation. Our framework first establishes a multi-dimensional evaluation system to accurately identify samples exhibiting low-frequency morphological interference or long-range word order mismatches. Subsequently, the target-side rejuvenation mechanism generates diversified noise-resistant translations through iterative optimization of sample contribution weights. Experimental results on four low-resource agglutinative language tasks demonstrate significant performance improvements (BLEU +2.1--3.4) across mainstream NMT architectures. Architecture-agnostic validation further confirms the framework's generalizability.

Neural Machine Translation for Agglutinative Languages via Data Rejuvenation

Structured code comments in docstring format are essential for code comprehension and maintenance, but existing machine learning models for their generation perform poorly for Russian compared to English. To bridge this gap, we present StRuCom — the first large-scale dataset (153K examples) specifically designed for Russian code documentation. Unlike machine-translated English datasets that distort terminology (e.g., technical loanwords vs. literal translations) and docstring structures, StRuCom combines human-written comments from Russian GitHub repositories with synthetically generated ones, ensuring compliance with Python, Java, JavaScript, C#, and Go standards through automated validation. Fine-tuning Qwen2.5-Coder models (0.5B-7B) on StRuCom shows statistically significant improvements of chrf++ and BERTScore over baseline models.

StRuCom: A Novel Dataset of Structured Code Comments in Russian

Back-translation has been proven effective in enhancing the performance of Neural Machine Translation (NMT), with its core mechanism relying on synthesizing parallel corpora to strengthen model training. However, while traditional back-translation methods alleviate the data scarcity in low-resource machine translation, their dependence on random sampling strategies ignores the semantic quality of monolingual data. This results in the contamination of model training through the inclusion of substantial low-quality samples in the generated corpora. To mitigate noise interference, additional training iterations or model scaling are required, significantly increasing computational costs. To address this challenge, this study proposes a Semantic Uncertainty Sampling strategy, which prioritizes sentences with higher semantic uncertainty as training samples by computationally evaluating the complexity of unannotated monolingual data. Experiments were conducted on three typical low-resource agglutinative language pairs: Mongolian-Chinese, Uyghur-Chinese, and Korean-Chinese. Results demonstrate an average BLEU score improvement of +1.7 on test sets across all three translation tasks, confirming the method’s effectiveness in enhancing translation accuracy and fluency. This approach provides a novel pathway for the efficient utilization of unannotated data in low-resource language scenarios.

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Navigating the Prompt Space: Supervision Matters in CoT When Reasoning Misleads

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