Austria

Free-text rationales justify model decisions in natural language and thus become likable and accessible among approaches to explanation across many tasks. However, their effectiveness can be hindered by misinterpretation and hallucination. As a perturbation test, we investigate how large language models (LLMs) perform rationale generation under the effects of readability level control, i.e., being prompted for an explanation targeting a specific expertise level, such as sixth grade or college. We find that explanations are adaptable to such instruction, though the observed distinction between readability levels does not fully match the defined complexity scores according to traditional readability metrics. Furthermore, the generated rationales tend to feature medium level complexity, which correlates with the measured quality using automatic metrics. Finally, our human annotators confirm a generally satisfactory impression on rationales at all readability levels, with high-school-level readability being most commonly perceived and favored.

ACL 2025

Free-text Rationale Generation under Readability Level Control

workshop paper

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
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Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

A frequently observed problem with LLMs is their tendency to generate output that is nonsensical, illogical, or factually incorrect, often referred to broadly as “hallucination”. Building on the recently proposed HalluciGen task for hallucination detection and generation, we evaluate a suite of open-access LLMs on their ability to detect intrinsic hallucinations in two conditional generation tasks: translation and paraphrasing. We study how model performance varies across tasks and languages and we investigate the impact of model size, instruction-tuning, and prompt choice. We find that performance varies across models but is consistent across prompts. Finally, we find that NLI models perform comparably well, suggesting that LLM-based detectors are not the only viable option for this specific task.

Can LLMs Detect Intrinsic Hallucinations in Paraphrasing and Machine Translation?

We introduce SECQUE, a comprehensive benchmark for evaluating large language models (LLMs) in financial analysis tasks. SECQUE comprises 565 expert-written questions covering SEC filings analysis across four key categories: comparison analysis, ratio calculation, risk assessment, and financial insight generation. To assess model performance, we develop SECQUE-Judge, an evaluation mechanism leveraging multiple LLM-based judges, which demonstrates strong alignment with human evaluations. Additionally, we provide an extensive analysis of various models' performance on our benchmark. By making SECQUE publicly available (https://anonymous.4open.science/r/sqbp-F6E6), we aim to facilitate further research and advancements in financial AI.

SECQUE: A Benchmark for Evaluating Real-World Financial Analysis Capabilities

Chatbots for customer service have been widely studied in many different fields, ranging from Natural Language Processing (NLP) to Communication Science. These fields have developed different evaluation practices to assess chatbot performance (e.g., fluency, task success) and to measure the impact of chatbot usage on the user's perception of the organisation controlling the chatbot (e.g., brand attitude) as well as their willingness to enter a business transaction or to continue to use the chatbot in the future (i.e., purchase intention, reuse intention). While NLP researchers have developed many automatic measures of success, other fields mainly use questionnaires to compare different chatbots. This paper explores the extent to which we can bridge the gap between the two, and proposes a research agenda to further explore this question.

Measure only what is measurable: towards conversation requirements for evaluating task-oriented dialogue systems

While LLMs excel at processing standard linguistic inputs but often underperform when faced with transformed or non-standard texts. This paper introduces a multilingual benchmark and evaluation framework designed to assess LLMs' ability to restore original meanings from heavily modified or playful text in both English and Korean. Our dataset includes visual and phonetic substitutions, abbreviations, and character-level manipulations, commonly found in informal digital communication. We conduct a comprehensive evaluation of state-of-the-art LLMs under three prompting paradigms: Zero-Shot, CoT, and CoT+ICL. Results highlight performance gaps, especially in Korean tasks compared to English tasks, and show how prompt engineering and model architecture influence robustness to linguistic variations. This benchmark emulates real-world linguistic distortions observed in informal and online settings, serving as a complementary resource to existing clean-text benchmarks. This allows us to identify robustness gaps in LLMs when handling realistic noisy inputs. It is accessible at: https://anonymous.4open.science/r/Text-Manipulation-C58B

Evaluating LLMs Beyond Standard Text: A Benchmark on Non-Traditional Text Variations

Referring Expression Generation (REG) is a core task for evaluating the pragmatic competence of vision-language systems, requiring not only accurate semantic grounding but also adherence to principles of cooperative communication~\citep{grice1975logic}. 
However, current evaluations of vision-language models (VLMs) often overlook the pragmatic dimension, reducing REG to a region-based captioning task and neglecting Gricean maxims. 
In this work, we revisit REG from a pragmatic perspective, introducing a new dataset of 1.5k images annotated with both written and spoken referring expressions. 
Through a systematic evaluation of state-of-the-art VLMs, we identify three key failures of pragmatic competence: (1) failure to uniquely identify the referent, (2) inclusion of excessive or irrelevant information, and (3) misalignment with human pragmatic preference, such as the underuse of minimal spatial cues. 
We also show that standard automatic evaluations fail to capture these pragmatic violations, reinforcing superficial cues rather than genuine referential success. 
Our findings call for a renewed focus on pragmatically informed models and evaluation frameworks that align with real human communication.

Vision-Language Models Are Not Pragmatically Competent in Referring Expression Generation

The impact of subword tokenization on language model performance is well-documented for perplexity, with finer granularity consistently reducing this intrinsic metric. However, research on how different tokenization schemes affect a model's understanding capabilities remains limited, particularly for non-Latin script languages. Addressing this gap, we conducted a comprehensive evaluation of six distinct tokenization strategies by pretraining transformer-based language models for Nepali and evaluating their performance across multiple downstream tasks. While recent prominent models like GPT, RoBERTa, Claude, LLaMA, Mistral, Falcon, and MPT have adopted byte-level BPE tokenization, our findings demonstrate that for Nepali, SentencePiece tokenization consistently yields superior results on understanding-based tasks. Unlike previous studies that primarily focused on BERT-based architectures, our research specifically examines sequential transformer models, providing valuable insights for language model development in low-resource languages and highlighting the importance of tokenization strategy beyond perplexity reduction.

Can Perplexity Predict Finetuning Performance? An Investigation of Tokenization Effects on Sequential Language Models for Nepali

Open-domain Dialogue (OD) exhibits a one-to-many (o2m) property, whereby multiple appropriate responses exist for a single dialogue context. Despite prior research showing that modeling this property boosts response diversity, most modern LLM-based dialogue agents do not explicitly do so. In this work, we model the o2m property of OD in LLMs by decomposing OD generation into two key tasks: Multi-Response Generation (MRG) and Preference-based Selection (PS), which entail generating a set of $n$ semantically and lexically diverse high-quality responses for a given dialogue context, followed by selecting a single response based on human preference, respectively. To facilitate MRG and PS, we introduce o2mDial, a dialogue corpus explicitly designed to capture the o2m property by featuring multiple plausible responses for each context. Leveraging o2mDial, we propose new in-context learning and instruction-tuning strategies, as well as novel evaluation metrics for MRG, alongside a model-based approach for PS. Empirical results demonstrate that applying the proposed two-stage framework to smaller LLMs for OD generation enhances overall response diversity while maintaining contextual coherence, improving response quality by up to 90\%, bringing them closer to the performance of larger models.

Modeling the One-to-Many Property in Open-Domain Dialogue with LLMs

Despite the outstanding performance of large language models (LLMs) across various NLP tasks, hallucinations in LLMs--where LLMs generate inaccurate responses--remains as a critical problem as it can be directly connected to a crisis of building safe and reliable LLMs. Uncertainty estimation is primarily used to measure hallucination levels in LLM responses so that correct and incorrect answers can be distinguished clearly. This study proposes an effective uncertainty estimation approach, Clustering-based semantic consistency (Cleanse). Cleanse quantifies the uncertainty with the proportion of the intra-cluster consistency in the total consistency between LLM hidden embeddings which contain adequate semantic information of generations, by employing clustering. The effectiveness of Cleanse for detecting hallucination is validated using four off-the-shelf models, LLaMA-7B, LLaMA-13B, LLaMA2-7B and Mistral-7B and two question-answering benchmarks, SQuAD and CoQA.

Cleanse: Uncertainty Estimation Approach Using Clustering-based Semantic Consistency in LLMs

We introduce CUS-QA, a manually curated, open-ended question-answering dataset specialized in local common knowledge.
The dataset contains 3,904 question-answer pairs across three regions (Czechia, Slovakia, Ukraine) and two modalities (text, visual).
CUS-QA is augmented with manual English translations to all questions and answers, as well as automatically generated baseline answers by a number of prominent open-weight large language models.
While the results using automatic metrics reveal differences between the tested models and languages,
the correlation between these metrics and human judgment is yet to be assessed, and our preliminary experiments suggest only a negligible match.

CUS-QA: Local-Knowledge-Oriented Open-Ended Question Answering Dataset

Evaluating the quality and relevance of textual outputs from Large Language Models (LLMs) remains challenging and resource-intensive.
Existing automated metrics often fail to capture the complexity and variability inherent in LLM-generated outputs.
Moreover, these metrics typically rely on explicit reference standards, limiting their use mostly to domains with objective benchmarks.
This work introduces a novel evaluation framework designed to approximate expert-level assessments of LLM-generated content.
The proposed method employs pairwise comparisons of outputs by multiple LLMs, reducing biases from individual models.
An Elo rating system is used to generate stable and interpretable rankings.
Adjustable agreement thresholds—from full unanimity to majority voting—allow flexible control over evaluation confidence and coverage.
The method’s effectiveness is demonstrated through evaluating competency profiles extracted from scientific abstracts.
Preliminary results show that automatically derived rankings correlate well with expert judgments, significantly reducing the need for extensive human intervention.
By offering a scalable, consistent, and domain-agnostic evaluation layer, the framework supports more efficient and reliable quality assessments of LLM outputs across diverse applications.

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Can LLMs Detect Intrinsic Hallucinations in Paraphrasing and Machine Translation?

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