Austria

With rapid advancements in large language models (LLMs) across artificial intelligence, machine learning, and data sci-ence, there is a growing need for evaluation frameworks that go beyond traditional performance metrics. Conventional methods focus mainly on accuracy and computational metrics, often neglecting user experience and community interaction—key elements in open-source environments. This paper intro-duces a multi-dimensional, user-centered evaluation frame-work, integrating metrics like User Engagement Index (UEI), Community Response Rate (CRR), and a Time Weight Factor (TWF) to assess LLMs&#39; real-world impact. Additionally, we propose an adaptive weighting mechanism using Bayesian op-timization to dynamically adjust metric weights for more ac-curate model evaluation. Experimental results confirm that our framework effectively identifies models with strong user engagement and community support, offering a balanced, data-driven approach to open-source LLM evaluation. This frame-work serves as a valuable tool for developers and researchers in selecting and improving open-source models.

ACL 2025

Multi-Dimensional Evaluation of Open-Source Language Models: Based on Machine Learning and Bayesian Optimization

With rapid advancements in large language models (LLMs) across artificial intelligence, machine learning, and data sci-ence, there is a growing need for evaluation frameworks that go beyond traditional performance metrics. Conventional methods focus mainly on accuracy and computational metrics, often neglecting user experience and community interaction—key elements in open-source environments. This paper intro-duces a multi-dimensional, user-centered evaluation frame-work, integrating metrics like User Engagement Index (UEI), Community Response Rate (CRR), and a Time Weight Factor (TWF) to assess LLMs' real-world impact. Additionally, we propose an adaptive weighting mechanism using Bayesian op-timization to dynamically adjust metric weights for more ac-curate model evaluation. Experimental results confirm that our framework effectively identifies models with strong user engagement and community support, offering a balanced, data-driven approach to open-source LLM evaluation. This frame-work serves as a valuable tool for developers and researchers in selecting and improving open-source models.

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.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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

Spatial representations are fundamental to human cognition, as understanding spatial relationships between objects is essential in daily life. Language serves as an indispensable tool for communicating spatial information, creating a close connection between spatial representations and spatial language. Large language models (LLMs), theoretically, possess spatial cognition due to their proficiency in natural language processing. This study examines the spatial representations of LLMs by employing traditional spatial tasks used in human experiments and comparing the models' performance to that of humans. The results indicate that LLMs resemble humans in selecting spatial prepositions to describe spatial relationships and exhibit a preference for vertically oriented spatial terms. However, the human tendency to better represent locations along specific axes is absent in the performance of LLMs. This finding suggests that, although spatial language is closely linked to spatial representations, the two are not entirely equivalent.

Spatial Representation of Large Language Models in 2D Scene

This paper presents a novel methodology for generating synthetic Preference Optimization (PO) datasets using multi-model workflows. We evaluate the effectiveness and potential of these workflows in automating and enhancing the dataset generation process. PO dataset generation requires two modules: (1) $\textit{response evaluation}$, and (2) $\textit{response generation}$. In the $\textit{response evaluation}$ module, the responses from Large Language Models (LLMs) are evaluated and ranked - a task typically carried out by human annotators that we automate using LLMs. We assess the response evaluation module in a 2 step process. In step 1, we assess LLMs as evaluators using three distinct prompting strategies. In step 2, we apply the winning prompting strategy to compare the performance of LLM-as-a-Judge, LLMs-as-a-Jury, and LLM Debate. Our evaluation shows that GPT-4o-as-a-Judge is more consistent across all datasets. For the $\textit{response generation}$ module, we use the identified LLM evaluator configuration and compare different configurations of the LLM Feedback Loop. We use the win rate to determine the best multi-model configuration for generation. Experimenting with various configurations, we find that the LLM Feedback Loop, with Llama as the generator and Gemma as the reviewer, achieves a notable 71.8\% and 73.8\% win rate over single-model Llama and Gemma, respectively. After identifying the best configurations for both modules, we generate our PO datasets using the above pipeline.

The Fellowship of the LLMs: Multi-Model Workflows for Synthetic Preference Optimization Dataset Generation

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.

Free-text Rationale Generation under Readability Level Control

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.

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