United States

The critique of scalar benchmark rankings as proxies for model quality is now well-established (Raji et al., 2021; Wallach et al., 2025; Bean et al., 2025; Gehrmann et al., 2021). What the field still lacks is a shared structural vocabulary for comparing, combining, and contextualizing metric design choices. This paper provides that vocabulary: a four-primitive typology---representation ($\phi$), comparison ($D$), aggregation ($A$), and context ($C$)---under which existing metrics (BLEU, BERTScore, nDCG, LLM-as-judge, calibration scores, agentic outcome measures) are explicit parameterizations of a common form. This typology is paired with a measurement--decision split: metrics are noisy estimators of latent constructs, and model selection is context-dependent Pareto optimization over construct estimates, not over raw scores. The typology makes implicit metric assumptions comparable and debatable rather than hidden inside a single number.

ACL 2026

Toward a Metric Typology for Language Model Evaluation

multi-objective evaluation

benchmarking and leaderboards

llm-as-a-judge

language model evaluation

evaluation metrics

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

EACL 2023

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

workshop paper

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

Recent advances in large language models (LLMs) have demonstrated impressive medical reasoning capabilities. However, current evaluation methods are mostly limited to static case vignettes and multiple-choice questions which fail to reflect the complexity, uncertainty, and iterative nature of real-world clinical decision-making. To bridge this gap, we propose **DiagBench**, a novel benchmark where models interact dynamically with a LLM based Patient Simulator, querying relevant clinical details to formulate accurate diagnoses. To complement this, we introduce **MedConvBench**, a diagnostic conversation benchmark designed to assess the relevance and quality of model-generated clinical reasoning. To further address the interpretability and alignment challenges of AI-assisted diagnosis, we develop a modular and medically grounded framework called **VAIDYA** that mirrors a physician’s stepwise diagnostic reasoning. This structured approach improves transparency and yields substantial performance gains over base LLMs. Our work takes a critical step toward aligning AI systems with real-world clinical practices by combining dynamic interaction, interpretability, and clinical validation.

VAIDYA: Validated Agents for Intelligent Diagnosis and Yielded Analysis

Self-Anchoring Calibration Drift} (SACD), a tendency for large language models (LLMs) to show systematic changes in expressed confidence when building iteratively on their own prior outputs across multi-turn conversations. Through a controlled three-condition study comparing Claude Sonnet 4.6, Gemini 3.1 Pro, and GPT-5.2 across factual, technical, and open-ended domains, we find that SACD is real but multiform: models exhibit distinct self-anchoring signatures ranging from active confidence suppression to calibration improvement suppression, with effects concentrated in open-ended domains. These findings challenge the adequacy of single-turn calibration evaluation for characterizing LLM reliability in realistic multi-turn deployment contexts. Code and data are available at https://github.com/hvardhan878/calibration-drift

Self-Anchoring Calibration Drift in Large Language Models: How Multi-Turn Conversations Reshape Model Confidence

Representing continuous time is a critical and under-explored challenge in modeling temporal event sequences with large language models (LLMs). Various strategies like byte-level representations or calendar tokens have been proposed. However, the optimal approach remains unclear, especially given the diverse statistical distributions of real-world event data, which range from smooth log-normal to discrete, spiky patterns. This paper presents a systematic empirical study of temporal tokenization for modeling event sequences with LLMs, comparing distinct encoding strategies: naive numeric strings, high-precision byte-level representations, human-semantic calendar tokens, classic uniform binning, and adaptive residual scalar quantization. We evaluate these strategies by fine-tuning LLMs on real-world datasets that exemplify these diverse distributions. Our analysis reveals that no single strategy is universally superior; instead, prediction performance depends heavily on aligning the tokenizer with the data's statistical properties, highlighting temporal tokenization as a critical yet often overlooked design dimension in LLM-based event modeling.

Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Models

We present a large-scale human evaluation benchmark for assessing cultural localisation in machine translation produced by state-of-the-art multilingual large language models (LLMs). Existing MT benchmarks emphasise token-level and grammatical accuracy, but often overlook pragmatic and culturally grounded competencies required for real-world localisation. Building on a pilot study of 87 translations across 20 languages, we evaluate 7 multilingual LLMs across 15 target languages with 5 native-speaker raters per language. Raters scored both full-text translations and segment-level instances of culturally nuanced language (idioms, puns, holidays, and culturally embedded concepts) on an ordinal 0–3 quality scale; segment ratings additionally included an NA option for untranslated segments. Across full-text evaluations, mean overall quality is modest (1.68/3): GPT-5 (2.10/3), Claude Sonnet 3.7 (1.97/3), and Mistral Medium 3.1 (1.84/3) form the strongest tier with fewer catastrophic failures. Segment-level results show sharp category effects: holidays (2.20/3) and cultural concepts (2.19/3) translate substantially better than idioms (1.65/3) and puns (1.45/3), and idioms are most likely to be left untranslated. These findings demonstrate a persistent gap between grammatical adequacy and cultural resonance. To our knowledge, this is the first multilingual, human-annotated benchmark focused explicitly on cultural nuance in translation and localisation, highlighting the need for culturally informed training data, improved cross-lingual pragmatics, and evaluation paradigms that better reflect real-world communicative competence.

“Be My Cheese?”: Cultural Nuance Benchmarking for Machine Translation in Multilingual LLMs

The field of natural language generation has accumulated a rich ecosystem of automatic evaluation metrics, yet it lacks a coherent theory of what those metrics are actually measuring. Drawing on measurement theory from the quantitative social sciences, this paper argues that current NLG evaluation practices suffer from a fundamental construct validity problem: metrics are treated as proxies for output quality without explicit specification of the underlying constructs they are meant to operationalize. We examine four dominant evaluation paradigms (reference-based metrics, embedding-based metrics, LLM-as-judge, and human evaluation) and demonstrate that each conflates construct definition with operationalization. Building on a long psychometric tradition reaching back to Cronbach and Meehl (1955) and on recent NLP work that has begun to apply this tradition to bias measurement, dialogue evaluation, and benchmark design, we propose that the field adopt a measurement modeling perspective for NLG evaluation. We borrow the concepts of construct validity, reliability, and consequential validity as a foundation for more principled evaluation, and we outline a preliminary taxonomy of NLG quality constructs as a starting point for this work.

Position: What Are We Measuring? Rethinking Evaluation in Natural Language Generation

Post-training methods such as supervised fine-tuning and preference optimization are widely used to align large language models, yet how their benefits distribute across architectural components and transfer across tasks and prompts remains unclear. In this work, we analyze component-level transfer in a Mixture-of-Experts language model by selectively replacing routers, attention modules, and expert networks between two post-trained Mixture of Experts models trained with different post-training recipes and dataset mixtures. Starting from a SFT+DPO checkpoint, we systematically replace its components (routers, attention, experts) with those from a Tulu3 checkpoint and evaluate the impact of each replacement and their combinations on mathematical and scientific reasoning and a general-purpose classification task under zero-shot, few-shot and Chain of Thought prompting. We find strong component-specific specialization: expert networks account for most gains on mathematical and scientific reasoning, while attention mechanisms consistently outweigh expert transfer on general tasks and router transfer alone provides minimal benefit or harms performance. Prompting strategy further modulates these effects, with expert transfer degrading zero-shot science performance but improving few-shot reasoning. Strategically combining components from different model versions can in some cases match or exceed the performance of the best available model, motivating principled techniques for composing post-trained models into task- and prompt-specific systems without additional training.

Component Transfer Can Exceed Full Model Performance: Investigating Post-Trained Mixture-of-Experts

Large language models (LLMs) are increasingly evaluated using LLM-as-a-Judge frameworks, yet existing approaches often rely on fixed evaluation criteria that assume universal notions of answer quality. In expert domains, however, evaluation standards vary across communities and reflect domain-specific priorities. We introduce CAMEL, a framework for learning Community-Aligned Multi-metric Evaluator LLMs directly from large-scale human preference data. Using question–answer interactions from Stack Exchange communities, CAMEL discovers community-specific evaluation metrics, generates rubric-based scoring criteria, and learns metric importance weights that reproduce observed preference orderings. This process converts implicit community preferences into structured supervision for training evaluator models. The resulting evaluator produces interpretable reports that include metric-level scores, rubric-grounded explanations, and a calibrated overall score aligned with community norms. Experiments across seen and unseen communities show that CAMEL improves preference-order prediction compared to prompting-based judges and general-purpose evaluation models. In addition, a human-subject study with domain practitioners demonstrates that CAMEL’s metric-based evaluation reports are strongly preferred for their transparency, consistency, and diagnostic usefulness. Our results highlight the importance of community-aligned evaluation design and suggest that preference data from real-world expert communities can be leveraged to build more interpretable and human-aligned LLM evaluators.

CAMEL: Learning Community-Aligned Metrics and Weights for LLM Evaluation

Extractive QA tasks are commonly evaluated using Exact Match (EM) and F1-score, but these metrics often fail to reflect true model performance. Recent studies have proposed using large language models (LLMs) as judges (LLM-as-a-judge), yet they often lack comprehensive evaluation across datasets and overlook key factors such as sensitivity to answer types, prompt variations, and self-preference bias. In this work, we conduct a systematic study of LLM-as-a-judge across four extractive QA datasets and various prompt variations, assessing multiple LLM families in both answering and judging roles. Our results show that LLM-as-a-judge judgments correlate much more strongly with human evaluations than EM (0.22) and F1 (0.40), achieving correlations up to 0.85 with open-source models. Further analysis reveals that LLM-as-a-judge performs particularly well on number-related answers but faces challenges with more complex types, such as job titles. Contrary to findings in other NLP tasks, we observe no self-preference bias, even when the same model serves as both QA model and judge. Finally, we find that prompt phrasing has minimal impact, and zero-shot, context-free judging often yields the best evaluation performance.

Reassessing Extractive QA Datasets at Scale: LLM-as-a-Judge and In-Depth Analyses

Indic languages, spoken by over 1.5 billion people, pose unique challenges for NLP due to their cultural richness, linguistic diversity, and structural complexity. We present IndicMMLU-Pro, a comprehensive benchmark extending the MMLU-Pro framework to nine major Indic languages: Hindi, Bengali, Gujarati, Marathi, Kannada, Punjabi, Tamil, Telugu, and Urdu. Covering a wide range of tasks in comprehension, reasoning, and generation, IndicMMLU-Pro offers a standardized evaluation framework to advance AI model development in Indic contexts. This paper details the benchmark’s design, taxonomy, and data curation, and establishes baseline results using state-of-the-art multilingual models. As an open resource IndicMMLU-Pro aims to accelerate progress in Indic language technologies and support inclusive research in multilingual NLP.

IndicMMLU-Pro: Benchmarking Indic Large Language Models on Multi-Task Language Understanding

We design experiments to identify where Large Language Models (LLMs) struggle when answering complex questions. Our focus is on two key stages, mirroring the human QA process: 1) \textit{question decomposition}, where the model breaks down a complex question into sub-questions and 2) \textit{subproblem solving}, where it addresses each sub-question to obtain the final response. We preprocess and expand three multi-hop datasets to create experimental datasets featuring explicit and implicit multi-hop questions, crowdsourced and templated questions, and varying numbers of hops. Our results show that larger models (Llama 3.1 70B and o1) excel at decomposing explicit multi-hop questions but struggle with implicit ones, while smaller models (e.g., Llama 3.1 8B) have difficulty with both. In the sub-problem solving stage, all models perform well on simple questions with context. Furthermore, we found no correlation between accuracy in the question decomposition stage and final QA performance (direct response), highlighting a key difference between human and LLM reasoning.

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