United States

Curriculum learning trains models by progressively introducing examples from easy to difficult. While effective, two key challenges remain: how to define example difficulty and how to schedule training data appropriately. We present PUDF, a framework that addresses both challenges using Item Response Theory (IRT). First, IRT-AC estimates global, model-independent difficulty parameters for training examples by fitting an IRT model to response patterns from an artificial crowd of diverse LLMs. Second, DDS-MAE estimates model ability on the same IRT scale during each training epoch and selects training data accordingly. Experiments with Llama 3.1-8B and Qwen 2.5-7B across GLUE, AG News, MedQA, and GSM8K show that PUDF consistently improves accuracy and reduces training time by up to 70%, outperforming state-of-the-art curriculum learning methods.

ACL 2026

A Psychology-based Unified Dynamic Framework for Curriculum Learning

dynamic data selection

large language model fine-tuning

difficulty estimation

item response theory

curriculum learning

Welcome to the 18th 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 has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. 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. 
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EACL 2024

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.

How should we evaluate generation systems that combine autoregressive (AR) and diffusion decoding? We study this question through *Speculative Refinement* (SpecRef), a training-free hybrid method that warm-starts a masked diffusion language model from an AR draft using entropy-guided selective masking. Evaluating SpecRef across six benchmarks (HumanEval, MBPP, GSM8K, BBH, ARC-Challenge, HellaSwag) with three distinct evaluation protocols (execution-based pass@1, exact-match, log-likelihood scoring), we surface several findings relevant beyond our specific system: (1) code benchmarks conflate structural discovery} with logical correctness: providing a syntactic scaffold lifts accuracy from near zero to over 20\% without changing the model, indicating that much of the baseline failure is structural; (2) a *refinement tension* phenomenon where multi-stage correction degrades already-correct tokens, exposing benchmark saturation ceilings invisible to single-model evaluation; (3) log-likelihood and generative evaluation produce different model rankings for the same model pair, suggesting they measure different capabilities; (4) standard Python post-processing silently breaks code evaluation for non-AR generators. These observations apply to any multi-stage or non-autoregressive generation pipeline and point toward more diagnostic evaluation practices.

Speculative Refinement: A Hybrid Autoregressive Diffusion Decoding Strategy and Its Behavior Across Benchmarks

With the growing need for evaluating Large Language Models (LLMs) and their applications to speech, challenges persist in summarizing and evaluating conversations that lack a clear end goal. We introduce SAUCE -- a reference-free, fact-based evaluation pipeline for cross-lingual conversational speech summarization. It measures the accuracy and the fact coverage of a summary through the entailment between conversation and text. We compare SAUCE against several popular summarization metrics and demonstrate the effectiveness of capturing information loss due to transcription and translation error and identifying broken summaries. Crucially, unlike black-box LLM evaluators or dense embedding metrics, SAUCE is inherently explainable: it maps summary scores to discrete, verifiable facts, allowing users to pinpoint exact hallucinations or omissions. We illustrate how this interpretability helps developers systematically profile LLM behaviors and gives end-users an actionable tool to verify summary accuracy in noisy, real-world conditions. Preliminary investigations show SAUCE strongly align with human judgment.

SAFE: Summary Analysis via Fact Entailment for Cross-Lingual Conversational Speech Summarization

Evaluating automatic speech recognition (ASR) quality in production is a fundamentally challenging problem, owing to the volume and variability of real-time user interactions, thus making traditional methods impractical, and ground-truth annotations are typically unavailable at scale. We propose a large-scale evaluation pipeline that enables ASR quality evaluation via weakly supervised estimations derived from multiple independent ASR engines. The proxy estimations’ outputs are used to compute consensus-driven metrics such as word error rate (WER), character error rate (CER), and semantic divergence – to estimate transcription quality across varied query categories. These signals form the basis for a large language model (LLM)-driven insight agent, and custom tools to detect failure patterns, extract usage trends and behaviors, and provide actionable insights. This framework serves as a final layer of evaluation, which acts as an independent module compatible with any ASR system, regardless of its underlying architecture. By processing millions of queries periodically without manual intervention, this solution provides actionable insights that support the research and product team with valuable feedback for continuous improvement.

Evaluating ASR Quality at Scale on TV Entertainment Platforms

Multi-hop question answering is widely used to evaluate the reasoning capabilities of large language models (LLMs), as it requires integrating multiple pieces of supporting knowledge to arrive at a correct answer. While prior work has compared fine-tuning and retrieval-augmented generation (RAG) for factual recall and single-hop question answering, it remains unclear how these approaches perform in multi-hop settings that require compositional reasoning over temporally novel knowledge. In particular, prior comparisons often do not control for model scale, evaluation format, or knowledge freshness, making it difficult to isolate the effect of knowledge injection mechanisms. In this paper, we systematically compare parametric and non-parametric knowledge injection methods for open-domain multi-hop question answering. We evaluate unsupervised fine-tuning (continual pretraining), supervised fine-tuning, and retrieval-augmented generation across three 7B-parameter open-source LLMs. Experiments are conducted on two benchmarks: Question Answering Science Challenge (QASC), a standard multi-hop science question answering dataset, and a newly constructed dataset of over 10,000 multi-hop questions derived from Wikipedia events in 2024, which is designed to test knowledge beyond the models’ pretraining cutoff. Our results show that unsupervised fine-tuning provides only limited gains over base models, suggesting that continual pretraining alone is insufficient for improving multi-hop reasoning accuracy. In contrast, RAG yields substantial and consistent improvements, particularly when answering questions that rely on temporally novel information. Supervised fine-tuning achieves the highest overall accuracy across models and datasets. These findings highlight fundamental differences in how knowledge injection mechanisms support multi-hop question answering and underscore the importance of retrieval-based methods when external or compositional knowledge is required.

Fine-Tuning vs. RAG for Multi-Hop Question Answering with Novel Knowledge

Large language models (LLMs) are increasingly used in the mental health domain, yet it remains unclear how well they capture related biomedical knowledge and how reliably they apply it to clinically salient structured judgments. Here, we present a knowledge-graph (KG)-grounded benchmark for assessing LLMs on mental-health entity recognition, relation judgment, and two-hop reasoning. The benchmark is derived from PrimeKG and comprises nine task families with KG-supported answers and controlled negative options. Experiments across 15 closed- and open-source LLMs reveal a persistent recognition-to-judgment gap: leading models achieve near-ceiling performance on entity typing and on the small relation-typing subset, yet they still struggle with relation prediction and two-hop reasoning. Additionally, short KG-derived snippets benefit some models but degrade performance for others. Moreover, output-format reliability can substantially influence measured performance under constrained multiple-choice settings, highlighting the critical role of response validity in benchmark-based evaluation. MHGraphBench should therefore be interpreted as evaluating agreement with a curated mental-health slice of PrimeKG under a constrained multiple-choice interface, rather than as a direct assessment of real-world clinical safety.

MHGraphBench: Knowledge Graph-Grounded Benchmarking of Mental Health Knowledge in Large Language Models

Recent advancements in text-to-image generative models have improved narrative consistency in story visualization. However, current story visualization models often overlook cultural dimensions, resulting in visuals that lack cultural fidelity. In this study, we present a progressive evaluation framework for story visualization. We validate this framework on current text-to-image models across three languages (English, Hindi, and Chinese) on two datasets (VIST and FlintstonesSV). The proposed framework introduces three levels of cultural analysis as evaluation rubrics: 1) Basic Cultural Criteria, 2) Cultural Dimension Guidance, and 3) Cultural Examples Grounding. We evaluate story visualization by use of a novel MLLM-as-Jury approach across all three rubrics and a small-scale human evaluation only on the third rubric. We implement an MLLM-as-jury approach by aggregating scores from three different families of MLLM-as-Judge models. In our experiments, real-world stories generally receive higher cultural appropriateness scores than animated ones, with English tending to score higher than Hindi and Chinese across the evaluated models. Some examples also exhibited culturally inconsistent or stereotypical elements noted by annotators. The proposed progressive evaluation framework has therefore been shown to provide early insights into cultural misalignments in story visualization. Code for this work is made available on https://github.com/janak11111/Cultural_Eval_For_StoryViz

A Progressive Evaluation Framework for Multicultural Analysis of Story Visualization

As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for different RAG scenarios evaluation and comparison on semi-structured knowledge bases, including regular RAG, GraphRAG, Modular RAG and Agentic RAG. We provide implementation for 9 standardized RAG scenarios, and conduct experiments for a comprehensive comparison. These scenarios are designed for real use cases regarding data and domain restrictions, spanning from simple document-based retrieval to advanced features such as hybrid text-graph retrieval, integration with computed or pre-defined domain knowledge graphs, agentic multi-step planning, and agent-graph integration. Besides, we present a novel context engineering method for GraphRAG and Agentic RAG, addressing the context/memory overflow issues, efficiently managing text and graph retrievals with new representations and agentic loop design, leading to 19%-53% reduction on token usage. Moreover, further analysis identifies a retrieval-generation gap where expanded retrieval does not proportionally improve generation quality, suggesting retrieval-oriented metrics overstate advanced retrieval benefits. This work provides data-driven insights on when and how to use them for building production-ready intelligent RAG systems.

Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization

Automatic evaluation in industrial settings requires models to interpret and apply natural language rubrics reliably under language and domain shift. This challenge is compounded when reference answers are unavailable and proprietary models cannot be deployed due to data-governance constraints. We present E-Star-12B, a 12B-parameter evaluator for Korean industrial environments that jointly addresses rubric following and domain adaptation. Our approach combines a structured evaluation format—feedback, highlight, and decision—with a 6K high-confidence training set via multi-stage consensus-based filtering. We introduce two benchmarks: Ko Feedback Bench for rubric-following evaluation under Korean language transfer, and RAG Quality Bench for domain-specific evaluation in financial and legal settings. E-Star-12B achieves the strongest rubric alignment among small language models on Ko Feedback Bench, improving Pearson correlation by +0.173 over its base model. On RAG Quality Bench, the domain-adapted variant approaches frontier-model performance with more stable adaptation than general instruct models. Strong rubric-following capability serves as a reliable scaffold for subsequent domain adaptation.

E-star 12B: Reliable Rubric-Following and Domain-Adaptive SLM Evaluator for Korean Industrial Settings

Linear probes trained on internal activations of Large Language Models (LLMs) are increasingly proposed as evaluation metrics for deceptive generation, automated monitors that score whether a model's output was produced deceptively, without requiring ground-truth labels or human annotation. Yet these metrics report AUROC scores exceeding 0.96 on clean benchmarks while demonstrating profound fragility under distributional shift. This paper presents a systematic pressure-test of such probe-based evaluation metrics across the Gemma 3 model family (1B–27B parameters), diagnosing why they fail rather than merely documenting that they fail. We investigate four competing hypotheses about how deception is encoded: as (1) a single linear direction, (2) a multi-dimensional subspace, (3) a convex conic hull, or (4) a proxy for computational entropy. Our experimental design includes cross-domain transfer matrices, multi-dimensional probe analysis with permutation null baselines, entropy-residualization tests, and systematic distractor evaluations across 8 stylistic shifts. Across all four model scales, we find that: (a) probe-based metrics achieve near-perfect AUROC ($\ge$0.998) on clean data but collapse under stylistic shifts when trained without stylistic augmentation, style-augmented probes recover near-perfect detection (mean AUROC~0.979--0.983) even on unseen styles; (b) the single-direction hypothesis is decisively rejected (k=1 captures only 0.61–0.80 AUROC of the signal, with cross-domain transfer failure confirmed as geometric rather than layer-mismatch-driven; (c) the entropy-proxy hypothesis is rejected (maximum $|\rho|=0.454$, maximum $\Delta$AUROC after residualization=0.004); and (d) deception does not form a statistically significant linear subspace even within individual domains (per-domain $k^*{=}0$), yet multi-dimensional probes (k$\ge$5) consistently recover the signal through distributed sub-threshold features. These findings demonstrate that probe fragility under standard training reflects distributional narrowness rather than a fundamental architectural limitation: style-augmented probes recover near-perfect detection (mean AUROC 0.979–0.983 on unseen styles) at both the 4B and 27B scales, establishing that the inverse scaling pattern observed under standard training is a training-distribution artifact rather than a genuine scale-dependent phenomenon.

Pressure-Testing Deception Probes in LLMs: Scaling, Robustness, and the Geometry of Deceptive Representations

Automated evaluation of student responses has developed along two largely separate tracks: Automated Essay Scoring (AES), which emphasizes holistic writing quality, and Automatic Short Answer Grading (ASAG), which emphasizes factual correctness and rubric alignment. We introduce S-GRADES, an open-source benchmark consolidating 14 AES and ASAG datasets (37K+ responses) with a standardized evaluation framework, and use it to systematically evaluate three frontier LLMs across six reasoning configurations. Our results show that hybrid prompting generally performs best, AES is more predictable than ASAG, and cross-dataset exemplar transfer is often unstable, especially across paradigms. These findings highlight the need for unified, benchmark-driven evaluation of reliability and generalization in LLM-based student assessment. This work is published at LREC-2026 and available on arXiv (https://arxiv.org/abs/2603.10233). The benchmark is publicly accessible at https://sgrades.eng.unt.edu.

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Item Response Theory for Natural Language Processing

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