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Does task decomposition improve automatic NLG evaluation?
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arXiv Computation and Language
Sep 2
Does task decomposition improve automatic NLG evaluation?
The paper evaluates whether breaking down evaluation tasks into simpler sub‑tasks improves the LLM-as-a-judge (LLMaJ) framework for reference‑free NLG assessment. Systematic comparisons across multiple datasets show no performance advantage for LLMaJ methods that use task decomposition over a fair baseline that does not. The authors attribute previously reported gains to the use of human labels for training rather than to decomposition itself, and note that LLMaJ without decomposition can match human annotators when such labels are available.
arXiv Computation and Language
Oct 15
Fantastic Metrics and How to Find Them: Meta-Evaluating Metrics for Multi-lingual Style-Personalized Generation
arXiv:2610.19166v1 Announce Type: new Abstract: Meta-evaluation of text generation metrics and LLM-judges is primarily conducted using metric-human correlations. However, prior work has shown that co...
arXiv Computation and Language
Oct 9
Large Language Models for Machine Translation Quality Annotation: Humans and Models Are Both Challenged
Large Language Models (LLMs) are being tested as a cost‑effective alternative to human judgment for evaluating machine translation (MT). This study compares LLM agreement with human annotators on two MT quality schemes—Multidimensional Quality Metrics (MQM) and Error Span Annotation (ESA)—across 70 language pairs and public WMT23/25 data. Results show that while LLMs sometimes match or exceed human agreement, performance varies widely by scheme, language, and domain, and remains unreliable in many settings. The work also identifies specific challenges for both humans (e.g., fine‑grained MQM, low‑resource languages) and LLMs (e.g., minor errors, wrong language variants, span annotation).
arXiv Computation and Language
Oct 23
An Empirical Study of Recent Large Language Models for Knowledge Graph to Text Generation on Standard Benchmarks
The paper evaluates recent large language models (LLMs) for knowledge graph-to-text generation against traditional fine‑tuned encoder‑decoder models. Using both reference‑based metrics and graph‑grounded, reference‑free LLM‑based evaluation, the authors find that LLMs close the performance gap on reference metrics and outperform fine‑tuned models on graph‑grounded metrics. The study also shows that LLM‑based evaluation scores correlate poorly with reference metrics, indicating they assess different aspects of quality, and that prompted LLMs produce fewer contradictions and omissions, especially as graph size increases.
arXiv Computation and Language
Aug 31
Select, Label, Evaluate: Active Testing in NLP
The paper introduces Active Testing, a framework that selects the most informative test samples for annotation in NLP, aiming to reduce human effort while accurately estimating model performance. Experiments across 18 datasets and 4 embedding strategies show up to 95% annotation savings with less than 1% loss in performance estimation accuracy. The authors also propose an adaptive stopping criterion to determine the optimal number of samples without a predefined budget.
arXiv Computation and Language
Sep 22
Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generation
arXiv:2606.18389v2 Announce Type: replace Abstract: Large language models (LLMs) have become an effective tool for synthetic data generation, including for low-resource languages, where generated dat...