activity
20192026
most citedRELIC: Investigating Large Language Model Responses using Self-Consistency

33 citations · 45 across the 40 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.CL2025

Generating Difficult-to-Translate Texts

Vilém Zouhar, Wenda Xu, Parker Riley +4

Machine translation benchmarks sourced from the real world are quickly obsoleted, due to most examples being easy for state-of-the-art translation models. This limits the benchmark…

cs.HC2025

CafGa: Customizing Feature Attributions to Explain Language Models

Alan Boyle, Furui Cheng, Vilém Zouhar +1

Feature attribution methods, such as SHAP and LIME, explain machine learning model predictions by quantifying the influence of each input component. When applying feature attributi…

cs.CL2025

Searching the Internet for Challenging Benchmarks at Scale

Wenda Xu, Vilém Zouhar, Parker Riley +3

Many static benchmarks are beginning to saturate: as models rapidly improve, they achieve near-perfect scores on fixed test sets, leaving little headroom to expose genuine model we…

cs.CL2025

When LLMs Benchmark Themselves: Deconstructing Self-Bias in Automated Evaluation

Wenda Xu, Sweta Agrawal, Vilém Zouhar +2

As LLMs rapidly saturate existing benchmarks, automated benchmark creation using LLMs (LLM as a benchmark) where a model generates test inputs (LLM as a testset) and evaluates outp…

cs.CL2025

Preliminary Ranking of WMT25 General Machine Translation Systems

Tom Kocmi, Eleftherios Avramidis, Rachel Bawden +25

We present the preliminary rankings of machine translation (MT) systems submitted to the WMT25 General Machine Translation Shared Task, as determined by automatic evaluation metric…

cs.CL2025

Early-Exit and Instant Confidence Translation Quality Estimation

Vilém Zouhar, Maike Züfle, Beni Egressy +3

Quality estimation is omnipresent in machine translation, for both evaluation and generation. Unfortunately, quality estimation models are often opaque and computationally expensiv…

cs.CL2025

How to Select Datapoints for Efficient Human Evaluation of NLG Models?

Vilém Zouhar, Peng Cui, Mrinmaya Sachan

Human evaluation is the gold standard for evaluating text generation models. However, it is expensive. In order to fit budgetary constraints, a random subset of the test data is of…