# benchmark overfitting machine learning

A machine-learning evaluation problem in which adaptively reusing a held-out benchmark can cause overfitting to that benchmark.

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## When LLM judges agree, should we believe them?

DevFeed: [When LLM judges agree, should we believe them?](<https://devfeed.tech/articles/when-llm-judges-agree-should-we-believe-them-7609.md>)

Original publisher: [Read original article](<https://www.amazon.science/blog/when-llm-judges-agree-should-we-believe-them>)

Author: Krishna Balasubramanian; Sasha Podkopaev

Published: 2026-08-26T17:10:40Z

Content type: article

Language: en

Sources: [Amazon Science homepage](<https://devfeed.tech/sources/amazon-science-homepage.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Retrieval-Augmented Generation](<https://devfeed.tech/topics/retrieval-augmented-generation.md>), [Ising](<https://devfeed.tech/topics/ising.md>), [benchmark overfitting machine learning](<https://devfeed.tech/topics/benchmark-overfitting-machine-learning.md>), [Network](<https://devfeed.tech/topics/network.md>), [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>)

Tags: [conversational-ai](<https://devfeed.tech/tags/conversational-ai.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [ising](<https://devfeed.tech/tags/ising.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [llm](<https://devfeed.tech/tags/llm.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [retrieval-augmented-generation](<https://devfeed.tech/tags/retrieval-augmented-generation.md>)

### AI overview

The article examines whether agreement among LLM judges is trustworthy when their outputs may be correlated. It presents a dependence-aware aggregation method based on Ising models that discounts shared blind spots and outperforms historical-accuracy-weighted majority voting on three tasks.

### Source excerpt

Discounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives.

## Measuring benchmark optimization in speech recognition

DevFeed: [Measuring benchmark optimization in speech recognition](<https://devfeed.tech/articles/measuring-benchmark-optimization-in-speech-recognition-7104.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/asr-benchmark-optimization>)

Author: Theo Lebryk; Eric Bezzam; Alice; David Ayllon; Jakub Piotr Cłapa; Jens Madsen; Panagiotis Tzirakis

Published: 2026-08-21T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [asr](<https://devfeed.tech/topics/asr.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [benchmark overfitting machine learning](<https://devfeed.tech/topics/benchmark-overfitting-machine-learning.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [asr](<https://devfeed.tech/tags/asr.md>), [audio](<https://devfeed.tech/tags/audio.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [errors](<https://devfeed.tech/tags/errors.md>), [measurement](<https://devfeed.tech/tags/measurement.md>), [model](<https://devfeed.tech/tags/model.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [research](<https://devfeed.tech/tags/research.md>), [speech](<https://devfeed.tech/tags/speech.md>), [transcription](<https://devfeed.tech/tags/transcription.md>)

### AI overview

The article examines benchmark optimization, or "benchmaxxing," in speech recognition. It presents three tests and evaluates 11 open-source ASR models, finding that some reproduced benchmark transcripts even when the audio contradicted them. The research also uses model ensembles and human annotations to identify and validate likely benchmark errors.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.