← Back to all news
arXiv Machine Learning By Ahmed El Kady, Aravind Narayanan, Rehana Noorani, Yani Ioannou, Shaina Raza

Stress-Testing Efficient Responsible-AI Evaluation: When Compute Savings Change Benchmark Conclusions

Read the original on arXiv Machine Learning →

The paper investigates how making responsible‑AI evaluations more efficient—through batching, quantization, and benchmark reduction—affects the stability of conclusions drawn about model behavior. By testing three dense and mixture‑of‑experts models on the BBQ and BBQ‑V datasets under seven different conditions, the authors compare accuracy, bias, reasoning quality, subgroup performance, subset‑membership stability, runtime, and GPU energy consumption against a full‑benchmark BF16 baseline. Findings show that larger batching preserves accuracy and reduces energy in most settings, INT8 largely maintains quality but can increase energy use, INT4 introduces larger, context‑dependent changes, and reduced benchmarks save resources but are highly sensitive to which items are retained, underscoring that efficient evaluation must be validated against the benchmark’s intended conclusions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Sep 21

Balance of Benchmarks: Semantic Density Reweighting for Task-Conditioned Model Comparison

The paper introduces Balance of Benchmarks (BoB), a framework that improves task-conditioned model comparison by weighting benchmark evidence based on semantic density, equating scores across varying difficulty levels, and pooling task-relevant residuals. BoB retains all eligible benchmark data while adjusting its influence, outperforming uniform averaging on the WildScores dataset with higher Spearman correlation, lower MAE, and better shortlist hit rates. The method also reduces ranking instability when benchmarks are repeated or paraphrased, and lowers retrospective regret in model selection.

By Jhen-Ke Lin, Hong-Yun Lin
benchmarks
More like this →
arXiv AI
Aug 26

Evaluating Multiple LLM Generations with Validated Task Coverage

The paper introduces VTC-Bench, a five‑domain benchmark designed to evaluate multiple outputs from large language models (LLMs) by measuring Validated Task Coverage (VTC). VTC quantifies how many distinct, useful results are produced within a set number of attempts, using real‑data tasks that allow automatic, reproducible checks of output quality and task‑relevant distinctness without relying on model‑based judges. Experiments show that models which perform best on single‑draw quality do not always achieve the highest coverage, and simple output‑variation metrics fail to capture task‑relevant diversity, highlighting the importance of evaluating finite candidate sets directly.

By Florian Le Bronnec, Rio Yokota
llmsbenchmarks
More like this →
arXiv AI
Sep 17

The Inference Engineering Pareto Atlas: Which Optimizations Dominate the Cost, Quality, and Latency Frontier?

The paper presents a Pareto atlas of LLM inference optimizations, mapping cost, quality, and latency trade‑offs for Qwen2.5‑7B‑Instruct on L4, A100, and H100 GPUs. Using 54 measured configurations and a calibrated simulator, it identifies 18 of 36 setups on the Pareto frontier, showing that combined methods outperform single ones. Quality tests reveal that AWQ 4bit and FP8 weights offer significant latency reductions while largely preserving accuracy, but naive FP8 KV caching fails to answer any questions correctly.

By Srikanta Datta Tumkur, Jay Iyer, Mehar Simhadri, Sai Pavan Kumar, Sai Kapil Kumar, Ramesh Nampelly
llmsefficiency
More like this →