The Base-Rate Effect on LLM Benchmark Performance: Disambiguating Test-Taking Strategies from Benchmark Performance

• URL: http://arxiv.org/abs/2406.11634v2
• Date: Mon, 30 Sep 2024 17:51:11 GMT
• Title: The Base-Rate Effect on LLM Benchmark Performance: Disambiguating Test-Taking Strategies from Benchmark Performance
• Abstract summary: We show that the base-rate probability (BRP) differences across answer tokens are significant and affect task performance ie. guess A if uncertain. We propose the Nvr-X-MMLU task, a variation of MMLU, which helps to disambiguate test-taking ability from task performance and reports the latter.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Cloze testing is a common method for measuring the behavior of large language models on a number of benchmark tasks. Using the MMLU dataset, we show that the base-rate probability (BRP) differences across answer tokens are significant and affect task performance ie. guess A if uncertain. We find that counterfactual prompting does sufficiently mitigate the BRP effect. The BRP effect is found to have a similar effect to test taking strategies employed by humans leading to the conflation of task performance and test-taking ability. We propose the Nvr-X-MMLU task, a variation of MMLU, which helps to disambiguate test-taking ability from task performance and reports the latter.

Related papers

• Agent psychometrics: Task-level performance prediction in agentic coding benchmarks [24.348135523715815]
  We present a framework for predicting success or failure on individual tasks tailored to the agentic coding regime.<n>Our approach augments Item Response Theory (IRT) with rich features extracted from tasks, including issue statements, repository contexts, solutions, and test cases.
  arXiv  Detail & Related papers  (2026-04-01T07:59:59Z)
• Explainable Semantic Textual Similarity via Dissimilar Span Detection [53.32175252285023]
  We introduce the task of Dissimilar Span Detection (DSD), which aims to identify semantically differing spans between pairs of texts.<n>This can help users understand which particular words or tokens negatively affect the similarity score, or be used to improve performance in STS-dependent downstream tasks.
  arXiv  Detail & Related papers  (2026-03-22T11:32:31Z)
• Fluid Language Model Benchmarking [126.92394365620525]
  We introduce Fluid Benchmarking, a new evaluation approach that advances LM benchmarking across multiple dimensions.<n>Inspired by psychometrics, Fluid Benchmarking is based on the insight that the relative value of benchmark items depends on an LM's capability level.<n>We examine four dimensions -- efficiency, validity, variance, and saturation -- and find that Fluid Benchmarking achieves superior performance in all of them.
  arXiv  Detail & Related papers  (2025-09-14T05:49:42Z)
• LLM Performance for Code Generation on Noisy Tasks [0.41942958779358674]
  We show that large language models (LLMs) can solve tasks obfuscated to a level where the text would be unintelligible to human readers.<n>We report empirical evidence of distinct performance decay patterns between contaminated and unseen datasets.<n>We propose measuring the decay of performance under obfuscation as a possible strategy for detecting dataset contamination.
  arXiv  Detail & Related papers  (2025-05-29T16:11:18Z)
• No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success [1.950171084881346]
  Large language models (LLMs) capable of producing general-purpose representations lets us revisit the practicality of deep active learning (AL)<n>This study establishes a benchmark and systematically investigates the influence of LLM embedding quality on query strategies in deep AL.
  arXiv  Detail & Related papers  (2025-05-18T10:38:26Z)
• Beyond the Singular: The Essential Role of Multiple Generations in Effective Benchmark Evaluation and Analysis [10.133537818749291]
  Large language models (LLMs) have demonstrated significant utilities in real-world applications. Benchmark evaluations are crucial for assessing the capabilities of LLMs.
  arXiv  Detail & Related papers  (2025-02-13T03:43:33Z)
• Confident or Seek Stronger: Exploring Uncertainty-Based On-device LLM Routing From Benchmarking to Generalization [61.02719787737867]
  Large language models (LLMs) are increasingly deployed and democratized on edge devices. One promising solution is uncertainty-based SLM routing, offloading high-stakes queries to stronger LLMs when resulting in low-confidence responses on SLM. We conduct a comprehensive investigation into benchmarking and generalization of uncertainty-driven routing strategies from SLMs to LLMs over 1500+ settings.
  arXiv  Detail & Related papers  (2025-02-06T18:59:11Z)
• BenTo: Benchmark Task Reduction with In-Context Transferability [32.561978389905434]
  This paper investigates how to efficiently reduce the tasks used to benchmark large language models (LLMs) We propose a practically efficient metric for estimating the transferability between two tasks via in-context learning (ICL)
  arXiv  Detail & Related papers  (2024-10-17T17:41:15Z)
• Active Testing of Large Language Model via Multi-Stage Sampling [17.89896012553348]
  AcTracer is an active testing framework tailored for large language models (LLMs) It strategically selects a small subset of test data to achieve a nearly optimal performance estimation. Our experiment results demonstrate that AcTracer achieves state-of-the-art performance compared to existing methods.
  arXiv  Detail & Related papers  (2024-08-07T06:17:48Z)
• Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation [61.350306618479365]
  Leakage of benchmarks can prevent the accurate assessment of large language models' true performance. We propose Inference-Time Decontamination (ITD) to address this issue. ITD reduces inflated accuracy by 22.9% on GSM8K and 19.0% on MMLU.
  arXiv  Detail & Related papers  (2024-06-20T04:35:59Z)
• On the Worst Prompt Performance of Large Language Models [93.13542053835542]
  Performance of large language models (LLMs) is acutely sensitive to the phrasing of prompts. We introduce RobustAlpacaEval, a new benchmark that consists of semantically equivalent case-level queries. Experiments on RobustAlpacaEval with ChatGPT and six open-source LLMs from the Llama, Mistral, and Gemma families uncover substantial variability in model performance.
  arXiv  Detail & Related papers  (2024-06-08T13:40:38Z)
• Uncertainty Aware Learning for Language Model Alignment [97.36361196793929]
  We propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios. We implement UAL in a simple fashion -- adaptively setting the label smoothing value of training according to the uncertainty of individual samples. Experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-06-07T11:37:45Z)
• Test-Time Self-Adaptive Small Language Models for Question Answering [63.91013329169796]
  We show and investigate the capabilities of smaller self-adaptive LMs, only with unlabeled test data. Our proposed self-adaption strategy demonstrates significant performance improvements on benchmark QA datasets.
  arXiv  Detail & Related papers  (2023-10-20T06:49:32Z)
• Benchmarking Causal Study to Interpret Large Language Models for Source Code [6.301373791541809]
  This paper introduces a benchmarking strategy named Galeras comprised of curated testbeds for three SE tasks. We illustrate the insights of our benchmarking strategy by conducting a case study on the performance of ChatGPT under distinct prompt engineering methods.
  arXiv  Detail & Related papers  (2023-08-23T20:32:12Z)
• LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond [135.8013388183257]
  We propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits. Most LLMs struggle on SummEdits, with performance close to random chance. The best-performing model, GPT-4, is still 8% below estimated human performance.
  arXiv  Detail & Related papers  (2023-05-23T21:50:06Z)
• Planning for Sample Efficient Imitation Learning [52.44953015011569]
  Current imitation algorithms struggle to achieve high performance and high in-environment sample efficiency simultaneously. We propose EfficientImitate, a planning-based imitation learning method that can achieve high in-environment sample efficiency and performance simultaneously. Experimental results show that EI achieves state-of-the-art results in performance and sample efficiency.
  arXiv  Detail & Related papers  (2022-10-18T05:19:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.