Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat

• URL: http://arxiv.org/abs/2411.14483v2
• Date: Mon, 17 Feb 2025 16:21:10 GMT
• Title: Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat
• Authors: Roland Daynauth, Christopher Clarke, Krisztian Flautner, Lingjia Tang, Jason Mars,
• Abstract summary: Pairwise ranking has emerged as a new method for evaluating human preferences for large language models (LLM) We explore the effectiveness of ranking systems for head-to-head comparisons of LLMs. Our analysis uncovers key insights into the factors that affect ranking accuracy and efficiency.
• Score: 7.8905223445925055
• License:
• Abstract: Deciding which large language model (LLM) to use is a complex challenge. Pairwise ranking has emerged as a new method for evaluating human preferences for LLMs. This approach entails humans evaluating pairs of model outputs based on a predefined criterion. By collecting these comparisons, a ranking can be constructed using methods such as Elo. However, applying these algorithms as constructed in the context of LLM evaluation introduces several challenges. In this paper, we explore the effectiveness of ranking systems for head-to-head comparisons of LLMs. We formally define a set of fundamental principles for effective ranking and conduct a series of extensive evaluations on the robustness of several ranking algorithms in the context of LLMs. Our analysis uncovers key insights into the factors that affect ranking accuracy and efficiency, offering guidelines for selecting the most appropriate methods based on specific evaluation contexts and resource constraints.

Related papers

• Re-evaluating Automatic LLM System Ranking for Alignment with Human Preference [63.03859517284341]
  An automatic evaluation framework aims to rank LLMs based on their alignment with human preferences. An automatic LLM bencher consists of four components: the input set, the evaluation model, the evaluation type and the aggregation method.
  arXiv  Detail & Related papers  (2024-12-31T17:46:51Z)
• Self-Calibrated Listwise Reranking with Large Language Models [137.6557607279876]
  Large language models (LLMs) have been employed in reranking tasks through a sequence-to-sequence approach. This reranking paradigm requires a sliding window strategy to iteratively handle larger candidate sets. We propose a novel self-calibrated listwise reranking method, which aims to leverage LLMs to produce global relevance scores for ranking.
  arXiv  Detail & Related papers  (2024-11-07T10:31:31Z)
• DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation [75.81096662788254]
  Large Language Models (LLMs) are scalable and economical evaluators. The question of how reliable these evaluators are has emerged as a crucial research question. We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
  arXiv  Detail & Related papers  (2024-05-24T08:12:30Z)
• Make Large Language Model a Better Ranker [20.532118635672763]
  This paper introduces the large language model framework with Aligned Listwise Ranking Objectives (ALRO) ALRO is designed to bridge the gap between the capabilities of LLMs and nuanced requirements of ranking tasks. Our evaluative studies reveal that ALRO outperforms both existing embedding-based recommendation methods and LLM-based recommendation baselines.
  arXiv  Detail & Related papers  (2024-03-28T07:22:16Z)
• Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators [48.54465599914978]
  Large Language Models (LLMs) have demonstrated promising capabilities as automatic evaluators in assessing the quality of generated natural language. LLMs still exhibit biases in evaluation and often struggle to generate coherent evaluations that align with human assessments. We introduce Pairwise-preference Search (PAIRS), an uncertainty-guided search-based rank aggregation method that employs LLMs to conduct pairwise comparisons locally and efficiently ranks candidate texts globally.
  arXiv  Detail & Related papers  (2024-03-25T17:11:28Z)
• PiCO: Peer Review in LLMs based on the Consistency Optimization [19.130941716491716]
  We use peer-review mechanisms to measure large language models (LLMs) automatically. We formalize it as a constrained optimization problem, intending to maximize the consistency of each LLM's capabilities and scores. We propose three metrics called PEN, CIN, and LIS to evaluate the gap in aligning human rankings.
  arXiv  Detail & Related papers  (2024-02-02T18:49:26Z)
• F-Eval: Assessing Fundamental Abilities with Refined Evaluation Methods [102.98899881389211]
  We propose F-Eval, a bilingual evaluation benchmark to evaluate the fundamental abilities, including expression, commonsense and logic. For reference-free subjective tasks, we devise new evaluation methods, serving as alternatives to scoring by API models.
  arXiv  Detail & Related papers  (2024-01-26T13:55:32Z)
• Elo Uncovered: Robustness and Best Practices in Language Model Evaluation [9.452326973655447]
  We study two axioms that evaluation methods should adhere to: reliability and transitivity. We show that these axioms are not always satisfied raising questions about the reliability of current comparative evaluations of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T00:45:23Z)
• Evaluating Large Language Models at Evaluating Instruction Following [54.49567482594617]
  We introduce a challenging meta-evaluation benchmark, LLMBar, designed to test the ability of an LLM evaluator in discerning instruction-following outputs. We discover that different evaluators exhibit distinct performance on LLMBar and even the highest-scoring ones have substantial room for improvement.
  arXiv  Detail & Related papers  (2023-10-11T16:38:11Z)

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.