Beyond Metrics: A Critical Analysis of the Variability in Large Language Model Evaluation Frameworks

• URL: http://arxiv.org/abs/2407.21072v1
• Date: Mon, 29 Jul 2024 03:37:14 GMT
• Title: Beyond Metrics: A Critical Analysis of the Variability in Large Language Model Evaluation Frameworks
• Authors: Marco AF Pimentel, Clément Christophe, Tathagata Raha, Prateek Munjal, Praveen K Kanithi, Shadab Khan,
• Abstract summary: Large language models (LLMs) continue to evolve, the need for robust and standardized evaluation benchmarks becomes paramount. Various frameworks have emerged as noteworthy contributions to the field, offering comprehensive evaluation tests and benchmarks. This paper provides an exploration and critical analysis of some of these evaluation methodologies, shedding light on their strengths, limitations, and impact on advancing the state-of-the-art in natural language processing.
• Score: 3.773596042872403
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As large language models (LLMs) continue to evolve, the need for robust and standardized evaluation benchmarks becomes paramount. Evaluating the performance of these models is a complex challenge that requires careful consideration of various linguistic tasks, model architectures, and benchmarking methodologies. In recent years, various frameworks have emerged as noteworthy contributions to the field, offering comprehensive evaluation tests and benchmarks for assessing the capabilities of LLMs across diverse domains. This paper provides an exploration and critical analysis of some of these evaluation methodologies, shedding light on their strengths, limitations, and impact on advancing the state-of-the-art in natural language processing.

Related papers

• A Preliminary Study of Multilingual Code Language Models for Code Generation Task Using Translated Benchmarks [0.0]
  We evaluate the performance of Poly-Coder, a pioneering open-source, multilingual CLM built for code generation. Our results suggest that the outcomes observed in these translated benchmarks align well with evaluation metrics used during the training phase. These initial insights highlight the need for more comprehensive empirical studies.
  arXiv  Detail & Related papers  (2024-11-23T06:40:47Z)
• A Survey on Multimodal Benchmarks: In the Era of Large AI Models [13.299775710527962]
  Multimodal Large Language Models (MLLMs) have brought substantial advancements in artificial intelligence. This survey systematically reviews 211 benchmarks that assess MLLMs across four core domains: understanding, reasoning, generation, and application.
  arXiv  Detail & Related papers  (2024-09-21T15:22:26Z)
• LalaEval: A Holistic Human Evaluation Framework for Domain-Specific Large Language Models [6.002286552369069]
  LalaEval aims to fill a crucial research gap by providing a systematic methodology for conducting standardized human evaluations within specific domains. The paper demonstrates the framework's application within the logistics industry.
  arXiv  Detail & Related papers  (2024-08-23T19:12:45Z)
• A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations [35.12731651234186]
  Large Language Models (LLMs) have recently gained significant attention due to their remarkable capabilities. We systematically review the primary challenges and limitations causing these inconsistencies and unreliable evaluations. Based on our critical review, we present our perspectives and recommendations to ensure LLM evaluations are reproducible, reliable, and robust.
  arXiv  Detail & Related papers  (2024-07-04T17:15:37Z)
• Exploring Precision and Recall to assess the quality and diversity of LLMs [82.21278402856079]
  We introduce a novel evaluation framework for Large Language Models (LLMs) such as textscLlama-2 and textscMistral. This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora.
  arXiv  Detail & Related papers  (2024-02-16T13:53:26Z)
• 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)
• Leveraging Large Language Models for NLG Evaluation: Advances and Challenges [57.88520765782177]
  Large Language Models (LLMs) have opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. We propose a coherent taxonomy for organizing existing LLM-based evaluation metrics, offering a structured framework to understand and compare these methods. By discussing unresolved challenges, including bias, robustness, domain-specificity, and unified evaluation, this paper seeks to offer insights to researchers and advocate for fairer and more advanced NLG evaluation techniques.
  arXiv  Detail & Related papers  (2024-01-13T15:59:09Z)
• L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models [102.00201523306986]
  We present L2CEval, a systematic evaluation of the language-to-code generation capabilities of large language models (LLMs) We analyze the factors that potentially affect their performance, such as model size, pretraining data, instruction tuning, and different prompting methods. In addition to assessing model performance, we measure confidence calibration for the models and conduct human evaluations of the output programs.
  arXiv  Detail & Related papers  (2023-09-29T17:57:00Z)
• Multi-Dimensional Evaluation of Text Summarization with In-Context Learning [79.02280189976562]
  In this paper, we study the efficacy of large language models as multi-dimensional evaluators using in-context learning. Our experiments show that in-context learning-based evaluators are competitive with learned evaluation frameworks for the task of text summarization. We then analyze the effects of factors such as the selection and number of in-context examples on performance.
  arXiv  Detail & Related papers  (2023-06-01T23:27:49Z)
• Evaluating the Performance of Large Language Models on GAOKAO Benchmark [53.663757126289795]
  This paper introduces GAOKAO-Bench, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples. With human evaluation, we obtain the converted total score of LLMs, including GPT-4, ChatGPT and ERNIE-Bot. We also use LLMs to grade the subjective questions, and find that model scores achieve a moderate level of consistency with human scores.
  arXiv  Detail & Related papers  (2023-05-21T14:39:28Z)

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.