Dynamic Evaluation of Large Language Models by Meta Probing Agents

• URL: http://arxiv.org/abs/2402.14865v2
• Date: Fri, 7 Jun 2024 09:19:45 GMT
• Title: Dynamic Evaluation of Large Language Models by Meta Probing Agents
• Authors: Kaijie Zhu, Jindong Wang, Qinlin Zhao, Ruochen Xu, Xing Xie,
• Abstract summary: We propose meta probing agents (MPA) to evaluate large language models (LLMs) MPA is the key component of DyVal 2, which naturally extends the previous DyValcitepzhu2023dyval. MPA designs the probing and judging agents to automatically transform an original evaluation problem into a new one following psychometric theory.
• Score: 44.20074234421295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluation of large language models (LLMs) has raised great concerns in the community due to the issue of data contamination. Existing work designed evaluation protocols using well-defined algorithms for specific tasks, which cannot be easily extended to diverse scenarios. Moreover, current evaluation benchmarks can only provide the overall benchmark results and cannot support a fine-grained and multifaceted analysis of LLMs' abilities. In this paper, we propose meta probing agents (MPA), a general dynamic evaluation protocol inspired by psychometrics to evaluate LLMs. MPA is the key component of DyVal 2, which naturally extends the previous DyVal~\citep{zhu2023dyval}. MPA designs the probing and judging agents to automatically transform an original evaluation problem into a new one following psychometric theory on three basic cognitive abilities: language understanding, problem solving, and domain knowledge. These basic abilities are also dynamically configurable, allowing multifaceted analysis. We conducted extensive evaluations using MPA and found that most LLMs achieve poorer performance, indicating room for improvement. Our multifaceted analysis demonstrated the strong correlation between the basic abilities and an implicit Matthew effect on model size, i.e., larger models possess stronger correlations of the abilities. MPA can also be used as a data augmentation approach to enhance LLMs. Code is available at: https://github.com/microsoft/promptbench.

Related papers

• MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs [97.94579295913606]
  Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. In the development process, evaluation is critical since it provides intuitive feedback and guidance on improving models. This work aims to offer researchers an easy grasp of how to effectively evaluate MLLMs according to different needs and to inspire better evaluation methods.
  arXiv  Detail & Related papers  (2024-11-22T18:59:54Z)
• Revisiting Benchmark and Assessment: An Agent-based Exploratory Dynamic Evaluation Framework for LLMs [29.72874725703848]
  We introduce two concepts: Benchmark+, which extends traditional question-answer benchmark into a more flexible "strategy-criterion" format; and Assessment+, which enhances the interaction process. We propose an agent-based evaluation framework called TestAgent, which implements these concepts through retrieval augmented generation and reinforcement learning.
  arXiv  Detail & Related papers  (2024-10-15T11:20:42Z)
• DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph [70.79413606968814]
  We introduce Dynamic Evaluation of LLMs via Adaptive Reasoning Graph Evolvement (DARG) to dynamically extend current benchmarks with controlled complexity and diversity. Specifically, we first extract the reasoning graphs of data points in current benchmarks and then perturb the reasoning graphs to generate novel testing data. Such newly generated test samples can have different levels of complexity while maintaining linguistic diversity similar to the original benchmarks.
  arXiv  Detail & Related papers  (2024-06-25T04:27:53Z)
• CogBench: a large language model walks into a psychology lab [12.981407327149679]
  This paper introduces CogBench, a benchmark that includes ten behavioral metrics derived from seven cognitive psychology experiments. We apply CogBench to 35 large language models (LLMs) and analyze this data using statistical multilevel modeling techniques. We find that open-source models are less risk-prone than proprietary models and that fine-tuning on code does not necessarily enhance LLMs' behavior.
  arXiv  Detail & Related papers  (2024-02-28T10:43:54Z)
• AQA-Bench: An Interactive Benchmark for Evaluating LLMs' Sequential Reasoning Ability [29.1826948551409]
  AQA-Bench is a novel benchmark to assess the sequential reasoning capabilities of large language models. We build AQA-Bench with three different algorithms, namely binary search, depth-first search, and breadth-first search. Our investigations reveal several interesting findings.
  arXiv  Detail & Related papers  (2024-02-14T18:59:33Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)
• MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration [102.41118020705876]
  Large Language Models (LLMs) have marked a significant advancement in the field of natural language processing. As their applications extend into multi-agent environments, a need has arisen for a comprehensive evaluation framework. This work introduces a novel benchmarking framework specifically tailored to assess LLMs within multi-agent settings.
  arXiv  Detail & Related papers  (2023-11-14T21:46:27Z)
• EpiK-Eval: Evaluation for Language Models as Epistemic Models [16.485951373967502]
  We introduce EpiK-Eval, a novel question-answering benchmark tailored to evaluate LLMs' proficiency in formulating a coherent and consistent knowledge representation from segmented narratives. We argue that these shortcomings stem from the intrinsic nature of prevailing training objectives. The findings from this study offer insights for developing more robust and reliable LLMs.
  arXiv  Detail & Related papers  (2023-10-23T21:15:54Z)
• DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks [112.66827096358857]
  We introduce DyVal, a protocol for dynamic evaluation of large language models (LLMs) Based on our framework, we build graph-informed DyVal by leveraging the structural advantage of directed acyclic graphs. We evaluate various LLMs ranging from Flan-T5-large to GPT-3.5-Turbo and GPT-4.
  arXiv  Detail & Related papers  (2023-09-29T12:04:14Z)

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.