Meeseeks: An Iterative Benchmark Evaluating LLMs Multi-Turn Instruction-Following Ability

• URL: http://arxiv.org/abs/2504.21625v1
• Date: Wed, 30 Apr 2025 13:28:19 GMT
• Title: Meeseeks: An Iterative Benchmark Evaluating LLMs Multi-Turn Instruction-Following Ability
• Authors: Jiaming Wang,
• Abstract summary: Meeseeks simulates realistic human-LLM interactions through an iterative feedback process.<n>This design enables models to self-correct based on specific requirement failures.
• Score: 3.4354830835082195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability to follow instructions accurately is fundamental for Large Language Models (LLMs) to serve as reliable agents in real-world applications. While existing instruction-following benchmarks are either single-turn or introduce new requirements in each turn without allowing self-correction, Meeseeks simulates realistic human-LLM interactions through an iterative feedback process. This design enables models to self-correct based on specific requirement failures, better reflecting real-world user-end usage patterns. The benchmark implements a comprehensive evaluation system with 38 capability tags organized across three dimensions: Intent Recognition, Granular Content Validation, and Output Structure Validation. Through rigorous evaluation across LLMs, Meeseeks provides valuable insights into LLMs' instruction-following capabilities in practical applications.

Related papers

• TuRTLe: A Unified Evaluation of LLMs for RTL Generation [0.6010802600885173]
  We propose TuRTLe, a unified evaluation framework designed to assess LLMs across key RTL generation tasks. We benchmark a diverse set of open LLMs and analyze their strengths and weaknesses in EDA-specific tasks. Our results show that reasoning-based models, such as DeepSeek R1, consistently outperform others across multiple evaluation criteria.
  arXiv  Detail & Related papers  (2025-03-31T07:43:12Z)
• PanguIR Technical Report for NTCIR-18 AEOLLM Task [12.061652026366591]
  Large language models (LLMs) are increasingly critical and challenging to evaluate.<n>Manual evaluation, while comprehensive, is often costly and resource-intensive.<n>automatic evaluation offers greater scalability but is constrained by the limitations of its evaluation criteria.
  arXiv  Detail & Related papers  (2025-03-04T07:40:02Z)
• Integrating Expert Knowledge into Logical Programs via LLMs [3.637365301757111]
  ExKLoP is a framework designed to evaluate how effectively Large Language Models integrate expert knowledge into logical reasoning systems.<n>This capability is especially valuable in engineering, where expert knowledge-such as manufacturer-recommended operational ranges-can be directly embedded into automated monitoring systems.
  arXiv  Detail & Related papers  (2025-02-17T19:18:23Z)
• SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models [88.29990536278167]
  We introduce SPaR, a self-play framework integrating tree-search self-refinement to yield valid and comparable preference pairs.<n>Our experiments show that a LLaMA3-8B model, trained over three iterations guided by SPaR, surpasses GPT-4-Turbo on the IFEval benchmark without losing general capabilities.
  arXiv  Detail & Related papers  (2024-12-16T09:47:43Z)
• AutoBench-V: Can Large Vision-Language Models Benchmark Themselves? [65.92331309449015]
  We introduce AutoBench-V, an automated framework for serving evaluation on demand, i.e., benchmarking LVLMs based on specific aspects of model capability.<n>Through an extensive evaluation of nine popular LVLMs across five demanded user inputs, the framework shows effectiveness and reliability.
  arXiv  Detail & Related papers  (2024-10-28T17:55:08Z)
• Evaluation of Instruction-Following Ability for Large Language Models on Story-Ending Generation [2.4889060833127665]
  In this paper, we focus on evaluating the instruction-following ability of Large Language Models (LLMs) in the context of story-ending generation. We propose an automatic evaluation pipeline that utilizes a machine reading comprehension (MRC) model to determine whether the generated story-ending reflects instruction.
  arXiv  Detail & Related papers  (2024-06-24T06:53:36Z)
• Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants [28.691691883519542]
  We introduce a technique that decomposes complex instructions into simpler sub-components, modifies these, and reconstructs them into new variants. Based on DeMoRecon, we developed the FGIV dataset which contains fine-grained instruction variants of 1,773 seed instructions. Our findings show that LLMs fine-tuned with FGIV will gain significant performance boost on both ours and commonly used instructions-following benchmarks.
  arXiv  Detail & Related papers  (2024-06-17T08:08:11Z)
• RepEval: Effective Text Evaluation with LLM Representation [55.26340302485898]
  RepEval is a metric that leverages the projection of Large Language Models (LLMs) representations for evaluation. Our work underscores the richness of information regarding text quality embedded within LLM representations, offering insights for the development of new metrics.
  arXiv  Detail & Related papers  (2024-04-30T13:50:55Z)
• CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
  We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T08:29:54Z)
• Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization [132.25202059478065]
  We benchmark large language models (LLMs) on instruction controllable text summarization. Our study reveals that instruction controllable text summarization remains a challenging task for LLMs.
  arXiv  Detail & Related papers  (2023-11-15T18:25:26Z)
• Can Large Language Models Understand Real-World Complex Instructions? [54.86632921036983]
  Large language models (LLMs) can understand human instructions, but struggle with complex instructions. Existing benchmarks are insufficient to assess LLMs' ability to understand complex instructions. We propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically.
  arXiv  Detail & Related papers  (2023-09-17T04:18:39Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)

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.