A Judge-free LLM Open-ended Generation Benchmark Based on the Distributional Hypothesis
Evaluating the open-ended text generation of large language models (LLMs) is challenging because of the lack of a clear ground truth and the high cost of human or LLM-based assessments. We propose a novel benchmark that evaluates LLMs using n-gram statistics and rules, without relying on human judgement or LLM-as-a-judge approaches. Using 50 question and reference answer sets, we introduce three new metrics based on n-grams and rules: Fluency, Truthfulness, and Helpfulness. Our benchmark strongly correlates with GPT-4o-based evaluations while requiring significantly fewer computational resources, demonstrating its effectiveness as a scalable alternative for assessing LLMs' open-ended generation capabilities.
Code (1)
Tasks
Text GenerationSimilar Papers 제목 키워드 기반
Free-Text Evaluation of LLMs for 5G Domain Knowledge and Fault Analysis using LLM-as-Judge
Real-world fault analysis in 5G and emerging 6G networks demands domain expertise to analyze free-text diagnostics, including root-cause explanations and recommended actions. LLMs have emerged as a promising approach to …
Fault DiagnosisText GenerationSCOPE: Self-Play via Co-Evolving Policies for Open-Ended Tasks
Self-play can train language models without external supervision. However, existing methods require rule-checkable answers, leaving open-ended tasks dependent on curated prompts or frontier-model judges. We introduce SCO…
OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
Multimodal Large Language Models (MLLMs) have made significant strides in visual understanding and generation tasks. However, generating interleaved image-text content remains a challenge, which requires integrated multi…
Image Generationmultimodal generationText GenerationMany Voices, One Reward: Multi-Role Rubric Generation for LLM Judging and Reward Modeling
Reliable reward and preference signals are critical for evaluating and optimizing large language models on open-ended tasks. Rubric-based judges offer a transparent way to decompose such judgments into explicit evaluatio…
Reinforcement LearningCyclicJudge: Mitigating Judge Bias Efficiently in LLM-based Evaluation
LLM-as-judge evaluation has become standard practice for open-ended model assessment; however, judges exhibit systematic biases that cannot be averaged out by increasing the number of scenarios or generations. These bias…