Evaluation of Large Language Models via Coupled Token Generation

• URL: http://arxiv.org/abs/2502.01754v2
• Date: Mon, 25 Aug 2025 16:05:04 GMT
• Title: Evaluation of Large Language Models via Coupled Token Generation
• Authors: Nina Corvelo Benz, Stratis Tsirtsis, Eleni Straitouri, Ivi Chatzi, Ander Artola Velasco, Suhas Thejaswi, Manuel Gomez-Rodriguez,
• Abstract summary: State of the art large language models rely on randomization to respond to a prompt.<n>We argue that the evaluation and ranking of large language models should control for the randomization underpinning their functioning.
• Score: 19.187846871216568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State of the art large language models rely on randomization to respond to a prompt. As an immediate consequence, a model may respond differently to the same prompt if asked multiple times. In this work, we argue that the evaluation and ranking of large language models should control for the randomization underpinning their functioning. Our starting point is the development of a causal model for coupled autoregressive generation, which allows different large language models to sample responses with the same source of randomness. Building upon our causal model, we first show that, on evaluations based on benchmark datasets, coupled autoregressive generation leads to the same conclusions as vanilla autoregressive generation but using provably fewer samples. However, we further show that, on evaluations based on (human) pairwise comparisons, coupled and vanilla autoregressive generation can surprisingly lead to different rankings when comparing more than two models, even with an infinite amount of samples. This suggests that the apparent advantage of a model over others in existing evaluation protocols may not be genuine but rather confounded by the randomness inherent to the generation process. To illustrate and complement our theoretical results, we conduct experiments with several large language models from the Llama, Mistral and Qwen families. We find that, across multiple benchmark datasets, coupled autoregressive generation requires up to 75% fewer samples to reach the same conclusions as vanilla autoregressive generation. Further, we find that the win-rates derived from pairwise comparisons by a strong large language model to prompts from the LMSYS Chatbot Arena platform differ under coupled and vanilla autoregressive generation.

Related papers

• Machine-generated text detection prevents language model collapse [17.34282527020344]
  We investigate the impact of decoding strategy on model collapse.<n>We train a machine-generated text detector and propose an importance sampling approach to alleviate model collapse.
  arXiv  Detail & Related papers  (2025-02-21T18:22:36Z)
• Energy-Based Diffusion Language Models for Text Generation [126.23425882687195]
  Energy-based Diffusion Language Model (EDLM) is an energy-based model operating at the full sequence level for each diffusion step.<n>Our framework offers a 1.3$times$ sampling speedup over existing diffusion models.
  arXiv  Detail & Related papers  (2024-10-28T17:25:56Z)
• CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models [16.436592723426305]
  It is unclear whether language models produce the same value for different ways of assigning joint probabilities to word spans. Our work introduces a novel framework, ConTestS, involving statistical tests to assess score consistency across interchangeable completion and conditioning orders.
  arXiv  Detail & Related papers  (2024-09-30T06:24:43Z)
• Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines [74.42485647685272]
  We focus on Generative Masked Language Models (GMLMs) We train a model to fit conditional probabilities of the data distribution via masking, which are subsequently used as inputs to a Markov Chain to draw samples from the model. We adapt the T5 model for iteratively-refined parallel decoding, achieving 2-3x speedup in machine translation with minimal sacrifice in quality.
  arXiv  Detail & Related papers  (2024-07-22T18:00:00Z)
• Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times [15.738530737312335]
  Recent studies have shown that as Transformer-based language models become larger and are trained on very large amounts of data, the fit of their surprisal estimates to naturalistic human reading times degrades. This paper presents a series of analyses showing that word frequency is a key explanatory factor underlying these two trends. The results indicate that Transformer-based language models' surprisal estimates diverge from human-like expectations due to the superhumanly complex associations they learn for predicting rare words.
  arXiv  Detail & Related papers  (2024-02-03T20:22:54Z)
• Generative Pre-training for Speech with Flow Matching [81.59952572752248]
  We pre-trained a generative model, named SpeechFlow, on 60k hours of untranscribed speech with Flow Matching and masked conditions. Experiment results show the pre-trained generative model can be fine-tuned with task-specific data to match or surpass existing expert models on speech enhancement, separation, and synthesis.
  arXiv  Detail & Related papers  (2023-10-25T03:40:50Z)
• A novel approach to measuring the scope of patent claims based on probabilities obtained from (large) language models [0.0]
  This work proposes to measure the scope of a patent claim as the reciprocal of self-information contained in this claim. The more surprising the information required to define the claim, the narrower its scope.
  arXiv  Detail & Related papers  (2023-09-17T16:50:07Z)
• mFACE: Multilingual Summarization with Factual Consistency Evaluation [79.60172087719356]
  Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries. We leverage factual consistency evaluation models to improve multilingual summarization.
  arXiv  Detail & Related papers  (2022-12-20T19:52:41Z)
• Robustness Analysis of Deep Learning Models for Population Synthesis [5.9106199000537645]
  We present bootstrap confidence interval for the deep generative models to evaluate robustness to multiple datasets. The models are implemented on multiple travel diaries of Montreal Origin- Destination Survey of 2008, 2013, and 2018. Results show that the predictive errors of CTGAN have narrower confidence intervals indicating its robustness to multiple datasets.
  arXiv  Detail & Related papers  (2022-11-23T22:55:55Z)
• Twist Decoding: Diverse Generators Guide Each Other [116.20780037268801]
  We introduce Twist decoding, a simple and general inference algorithm that generates text while benefiting from diverse models. Our method does not assume the vocabulary, tokenization or even generation order is shared.
  arXiv  Detail & Related papers  (2022-05-19T01:27:53Z)
• Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark [6.815730801645785]
  Many studies have compared machine learning (ML) and discrete choice models (DCMs) in predicting travel demand.<n>These studies often lack generalizability as they compare models deterministically without considering contextual variations.<n>This benchmark study compares two large-scale data sources.
  arXiv  Detail & Related papers  (2021-02-01T19:45:47Z)
• Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [54.94763543386523]
  Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors. We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method. Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
  arXiv  Detail & Related papers  (2020-10-25T18:51:15Z)
• Multi-Referenced Training for Dialogue Response Generation [36.24321477524634]
  We show that gap between the real world probability distribution and the single-referenced data's probability distribution prevents the model from learning the one-to-many relations efficiently. We generate diverse pseudo references from a powerful pretrained model to build multi-referenced data that provides a better approximation of the real-world distribution.
  arXiv  Detail & Related papers  (2020-09-15T14:17:53Z)
• On the Discrepancy between Density Estimation and Sequence Generation [92.70116082182076]
  log-likelihood is highly correlated with BLEU when we consider models within the same family. We observe no correlation between rankings of models across different families.
  arXiv  Detail & Related papers  (2020-02-17T20:13:35Z)
• Learning to Compare for Better Training and Evaluation of Open Domain Natural Language Generation Models [23.62054164511058]
  We propose to evaluate natural language generation models by learning to compare a pair of generated sentences by fine-tuning BERT. While able to be trained in a fully self-supervised fashion, our model can be further fine-tuned with a little amount of human preference annotation.
  arXiv  Detail & Related papers  (2020-02-12T15:52:21Z)
• AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses [97.50616524350123]
  We build dialogue models that are dynamically aware of what utterances or tokens are dull without any feature-engineering. The first model, MinAvgOut, directly maximizes the diversity score through the output distributions of each batch. The second model, Label Fine-Tuning (LFT), prepends to the source sequence a label continuously scaled by the diversity score to control the diversity level. The third model, RL, adopts Reinforcement Learning and treats the diversity score as a reward signal.
  arXiv  Detail & Related papers  (2020-01-15T18:32:06Z)

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.