The Oracle Has Spoken: A Multi-Aspect Evaluation of Dialogue in Pythia

• URL: http://arxiv.org/abs/2509.16487v1
• Date: Sat, 20 Sep 2025 01:11:10 GMT
• Title: The Oracle Has Spoken: A Multi-Aspect Evaluation of Dialogue in Pythia
• Authors: Zixun Chen, Petr Babkin, Akshat Gupta, Gopala Anumanchipalli, Xiaomo Liu,
• Abstract summary: We employ a comprehensive suite of model-based metrics, each targeting a distinct fine-grained aspect of dialogue, motivated by linguistic theory.<n>We evaluate how the performance of pre-trained Pythia models changes with respect to each of those dimensions, depending on model size and as a result of supervised fine-tuning on conversational datasets.
• Score: 23.88625177239693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dialogue is one of the landmark abilities of large language models (LLMs). Despite its ubiquity, few studies actually distinguish specific ingredients underpinning dialogue behavior emerging during post-training. We employ a comprehensive suite of model-based metrics, each targeting a distinct fine-grained aspect of dialogue, motivated by linguistic theory. We evaluate how the performance of pre-trained Pythia models changes with respect to each of those dimensions, depending on model size and as a result of supervised fine-tuning on conversational datasets. We observe only a mild impact of raw model size on most metrics, whereas fine-tuning quickly saturates the scores for all but the smallest models tested. Somewhat contrary to our expectations, many metrics show very similar trends, especially if they are all rooted in the same evaluator model, which raises the question of their reliability in measuring a specific dimension. To that end, we conduct additional analyses of score distributions, metric correlations, and term frequencies in generated responses to help explain our observations.

Related papers

• Neural Models and Language Model Prompting for the Multidimensional Evaluation of Open-Ended Conversations [1.0006801729628605]
  We develop models to predict dialogue-level, dimension-specific scores.<n>Our work follows two main strategies: employing Language Models (LMs) as evaluators through prompting, and training encoder-based classification and regression models.<n>Although their performance decreases on the test set, it is important to note that the test set contains annotations with significantly different score ranges for some of the dimensions with respect to the train and validation sets.
  arXiv  Detail & Related papers  (2025-08-31T13:24:05Z)
• AHELM: A Holistic Evaluation of Audio-Language Models [78.20477815156484]
  multimodal audio-language models (ALMs) take interleaved audio and text as input and output text.<n>AHELM is a benchmark that aggregates various datasets -- including 2 new synthetic audio-text datasets called PARADE and CoRe-Bench.<n>We also standardize the prompts, inference parameters, and evaluation metrics to ensure equitable comparisons across models.
  arXiv  Detail & Related papers  (2025-08-29T07:40:39Z)
• Split and Rephrase with Large Language Models [2.499907423888049]
  Split and Rephrase (SPRP) task consists in splitting complex sentences into a sequence of shorter grammatical sentences. We evaluate large language models on the task, showing that they can provide large improvements over the state of the art on the main metrics.
  arXiv  Detail & Related papers  (2023-12-18T10:16:37Z)
• Anchor Points: Benchmarking Models with Much Fewer Examples [88.02417913161356]
  In six popular language classification benchmarks, model confidence in the correct class on many pairs of points is strongly correlated across models. We propose Anchor Point Selection, a technique to select small subsets of datasets that capture model behavior across the entire dataset. Just several anchor points can be used to estimate model per-class predictions on all other points in a dataset with low mean absolute error.
  arXiv  Detail & Related papers  (2023-09-14T17:45:51Z)
• Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective [69.50044040291847]
  We show how multi-dataset evaluations risk conflating different factors concerning what, precisely, is being measured. This makes it difficult to draw more generalizable conclusions from these evaluations.
  arXiv  Detail & Related papers  (2023-03-16T05:32:02Z)
• A Focused Study on Sequence Length for Dialogue Summarization [68.73335643440957]
  We analyze the length differences between existing models' outputs and the corresponding human references. We identify salient features for summary length prediction by comparing different model settings. Third, we experiment with a length-aware summarizer and show notable improvement on existing models if summary length can be well incorporated.
  arXiv  Detail & Related papers  (2022-09-24T02:49:48Z)
• A Study on the Evaluation of Generative Models [19.18642459565609]
  Implicit generative models, which do not return likelihood values, have become prevalent in recent years. In this work, we study the evaluation metrics of generative models by generating a high-quality synthetic dataset. Our study shows that while FID and IS do correlate to several f-divergences, their ranking of close models can vary considerably.
  arXiv  Detail & Related papers  (2022-06-22T09:27:31Z)
• CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning [5.389540975316299]
  Factual inconsistencies in generated summaries severely limit the practical applications of abstractive dialogue summarization. We provide a typology of factual errors with annotation data to highlight the types of errors and move away from a binary understanding of factuality. We propose a training strategy that improves the factual consistency and overall quality of summaries via a novel contrastive fine-tuning, called ConFiT.
  arXiv  Detail & Related papers  (2021-12-16T09:08:40Z)
• Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge [59.22170796793179]
  Transformers Language Models (TLMs) were tested on a benchmark for the textitdynamic estimation of thematic fit Our results show that TLMs can reach performances that are comparable to those achieved by SDM. However, additional analysis consistently suggests that TLMs do not capture important aspects of event knowledge.
  arXiv  Detail & Related papers  (2021-07-22T20:52:26Z)
• Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application [63.10266319378212]
  We propose a method for measuring complex variables on a continuous, interval spectrum by combining supervised deep learning with the Constructing Measures approach to faceted Rasch item response theory (IRT) We demonstrate this new method on a dataset of 50,000 social media comments sourced from YouTube, Twitter, and Reddit and labeled by 11,000 U.S.-based Amazon Mechanical Turk workers.
  arXiv  Detail & Related papers  (2020-09-22T02:15:05Z)
• An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue Generation [23.343006562849126]
  We present an empirical investigation of pre-trained Transformer-based auto-regressive language models for the task of open-domain dialogue generation. Training paradigm of pre-training and fine-tuning is employed to conduct learning. Experiments are conducted on the typical single-turn and multi-turn dialogue corpora such as Weibo, Douban, Reddit, DailyDialog, and Persona-Chat.
  arXiv  Detail & Related papers  (2020-03-09T15:20:21Z)

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.