Paloma: A Benchmark for Evaluating Language Model Fit

• URL: http://arxiv.org/abs/2312.10523v1
• Date: Sat, 16 Dec 2023 19:12:45 GMT
• Title: Paloma: A Benchmark for Evaluating Language Model Fit
• Authors: Ian Magnusson, Akshita Bhagia, Valentin Hofmann, Luca Soldaini, Ananya Harsh Jha, Oyvind Tafjord, Dustin Schwenk, Evan Pete Walsh, Yanai Elazar, Kyle Lo, Dirk Groeneveld, Iz Beltagy, Hannaneh Hajishirzi, Noah A. Smith, Kyle Richardson, Jesse Dodge
• Abstract summary: Language Model Assessment (Paloma) measures fit to 585 text domains. We populate our benchmark with results from baselines pretrained on popular corpora.
• Score: 114.63031978259467
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Language models (LMs) commonly report perplexity on monolithic data held out from training. Implicitly or explicitly, this data is composed of domains$\unicode{x2013}$varying distributions of language. Rather than assuming perplexity on one distribution extrapolates to others, Perplexity Analysis for Language Model Assessment (Paloma), measures LM fit to 585 text domains, ranging from nytimes.com to r/depression on Reddit. We invite submissions to our benchmark and organize results by comparability based on compliance with guidelines such as removal of benchmark contamination from pretraining. Submissions can also record parameter and training token count to make comparisons of Pareto efficiency for performance as a function of these measures of cost. We populate our benchmark with results from 6 baselines pretrained on popular corpora. In case studies, we demonstrate analyses that are possible with Paloma, such as finding that pretraining without data beyond Common Crawl leads to inconsistent fit to many domains.

Related papers

• TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking [6.070192392563392]
  We present TituLLMs, the first large pretrained Bangla LLMs in 1B and 3B parameter sizes. To train TituLLMs, we collected a pretraining dataset of approximately 37 billion tokens. We extended the Llama-3.2 tokenizer to incorporate language- and culture-specific knowledge.
  arXiv  Detail & Related papers  (2025-02-16T16:22:23Z)
• Revisiting a Pain in the Neck: Semantic Phrase Processing Benchmark for Language Models [10.482557806309174]
  We introduce LexBench, a comprehensive evaluation suite enabled to test language models (LMs) on semantic phrase processing tasks. Thanks to ourbenchmark, we assess the performance of 15 LMs across model architectures and parameter scales in classification, extraction, and interpretation tasks. Our benchmarking findings can serve future research aiming to improve the generic capability of LMs on semantic phrase comprehension.
  arXiv  Detail & Related papers  (2024-05-05T09:20:38Z)
• Acquiring Linguistic Knowledge from Multimodal Input [10.965306219502303]
  In contrast to children, language models (LMs) exhibit considerably inferior data efficiency when acquiring language. We test the hypothesis that this data efficiency gap is partly caused by a lack of multimodal input and grounding in the learning environment of typical language models.
  arXiv  Detail & Related papers  (2024-02-27T23:29:10Z)
• Are Pre-trained Language Models Useful for Model Ensemble in Chinese Grammatical Error Correction? [10.302225525539003]
  We explore several ensemble strategies based on strong PLMs with four sophisticated single models. The performance does not improve but even gets worse after the PLM-based ensemble.
  arXiv  Detail & Related papers  (2023-05-24T14:18:52Z)
• Ensemble Transfer Learning for Multilingual Coreference Resolution [60.409789753164944]
  A problem that frequently occurs when working with a non-English language is the scarcity of annotated training data. We design a simple but effective ensemble-based framework that combines various transfer learning techniques. We also propose a low-cost TL method that bootstraps coreference resolution models by utilizing Wikipedia anchor texts.
  arXiv  Detail & Related papers  (2023-01-22T18:22:55Z)
• 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)
• Bridging the Data Gap between Training and Inference for Unsupervised Neural Machine Translation [49.916963624249355]
  A UNMT model is trained on the pseudo parallel data with translated source, and natural source sentences in inference. The source discrepancy between training and inference hinders the translation performance of UNMT models. We propose an online self-training approach, which simultaneously uses the pseudo parallel data natural source, translated target to mimic the inference scenario.
  arXiv  Detail & Related papers  (2022-03-16T04:50:27Z)
• Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages [81.90356787324481]
  Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict. This work shows a comparison of a neural model and character language models with varying amounts on target language data. Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected.
  arXiv  Detail & Related papers  (2020-10-20T17:31:07Z)
• Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information [72.2412707779571]
  mRASP is an approach to pre-train a universal multilingual neural machine translation model. We carry out experiments on 42 translation directions across a diverse setting, including low, medium, rich resource, and as well as transferring to exotic language pairs.
  arXiv  Detail & Related papers  (2020-10-07T03:57:54Z)

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.