Benchmarking Large Language Model Capabilities for Conditional Generation

• URL: http://arxiv.org/abs/2306.16793v1
• Date: Thu, 29 Jun 2023 08:59:40 GMT
• Title: Benchmarking Large Language Model Capabilities for Conditional Generation
• Authors: Joshua Maynez, Priyanka Agrawal, Sebastian Gehrmann
• Abstract summary: We discuss how to adapt existing application-specific generation benchmarks to PLMs. We show that PLMs differ in their applicability to different data regimes and their generalization to multiple languages.
• Score: 15.437176676169997
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pre-trained large language models (PLMs) underlie most new developments in natural language processing. They have shifted the field from application-specific model pipelines to a single model that is adapted to a wide range of tasks. Autoregressive PLMs like GPT-3 or PaLM, alongside techniques like few-shot learning, have additionally shifted the output modality to generation instead of classification or regression. Despite their ubiquitous use, the generation quality of language models is rarely evaluated when these models are introduced. Additionally, it is unclear how existing generation tasks--while they can be used to compare systems at a high level--relate to the real world use cases for which people have been adopting them. In this work, we discuss how to adapt existing application-specific generation benchmarks to PLMs and provide an in-depth, empirical study of the limitations and capabilities of PLMs in natural language generation tasks along dimensions such as scale, architecture, input and output language. Our results show that PLMs differ in their applicability to different data regimes and their generalization to multiple languages and inform which PLMs to use for a given generation task setup. We share best practices to be taken into consideration when benchmarking generation capabilities during the development of upcoming PLMs.

Related papers

• mHumanEval -- A Multilingual Benchmark to Evaluate Large Language Models for Code Generation [28.531581489405745]
  mHumanEval is an extended benchmark supporting prompts in over 200 natural languages. We provide expert human translations for 15 diverse natural languages (NLs) We conclude by analyzing the multilingual code generation capabilities of state-of-the-art (SOTA) Code LLMs.
  arXiv  Detail & Related papers  (2024-10-19T08:44:26Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models [102.00201523306986]
  We present L2CEval, a systematic evaluation of the language-to-code generation capabilities of large language models (LLMs) We analyze the factors that potentially affect their performance, such as model size, pretraining data, instruction tuning, and different prompting methods. In addition to assessing model performance, we measure confidence calibration for the models and conduct human evaluations of the output programs.
  arXiv  Detail & Related papers  (2023-09-29T17:57:00Z)
• A Survey of Large Language Models [81.06947636926638]
  Language modeling has been widely studied for language understanding and generation in the past two decades. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
  arXiv  Detail & Related papers  (2023-03-31T17:28:46Z)
• MEGA: Multilingual Evaluation of Generative AI [23.109803506475174]
  Generative AI models have shown impressive performance on many Natural Language Processing tasks. Most studies on generative LLMs have been restricted to English. It is unclear how capable these models are at understanding and generating text in other languages.
  arXiv  Detail & Related papers  (2023-03-22T13:03:10Z)
• An Overview on Language Models: Recent Developments and Outlook [32.528770408502396]
  Conventional language models (CLMs) aim to predict the probability of linguistic sequences in a causal manner. Pre-trained language models (PLMs) cover broader concepts and can be used in both causal sequential modeling and fine-tuning for downstream applications.
  arXiv  Detail & Related papers  (2023-03-10T07:55:00Z)
• Augmented Language Models: a Survey [55.965967655575454]
  This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools. We refer to them as Augmented Language Models (ALMs) The missing token objective allows ALMs to learn to reason, use tools, and even act, while still performing standard natural language tasks.
  arXiv  Detail & Related papers  (2023-02-15T18:25:52Z)
• Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study [76.52997424694767]
  We present an in-depth empirical study of keyphrase extraction and keyphrase generation using pre-trained language models. We show that PLMs have competitive high-resource performance and state-of-the-art low-resource performance. Further results show that in-domain BERT-like PLMs can be used to build strong and data-efficient keyphrase generation models.
  arXiv  Detail & Related papers  (2022-12-20T13:20:21Z)
• PaLM: Scaling Language Modeling with Pathways [180.69584031908113]
  We trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks.
  arXiv  Detail & Related papers  (2022-04-05T16:11:45Z)
• On the Universality of Deep COntextual Language Models [15.218264849664715]
  Deep Contextual Language Models (LMs) like ELMO, BERT, and their successors dominate the landscape of Natural Language Processing. Multilingual versions of such models like XLM-R and mBERT have given promising results in zero-shot cross-lingual transfer. Due to this initial success, pre-trained models are being used as Universal Language Models'
  arXiv  Detail & Related papers  (2021-09-15T08:00:33Z)

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.