Token Weighting for Long-Range Language Modeling

• URL: http://arxiv.org/abs/2503.09202v1
• Date: Wed, 12 Mar 2025 09:46:59 GMT
• Title: Token Weighting for Long-Range Language Modeling
• Authors: Falko Helm, Nico Daheim, Iryna Gurevych,
• Abstract summary: We propose novel token-weighting schemes that assign different weights to each training token in the loss.<n>We evaluate all methods on multiple long-context understanding tasks and show that non-uniform loss weights are helpful.<n>This work contributes to a better understanding of the trade-offs long-context language modeling faces.
• Score: 50.2371550397256
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many applications of large language models (LLMs) require long-context understanding, but models continue to struggle with such tasks. We hypothesize that conventional next-token prediction training could contribute to this, because each token is assigned equal weight. Yet, intuitively, the amount of context needed to predict the next token accurately varies greatly across different data. To reflect this, we propose various novel token-weighting schemes that assign different weights to each training token in the loss, thereby generalizing existing works. For this, we categorize token-weighting methods using a two-step framework which compares the confidences of a long-context and short-context model to score tokens. We evaluate all methods on multiple long-context understanding tasks and show that non-uniform loss weights are helpful to improve the long-context abilities of LLMs. Different short-context models can be used effectively for token scoring, including models that are much smaller than the long-context model that is trained. All in all, this work contributes to a better understanding of the trade-offs long-context language modeling faces and provides guidelines for model steering via loss-weighting based on empirical evidence. The code can be found on Github.

Related papers

• What is Wrong with Perplexity for Long-context Language Modeling? [71.34933096461124]
  Long-context inputs are crucial for large language models (LLMs) in tasks such as extended conversations, document summarization, and many-shot in-context learning.<n>Perplexity (PPL) has proven unreliable for assessing long-context capabilities.<n>We propose bfLongPPL, a novel metric that focuses on key tokens by employing a long-short context contrastive method to identify them.
  arXiv  Detail & Related papers  (2024-10-31T09:39:28Z)
• Faster Language Models with Better Multi-Token Prediction Using Tensor Decomposition [5.575078692353885]
  We propose a new model for multi-token prediction in transformers, aiming to enhance sampling efficiency without compromising accuracy. By generalizing it to a rank-$r$ canonical probability decomposition, we develop an improved model that predicts multiple tokens simultaneously.
  arXiv  Detail & Related papers  (2024-10-23T11:06:36Z)
• Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles [23.134664392314264]
  Tokenization is associated with many poorly understood shortcomings in language models (LMs) This work studies how tokenization impacts model performance by analyzing and comparing models with their byte-level counterparts. We introduce the Byte-Token Representation Lemma, a framework that establishes a mapping between the learned token distribution and its equivalent byte-level distribution.
  arXiv  Detail & Related papers  (2024-10-11T23:30:42Z)
• Dual Process Learning: Controlling Use of In-Context vs. In-Weights Strategies with Weight Forgetting [15.69952375347308]
  Language models have the ability to perform in-context learning (ICL)<n>Despite their apparent ability to learn in-context, language models are known to struggle when faced with unseen or rarely seen tokens.<n>We study structural in-context algorithms on both synthetic and naturalistic tasks using toy models, masked language models, and autoregressive language models.
  arXiv  Detail & Related papers  (2024-05-28T21:38:20Z)
• Better & Faster Large Language Models via Multi-token Prediction [29.067271500844928]
  Large language models such as GPT and Llama are trained with a next-token prediction loss. We suggest that training language models to predict multiple future tokens at once results in higher sample efficiency.
  arXiv  Detail & Related papers  (2024-04-30T17:33:57Z)
• Training-Free Long-Context Scaling of Large Language Models [114.53296002607993]
  We propose Dual Chunk Attention, which enables Llama2 70B to support context windows of more than 100k tokens without continual training. By decomposing the attention for long sequences into chunk-based modules, DCA manages to effectively capture the relative positional information of tokens.
  arXiv  Detail & Related papers  (2024-02-27T12:39:23Z)
• Code Representation Learning At Scale [75.04686476303436]
  We fuel code representation learning with a vast amount of code data via a two-stage pretraining scheme. We first train the encoders via a mix that leverages both randomness in masking language modeling and the structure aspect of programming language. We then enhance the representations via contrastive learning with hard negative and hard positive constructed in an unsupervised manner.
  arXiv  Detail & Related papers  (2024-02-02T22:19:15Z)
• The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute [66.84421705029624]
  We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours. We pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length. This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput.
  arXiv  Detail & Related papers  (2023-09-20T10:31:17Z)
• Training Trajectories of Language Models Across Scales [99.38721327771208]
  Scaling up language models has led to unprecedented performance gains. How do language models of different sizes learn during pre-training? Why do larger language models demonstrate more desirable behaviors?
  arXiv  Detail & Related papers  (2022-12-19T19:16:29Z)

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.