| Challenge: | Existing methods to improve language modeling performance are based on regularized LSTMs with a large number of parameters and training time. |
| Approach: | They propose a method that decodes the last token in context using the predicted distribution of the next token. |
| Outcome: | The proposed method improves perplexity on the Penn Treebank dataset by 1.8 points and 2.3 points on the WikiText-2 datasets. |
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An Embarrassingly Simple Method to Mitigate Undesirable Properties of Pretrained Language Model Tokenizers (2022.acl-short)
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| Challenge: | a standard tokenizer does not cover all characters of a word but preserves key aspects of its morphological structure . a novel method to improve tokenization of pretrained language models is proposed . |
| Approach: | They propose a method to improve the tokenization of pretrained language models . they use the vocabulary of a standard tokenizer but preserves morphological structure . |
| Outcome: | The proposed method improves tokenization of pretrained language models on morphological gold segmentations and text classification tasks. |
Sharp Nearby, Fuzzy Far Away: How Neural Language Models Use Context (P18-1)
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| Challenge: | Recent studies have shed light on the information encoded by long-term memory networks. |
| Approach: | They propose to use a neural caching model to model the role of context in an LSTM LM . they analyze the increase in perplexity when prior context words are shuffled, replaced, or dropped . |
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Efficient Training of Language Models with Compact and Consistent Next Token Distributions (2024.findings-acl)
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| Challenge: | Existing methods to train language models have focused on maximizing the likelihood of the next token . however, the construction and querying of such n-grams can be costly and impede training speed. |
| Approach: | They propose a method to train language models faster by pre-aggregating corpus with collapsed n-gram distribution. |
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Learn Your Tokens: Word-Pooled Tokenization for Language Modeling (2023.findings-emnlp)
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| Challenge: | Language models typically tokenize text into subwords, using a deterministic, hand-engineered heuristic of combining characters into longer surface-level strings such as ‘ing’ or whole words. |
| Approach: | They propose a 'learn your tokens' scheme which pooles bytes/characters into word representations and decodes individual characters/bytes per word in parallel. |
| Outcome: | The proposed tokenizer outperforms subword models and byte/character models over the word boundary and outperformed on rare words by a factor of 30! |
DecoCal: Decoding with Calibration in Diffusion Large Language Models (2026.acl-long)
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| Challenge: | Diffusion Large Language Models (DLLMs) generate text via iterative token denoising . but decoding is challenging, with many tokens appearing predictable early . |
| Approach: | They propose a Decoding framework that performs Calibration of token-level confidence across diffusion steps and leverages the calibrated results to guide decoding decisions. |
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Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions (2023.acl-long)
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| Challenge: | Recent work on why Transformer-based large language models make predictions has made their behavior opaque due to the complexity of the computations performed within each layer. |
| Approach: | They propose a linear decomposition of final hidden states from autoregressive language models based on each initial input token, which is exact for virtually all contemporary Transformer architectures. |
| Outcome: | The proposed method analyzes the influence of input tokens on model probabilities over a sequence of upcoming words with only one forward pass from the model. |
Mitigating the Learning Bias towards Repetition by Self-Contrastive Training for Open-Ended Generation (2023.findings-acl)
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| Challenge: | Existing language models generate repetitive texts with greedy decoding or beam search. |
| Approach: | They propose a self-contrastive training technique to penalize the output of a premature checkpoint of the same model when it incorrectly predicts repetition. |
| Outcome: | The proposed training mitigates repetition while maintaining fluency while minimizing the overestimation of token-level repetition probabilities. |
Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change (2022.emnlp-main)
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| Challenge: | Existing methods to improve neural language models perform poorly on emerging data. |
| Approach: | They propose a lexical-level masking strategy to post-train a neural language model using static data from past years. |
| Outcome: | The proposed method outperforms existing methods on two pre-trained language models, two classification tasks, and four benchmark datasets. |
Look-back Decoding for Open-Ended Text Generation (2023.emnlp-main)
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| Challenge: | Existing approaches to decode open-ended text have addressed degeneration problems in large-scale language models (LLMs) |
| Approach: | They propose an improved decoding algorithm that leverages the Kullback–Leibler divergence to track the distribution distance between current and historical decoding steps. |
| Outcome: | The proposed algorithm outperforms existing methods in document continuation and story generation. |
Nonparametric Masked Language Modeling (2023.findings-acl)
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| Challenge: | Existing language models (LMs) predict tokens with a softmax over a finite vocabulary, which can make it difficult to predict rare tokens or phrases. |
| Approach: | They introduce a nonparametric masked language model that replaces a softmax with a distribution over every phrase in a reference corpus and uses an in-batch approximation to train it. |
| Outcome: | The proposed model outperforms larger parametric models on 16 tasks including classification, fact probing and question answering. |