Papers with token prediction
Better Language Model with Hypernym Class Prediction (2022.acl-long)
Copied to clipboard
| Challenge: | Class-based language models (LMs) have been devised to address context sparsity in n-gram LMs for decades. |
| Approach: | They propose to use class-based prediction to improve generalization for rare words by annealing from predicting the class to token prediction during training. |
| Outcome: | The proposed model improves perplexity without sacrificing performance on rare words. |
Autoregressive Pre-Training on Pixels and Texts (2024.emnlp-main)
Copied to clipboard
| Challenge: | pixel-based language modeling integrates visual and textual data to improve performance of language models. |
| Approach: | They propose a method that integrates visual and textual data into an autoregressive framework. |
| Outcome: | The proposed method improves performance of pixel-based language models by incorporating visual and textual data. |
Tree-Structured Non-Autoregressive Decoding for Sequence-to-Sequence Text Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Autoregressive Transformers suffer from high inference latency due to sequential token generation. |
| Approach: | They propose a tree-structured non-autoregressive decoding paradigm that bridges autoregressive and non-automatic decoding. |
| Outcome: | The proposed paradigm outperforms autoregressive and non-autoregressive decoding in machine translation and paraphrase generation. |
LaMemo: Language Modeling with Look-Ahead Memory (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing approaches to model long-term dependencies are limited to long texts with thousands of words. |
| Approach: | They propose a look-ahead memory that augments the recurrence memory by attending to the right-side tokens and interpolating with the old memory states to maintain long-term information in the history. |
| Outcome: | Experiments on widely used language modeling benchmarks show that LaMemo outperforms baseline models with recurrence memory. |
Outdated Issue Aware Decoding for Factual Knowledge Editing (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing knowledge editing methods retain outdated responses for reasoning questions . naively retraining LLMs can be computationally intensive and can lead to catastrophic forgetting . |
| Approach: | They propose a simple yet effective decoding strategy to enhance edited models on reasoning questions. |
| Outcome: | The proposed method outDates ISsue aware deCOding (DISCO) to improve models on reasoning questions. |
“You are grounded!”: Latent Name Artifacts in Pre-trained Language Models (2020.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models perpetuate biases originating in their training corpus to downstream models. |
| Approach: | They focus on the representations of given names in pre-trained language models and show that name perturbation can have an effect on downstream tasks. |
| Outcome: | The proposed model can be used to model the representation of given names in pre-trained language models on reading comprehension probes where name perturbation changes the model answers. |
Towards Opening the Black Box of Neural Machine Translation: Source and Target Interpretations of the Transformer (2022.emnlp-main)
Copied to clipboard
| Challenge: | Neural Machine Translation (NMT) relies on source sentence and target prefix attributions for each input token. |
| Approach: | They propose an interpretability method that tracks input tokens’ attributions for both contexts and extends it to any encoder-decoder Transformer-based model. |
| Outcome: | The proposed method can be extended to any encoder-decoder Transformer-based model and provides insights into their behaviour. |
M-Ped: Multi-Prompt Ensemble Decoding for Large Language Models (2025.findings-emnlp)
Copied to clipboard
Jiaxin Guo, Daimeng Wei, Yuanchang Luo, Hengchao Shang, Zongyao Li, Jinlong Yang, Zhanglin Wu, Zhiqiang Rao, Shimin Tao, Hao Yang
| Challenge: | a new ensemble decoding approach enhances the performance of Large Language Models. |
| Approach: | They propose a multi-prompt ensemble decoding approach to enhance LLM performance . they submit n variations of prompts with X to LLMs in batch mode to decode and derive probability distributions . |
| Outcome: | The proposed method improves pass@k rates, LENS metrics and BLEU scores on diverse NLP tasks. |
Calibrating Language Models with Adaptive Temperature Scaling (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models' confidence scores are degraded after fine-tuning with reinforcement learning from human feedback. |
| Approach: | They propose a post-hoc calibration method that predicts a temperature scaling parameter for each token prediction. |
| Outcome: | Adaptive temperature scaling improves calibration by over 10% compared to prior methods . RLHF fine-tuning improves model accuracy, but degradation is not significant . |
LLM-Codec: Neural Audio Codec Meets Language Model Objectives (2026.findings-acl)
Copied to clipboard
| Challenge: | Neural audio codecs are optimized for waveform reconstruction rather than autoregressive prediction. |
| Approach: | They propose to augment codec training with language-model-facing objectives while keeping both codec and LLM architectures unchanged. |
| Outcome: | The proposed model improves speech coherence and predictability by preserving the semantic alignment between audio and text representations. |
GiLT: Augmenting Transformer Language Models with Dependency Graphs (2026.acl-long)
Copied to clipboard
| Challenge: | Recent work focuses on syntactic tree structures of languages, in particular constituency tree structures. |
| Approach: | They propose a Graph-Infused Layers Transformer Language Model which leverages dependency graphs to augment Transformer language models. |
| Outcome: | The proposed model achieves better syntactic generalization while maintaining competitive perplexity compared with baseline models. |
SSG: Logit-Balanced Vocabulary Partitioning for LLM Watermarking (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models can generate highquality, human-like content, but they also pose risks such as infringement of proprietary interests, misuse of outputs, and spread harmful misinformation. |
| Approach: | They propose a method that partitions the vocabulary into two logit-balanced subsets and lifts the lower bound of watermark strength for each token prediction. |
| Outcome: | The proposed method lifts the lower bound of watermark strength for each token prediction, thereby improving watermark detectability. |