Papers with WikiText-2
Identifying and Reducing Gender Bias in Word-Level Language Models (N19-3)
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| Challenge: | Existing discriminatory biases in training data can be amplified by models . text corpora exhibit socially problematic biase . |
| Approach: | They propose a metric to measure gender bias and a regularization loss term to minimize embeddings onto an embeddable subspace that encodes gender. |
| Outcome: | The proposed method reduces gender bias up to an optimal weight assigned to the loss term, and the model becomes unstable as the perplexity increases. |
Grouped Sequency-arranged Rotation: Optimizing Rotation Transformation for Quantization for Free (2025.acl-srw)
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| Challenge: | Existing rotation-based methods struggle at low bit-widths, such as 2-bit weight quantization. |
| Approach: | They propose a training-free approach to construct an improved rotation matrix . they leverage the Walsh-Hadamard transform with sequency ordering to reduce quantization error . |
| Outcome: | The proposed method demonstrates robust performance on reasoning tasks and high Perplexity (PPL) score on WikiText-2. |
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 . |
| Outcome: | The proposed model is highly sensitive to the order of words within the most recent sentence, but ignores word order in the long-range context, suggesting the distant past is modeled only as a rough semantic field or topic. |
Direct Output Connection for a High-Rank Language Model (D18-1)
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| Challenge: | Neural network language models have played a central role in recent natural language processing advances. |
| Approach: | They propose a recurrent neural network language model that combines probability distributions computed from a final RNN layer and also middle layers. |
| Outcome: | The proposed language model achieves the best score on the Penn Treebank and WikiText-2 benchmark datasets. |
Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge (2025.findings-emnlp)
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Yan-Lun Chen, Yi-Ru Wei, Chia-Yi Hsu, Chia-Mu Yu, Chun-Ying Huang, Ying-Dar Lin, Yu-Sung Wu, Wei-Bin Lee
| Challenge: | Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine- tuned models often leads to degraded performance due to overlapping instruction-following components. |
| Approach: | They propose a layer-wise approach that assigns layer-specific weights to task vectors based on their alignment with instruction-following or task-specific components. |
| Outcome: | The proposed approach outperforms existing methods in learning and forgetting tasks while preserving overall model utility. |
Pruning before Fine-tuning: A Retraining-free Compression Framework for Pre-trained Language Models (2024.lrec-main)
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| Challenge: | Structured pruning is an effective technique for compressing pre-trained language models (PLMs), but it requires retraining, leading to additional computational overhead. |
| Approach: | They propose a task-specific pruning framework that prunes redundant modules of pre-trained language models before fine-tuning them. |
| Outcome: | The proposed pruning framework achieves higher performance on GLUE, SQUAD, WikiText-2, Wik-103, and PTB datasets while reducing the time required for fine-tuning. |