Papers by Kwang-Ting Cheng
Fewer is More: Boosting Math Reasoning with Reinforced Context Pruning (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown impressive capabilities, yet they struggle with math reasoning. |
| Approach: | They propose a coarse-to-fine pruner that prunes unimportant tokens to fit the context window. |
| Outcome: | The proposed approach outperforms prompting baselines across various LLMs and 5 math datasets and achieves 4.55% absolute improvements without any fine-tuning. |
LLM-FP4: 4-Bit Floating-Point Quantized Transformers (2023.emnlp-main)
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| Challenge: | Existing quantization solutions are integer-based and struggle with bit widths below 8 bits. |
| Approach: | They propose a method for quantizing weights and activations in large language models down to 4-bit floating-point values in a post-training manner. |
| Outcome: | The proposed method outperforms existing methods on common sense zero-shot reasoning tasks by 12.7 points. |
RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation Quantization (2024.findings-emnlp)
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| Challenge: | Low-Rank Adaptation (LoRA) improves training efficiency by updating only a small portion of the weights in Large Language Models. |
| Approach: | They propose a rotation-aware scheme to fine-tune rotated outlier-free LLMs for effective weight-activation quantization. |
| Outcome: | The proposed method improves low-bit LoRA convergence and post-training quantization robustness. |