Papers with finetuning process
Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models (2024.acl-long)
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Zhengxin Zhang, Dan Zhao, Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Qing Li, Yong Jiang, Zhihao Jia
| Challenge: | Existing methods to finetun large language models (LLMs) only update a small number of trainable parameters, or attempt to reduce the memory footprint during the training phase of the finetune process. |
| Approach: | They propose quantized side tuing (QST) which quantizes an LLM’s model weights into 4-bit to reduce the memory footprint of the original weights. |
| Outcome: | The proposed method reduces the memory footprint of the model weights, optimizer states, and intermediate activations while reducing the memory requirements. |
GAP-Gen: Guided Automatic Python Code Generation (2023.eacl-srw)
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| Challenge: | Several previous approaches convert a sentence into a formal statement by mapping verbs to functions in the formal language. |
| Approach: | They propose a Guided Automatic Python Code Generation method based on Python syntactic constraints and semantic constraints. |
| Outcome: | The proposed method achieves better results on automatic Python code generation task than previous methods. |
Where to start? Analyzing the potential value of intermediate models (2023.emnlp-main)
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| Challenge: | a finetuned model may be better base models than the vanilla pretrained model . this scheme, often referred to as intertraining, is the focus of the present work . |
| Approach: | They propose a scheme to analyze the potential intertraining gain independently for the target dataset and for a base model being considered as a starting point. |
| Outcome: | The proposed model is strong even if training data was not aligned with target dataset. |
Analyzing the Forgetting Problem in Pretrain-Finetuning of Open-domain Dialogue Response Models (2021.eacl-main)
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| Challenge: | a large-scale unsupervised pretraining has been shown to greatly boost the performance of natural language processing models. |
| Approach: | They propose an intuitive finetuning strategy to regularize the finetune process . they propose a mix-review strategy to alleviate the forgetting problem . |
| Outcome: | The proposed strategy regularizes the finetuning process, and the forgetting problem is alleviated . the proposed strategy also improves the performance of the resulting model . |
LiST: Lite Prompted Self-training Makes Parameter-efficient Few-shot Learners (2022.findings-naacl)
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| Challenge: | LiST is an efficient method for fine-tuning large pre-trained language models in few-shot learning settings. |
| Approach: | They propose a method for efficient fine-tuning of large pre-trained language models in few-shot settings using self-training and meta-learning. |
| Outcome: | The proposed method outperforms GPT-3 in-context learning by 33% on few-shot tasks. |
Multilingual Sentence Transformer as A Multilingual Word Aligner (2022.findings-emnlp)
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| Challenge: | Multilingual pretrained language models (mPLMs) have shown their effectiveness in multilingual word alignment induction, but these methods usually start from mBERT or XLM-R. |
| Approach: | They propose to fine tune multilingual sentence Transformer LaBSE for alignment induction using parallel corpus and a parallel corpora model. |
| Outcome: | The proposed model outperforms existing models on seven language pairs and achieves new state-of-the-art on zero-shot language pairs. |
Few-Shot Natural Language to First-Order Logic Translation via Code Generation (2025.naacl-long)
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| Challenge: | Recent studies have focused on translation of natural language to first-order logical formula (NL-FOL) but these methods face challenges such as inconsistency between training and inference phases and data-intensive finetuning process. |
| Approach: | They propose a method for translating natural language into first-order logical formulas using code snippets. |
| Outcome: | The proposed method surpasses training-free baselines and is comparable to supervised models trained on the full training data. |
VLEU: a Method for Automatic Evaluation for Generalizability of Text-to-Image Models (2024.emnlp-main)
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| Challenge: | Existing metrics, such as CLIP, measure the semantic alignment between single prompts and their corresponding images, but they fail to evaluate a model’s generalizability across a broad spectrum of textual inputs. |
| Approach: | They propose a metric that leverages the power of Large Language Models to sample from the visual text domain and assess its generalizability. |
| Outcome: | The proposed metric evaluates the generalizability of T2I models and provides valuable insights during the finetuning process. |
Finetuning Pretrained Transformers into RNNs (2021.emnlp-main)
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Jungo Kasai, Hao Peng, Yizhe Zhang, Dani Yogatama, Gabriel Ilharco, Nikolaos Pappas, Yi Mao, Weizhu Chen, Noah A. Smith
| Challenge: | Efficient transformers outperform recurrent neural networks in natural language generation, but this comes with significant computational cost and memory footprint during generation. |
| Approach: | They propose to convert a pretrained transformer into its efficient recurrent counterpart, improving efficiency while maintaining accuracy. |
| Outcome: | The proposed transformers outperform recurrent neural networks in natural language generation but come with significant computational and memory footprint during generation. |