Unveiling Imitation Learning: Exploring the impact of Data Falsity to Large Language Model (2024.findings-acl)
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| Challenge: | Recent studies have focused on improving open-source language models through imitation learning. |
| Approach: | They propose to use false pairs to control the factuality ratio of a dataset to study noise. |
| Outcome: | The proposed model is based on a Falsity-Controllable dataset with false pairs and false pairs to control the factuality ratio. |
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| Challenge: | acquiring large amounts of high-quality data can be challenging due to data scarcity, privacy concerns, and high costs. |
| Approach: | They propose a method which reverses instruction-following issues caused by uniform format of synthetic data and proposes unlearning techniques to mitigate these flaws. |
| Outcome: | The proposed method reverses instruction-following issues caused by pattern overfitting without compromising performance on benchmarks at relatively low cost. |
Is It Good Data for Multilingual Instruction Tuning or Just Bad Multilingual Evaluation for Large Language Models? (2024.emnlp-main)
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| Challenge: | Existing practices of fine-tuning and evaluating multilingual large language models may not align with this objective due to a heavy reliance on translation. |
| Approach: | They propose to use translated or native instruction data to fine-tune multilingual large language models. |
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Demystifying Instruction Mixing for Fine-tuning Large Language Models (2024.acl-srw)
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| Challenge: | Instruction tuning is effective for aligning large language models with human instructions, but the procedure to optimizing the mixing of instruction datasets is still unclear. |
| Approach: | They categorize instructions into three primary types: NLP downstream tasks, coding, and general chat. |
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Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning (2025.emnlp-main)
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Yunhao Gou, Hansi Yang, Zhili Liu, Kai Chen, Yihan Zeng, Lanqing Hong, Zhenguo Li, Qun Liu, Bo Han, James Kwok, Yu Zhang
| Challenge: | Visual Instruction Tuning (VIT) aims to enhance Multimodal Large Language Models (MLLMs), but its effectiveness is often compromised by corrupted datasets with issues such as hallucinated content and poor OCR quality. |
| Approach: | They propose a corruption-robust training paradigm that surpasses existing strategies for mitigating the effects of corrupted data. |
| Outcome: | The proposed training paradigm surpasses existing strategies for mitigating the effects of corrupted data. |
An Investigation of the (In)effectiveness of Counterfactually Augmented Data (2022.acl-long)
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| Challenge: | Pretrained language models tend to rely on spurious correlations and generalize poorly to out-of-distribution (OOD) data. |
| Approach: | They propose to use counterfactually-augmented data (CAD) to identify robust features that are invariant under distribution shift to train models for OOD generalization. |
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Dynamics of Instruction Fine-Tuning for Chinese Large Language Models (2025.coling-main)
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| Challenge: | Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models. |
| Approach: | They investigate the effects of data quantity, model size, and data construction methods on instruction tuning for Chinese LLMs. |
| Outcome: | The proposed model includes over 40,000 high-quality instruction instances covering ten underlying abilities. |
Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck? (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit a puzzling disparity in their formal linguistic competence, even after training on trillions of tokens. |
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GSM-Noise: Exploring and Enhancing Large Language Models’ Reasoning under Noisy Inputs (2026.findings-acl)
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| Challenge: | Large language models struggle when dealing with complex, ill-formed, or noisy inputs . open-source models are less robust, while closed-source ones are more robust . |
| Approach: | They propose to use GSM-Noise to refine inputs before engaging in in-depth analysis to improve LLM robustness under noisy conditions. |
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Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor (2023.acl-long)
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| Challenge: | Instruction tuning enables pretrained language models to perform new tasks from inference-time natural language descriptions without human supervision. |
| Approach: | They propose to use a dataset of natural language instructions to generate large datasets with no human supervision. |
| Outcome: | The proposed dataset outperforms open-source models on various benchmarks, and is cost-effective. |
Mitigating Shortcuts in Language Models with Soft Label Encoding (2024.lrec-main)
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| Challenge: | Recent studies have shown that large language models rely on spurious correlations in the data for natural language understanding (NLU) tasks. |
| Approach: | They propose a framework for debiasing shortcuts and a dummy class to encode shortcuts into a model and use it to generate soft labels. |
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