Papers with TDA methods
Towards Tracing Knowledge in Language Models Back to the Training Data (2022.findings-emnlp)
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| Challenge: | Prior work on training data attribution (TDA) may offer effective tools for identifying such examples, known as "proponents". |
| Approach: | They propose a benchmark to identify which training examples taught an LM to generate a particular factual assertion. |
| Outcome: | The proposed methods have lower proponent-retrieval precision than baselines that do not have access to the LM. |
Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are difficult to interpret due to their black-box nature and randomness. |
| Approach: | They propose a new method which enhances influence functions by addressing fitting errors by eliminating knowledge bias present in the base model before fine-tuning. |
| Outcome: | The proposed method outperforms existing methods and achieves an average AUC of 91.64%. |