Papers with TracIn
FASTTRACK: Reliable Fact Tracing via Clustering and LLM-Powered Evidence Validation (2024.findings-emnlp)
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| Challenge: | Existing methods to fact tracing rely on assessing the similarity between training samples and the query along a certain dimension, such as lexical similarity, gradient, or embedding space. |
| Approach: | They propose a new approach that harnesses the capabilities of Large Language Models to validate supportive evidence for queries and clusters the training database towards a reduced extent for LLMs to trace facts. |
| Outcome: | The proposed approach outperforms existing methods in accuracy and efficiency while being x33 faster than TracIn. |
On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation (2021.acl-long)
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| Challenge: | Existing methods for explaining "black-box" models such as Influence Functions are becoming more popular. |
| Approach: | They propose a semantic-based evaluation metric that can better align with humans’ judgment of explanations than the widely adopted diagnostic or re-training measures. |
| Outcome: | The proposed method can better align with humans’ judgment of explanations than diagnostic or re-training measures. |
How do languages influence each other? Studying cross-lingual data sharing during LM fine-tuning (2023.emnlp-main)
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| Challenge: | Multilingual language models can learn generalisations useful for other languages . yet, it remains unclear to what extent and under which conditions these models benefit from multilingual data and cross-lingual sharing. |
| Approach: | They propose a training data attribution method to retrieve training samples from multilingual data that are most influential for test predictions in a given language. |
| Outcome: | The proposed method exploits the ability to learn generalisations useful for other languages on zero-shot cross-lingual transfer for many languages. |