Papers with token-level analysis
Diagnosing Memorization in Chain-of-Thought Reasoning, One Token at a Time (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) perform well on reasoning benchmarks but often fail when inputs alter slightly, raising concerns about overreliance on memorization. |
| Approach: | They propose a framework for Source-aware Token-level Identification of Memorization which attributes each token in a reasoning chain to one of multiple memorization sources based on their statistical co-occurrence with the token in the pretraining corpus. |
| Outcome: | The proposed framework attributes each token in a reasoning chain to one of multiple memorization sources based on their statistical co-occurrence with the token in the pretraining corpus. |
Mitigating Biases in Hate Speech Detection from A Causal Perspective (2023.findings-emnlp)
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| Challenge: | Existing methods to detect hate speech are prone to spurious correlations between training data and labels, which could lead to biased treatment of vulnerable and minority groups. |
| Approach: | They propose to use grammar induction to find grammar patterns for hate speech and analyze this phenomenon from a causal perspective. |
| Outcome: | The proposed methods can detect hate speech from a causal perspective and are robust to different datasets. |
Columbo: Expanding Abbreviated Column Names for Tabular Data Using Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing solutions to expand table names are limited by the abbreviated column names of tables. |
| Approach: | They propose to use abbreviated tables to expand column names . they propose to introduce four new datasets with real-world abbrevations . |
| Outcome: | The proposed solution outperforms NameGuess in terms of accuracy and consistency over five datasets. |