REVIEW 5 cited by
Explore Spurious Correlations at the Concept Level in Language Models for Text Classification
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Language models (LMs) have achieved notable success in numerous NLP tasks, employing both fine-tuning and in-context learning (ICL) methods. While language models demonstrate exceptional performance, they face robustness challenges due to spurious correlations arising from imbalanced label distributions in training data or ICL exemplars. Previous research has primarily concentrated on word, phrase, and syntax features, neglecting the concept level, often due to the absence of concept labels and difficulty in identifying conceptual content in input texts. This paper introduces two main contributions. First, we employ ChatGPT to assign concept labels to texts, assessing concept bias in models during fine-tuning or ICL on test data. We find that LMs, when encountering spurious correlations between a concept and a label in training or prompts, resort to shortcuts for predictions. Second, we introduce a data rebalancing technique that incorporates ChatGPT-generated counterfactual data, thereby balancing label distribution and mitigating spurious correlations. Our method's efficacy, surpassing traditional token removal approaches, is validated through extensive testing.
Forward citations
Cited by 5 Pith papers
-
The SuperActivator Mechanism: Transformers Concentrate Reliable Concept Signals in the Tail
Reliable concept presence in transformers is concentrated in the extreme high-activation tail of in-concept tokens; thresholding that tail improves concept detection and localization.
-
CAT: Causal Attention Tuning For Injecting Fine-grained Causal Knowledge into Large Language Models
A training method that injects token-level causal labels into attention improves out-of-distribution accuracy on a synthetic benchmark and slightly on math/reasoning tasks.
-
MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs
Merging specialized LLMs into a MoE can be improved by replacing averaging with Dare/Ties merging and by using perplexity-based routing, while heterogeneous experts can be merged with projectors and a sequence-level router.
-
Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment
Causal reward modeling applies MMD regularization to RLHF reward models to make reward scores statistically independent of spurious features, reducing measured length, sycophancy, concept, and demographic biases in ex...
-
Expert-guided Clinical Text Augmentation via Query-Based Model Collaboration
Constraining LLM rewriting with a biomedical NER model improves medical entity preservation and reduces hallucinations in synthetic clinical notes, with modest downstream gains on MIMIC-III tasks.
Discussion (0). Continue with ORCID to comment.