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🔍 Today's pick in Interpretability & Analysis of LMs: In-Context Learning Demonstration Selection via Influence Analysis
🔍 Today's pick in Interpretability & Analysis of LMs: In-Context Learning Demonstration Selection via Influence Analysis
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 by Vinay M.S. 
 by Vinay M.S. 
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@minhhaovan
minhhaovan
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 X. Wu
 X. Wu
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Recent work showed how the performance of LMs using in-context learning (ICL) is heavily dependent on selected demonstrations.
Recent work showed how the performance of LMs using in-context learning (ICL) is heavily dependent on selected demonstrations.
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This work introduces InfICL, a demonstration selection method using influence functions to identify salient training examples to use as demonstrations at inference time. InfICL is tested alongside other examples selection baselines for prompting medium-sized LLMs for COLA and RTE, showing improvements over other methods especially when a smaller number of in-context examples is used. 
This work introduces InfICL, a demonstration selection method using influence functions to identify salient training examples to use as demonstrations at inference time. InfICL is tested alongside other examples selection baselines for prompting medium-sized LLMs for COLA and RTE, showing improvements over other methods especially when a smaller number of in-context examples is used. 
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📄 Paper: 
📄 Paper: 
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https://huggingface.co/papers/2402.11750
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2402.11750
https://huggingface.co/papers/2402.11750
In-Context Learning Demonstration Selection via Influence Analysis (2402.11750)
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🔍 All daily picks in LM interpretability: 
🔍 All daily picks in LM interpretability: 
resource
https://huggingface.co/collections/gsarti/daily-picks-in-interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9
collection
gsarti/daily-picks-in-interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9
https://huggingface.co/collections/gsarti/daily-picks-in-interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9
🔍 Today's pick in Interpretability & Analysis of LMs: In-Context Learning Demonstration Selection via Influence Analysis
 by Vinay M.S. @minhhaovan X. Wu

Recent work showed how the performance of LMs using in-context learning (ICL) is heavily dependent on selected demonstrations.

This work introduces InfICL, a demonstration selection method using influence functions to identify salient training examples to use as demonstrations at inference time. InfICL is tested alongside other examples selection baselines for prompting medium-sized LLMs for COLA and RTE, showing improvements over other methods especially when a smaller number of in-context examples is used. 

📄 Paper: https://huggingface.co/papers/2402.11750

🔍 All daily picks in LM interpretability: https://huggingface.co/collections/gsarti/daily-picks-in-interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9
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kramp clem vladbogo santiviquez nickandbro