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🔍 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: In-Context Learning Demonstration Selection via Influence Analysis (2402.11750)
🔍 All daily picks in LM interpretability: gsarti/daily-picks-in-interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9
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: In-Context Learning Demonstration Selection via Influence Analysis (2402.11750)
🔍 All daily picks in LM interpretability: gsarti/daily-picks-in-interpretability-and-analysis-of-lms-65ae3339949c5675d25de2f9