Compositional Consistency-Guided Decoding for Three-Way Logical Question Answering
📰 ArXiv cs.AI
Learn to improve three-way logical question answering using compositional consistency-guided decoding to reduce failure modes in large language models
Action Steps
- Implement a compositional inference framework for three-way logical QA
- Define a deterministic negation map to ensure consistency between predictions for H and ¬H
- Train a large language model using a dataset with mechanically negated hypotheses
- Evaluate the model's performance using a consistency-guided decoding metric
- Fine-tune the model to minimize failure modes and improve overall accuracy
Who Needs to Know This
NLP researchers and engineers working on question answering systems can benefit from this technique to improve the accuracy and consistency of their models
Key Insight
💡 Compositional consistency-guided decoding can help large language models avoid practical failure modes in three-way logical question answering
Share This
🤖 Improve 3-way logical QA with compositional consistency-guided decoding! 📈 Reduce failure modes in LLMs and boost accuracy 🚀
Full Article
Title: Compositional Consistency-Guided Decoding for Three-Way Logical Question Answering
Abstract:
arXiv:2604.06196v2 Announce Type: replace-cross Abstract: Three-way logical question answering (QA) assigns one of $\text{True}$, $\text{False}$, or $\text{Unknown}$ to a hypothesis $H$ given a premise set $S$. We study this task as a compact compositional inference problem: predictions for $H$ and for a mechanically negated hypothesis $\neg H$ should agree under a deterministic negation map. Despite this simple structure, large language models (LLMs) can exhibit two practical failure modes: (i)
Abstract:
arXiv:2604.06196v2 Announce Type: replace-cross Abstract: Three-way logical question answering (QA) assigns one of $\text{True}$, $\text{False}$, or $\text{Unknown}$ to a hypothesis $H$ given a premise set $S$. We study this task as a compact compositional inference problem: predictions for $H$ and for a mechanically negated hypothesis $\neg H$ should agree under a deterministic negation map. Despite this simple structure, large language models (LLMs) can exhibit two practical failure modes: (i)
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