An LLM-generated candidate list, averaged in embedding space and ranked against dictionary terms, outperforms several supervised reverse dictionary models on generalization splits.
A Unified Model for Reverse Dictionary and Definition Modelling
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abstract
We build a dual-way neural dictionary to retrieve words given definitions, and produce definitions for queried words. The model learns the two tasks simultaneously and handles unknown words via embeddings. It casts a word or a definition to the same representation space through a shared layer, then generates the other form in a multi-task fashion. Our method achieves promising automatic scores on previous benchmarks without extra resources. Human annotators prefer the model's outputs in both reference-less and reference-based evaluation, indicating its practicality. Analysis suggests that multiple objectives benefit learning.
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2024 1verdicts
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GEAR: A Simple GENERATE, EMBED, AVERAGE AND RANK Approach for Unsupervised Reverse Dictionary
An LLM-generated candidate list, averaged in embedding space and ranked against dictionary terms, outperforms several supervised reverse dictionary models on generalization splits.