TELLER: Dual-Path Iterative Preference Optimization for Table Entity Linking
Quick Answer
TELLER introduces a dual-path iterative preference optimization for table entity linking, enhancing accuracy on benchmarks like TableInstruct (from 94.35% to 94.50%) and MammoTab V2 (from 87.59% to 88.20%).
Quick Take
This model adapts preference data dynamically, addressing static supervision limitations and improving reasoning generation rates.
Key Points
- TELLER optimizes entity linking by learning from errors and reasoning.
- Direct-answer path accuracy improved from 94.35% to 94.50% on TableInstruct.
- MammoTab V2 accuracy increased from 87.59% to 88.20% using TELLER.
- Reasoning path accuracy rose from 79.09% to 81.85% on MammoTab V2.
- Iterative preference learning enhances both concise predictions and reasoning.
Paper Resources
Source Excerpt
Entity linking in tables matches short and ambiguous cell mentions to their corresponding knowledge-base entities. Existing approaches typically rely on data preprocessing pipelines that retain either compact or extensive table content as contextual evidence, and then formulate entity linking as a language generation task for instruction-tuned models; recent systems further incorporate explicit reasoning to disambiguate challenging mentions. However, their training supervision is usually static:
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