Papers by Veronika Thost
A Graph per Persona: Reasoning about Subjective Natural Language Descriptions (2024.findings-acl)
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| Challenge: | Existing large language models (LLMs) perform poorly in reasoning about subjective knowledge, showing strong biases and lack interpretability requirements. |
| Approach: | They propose a novel approach for reasoning about subjective knowledge that integrates potential and implicit meanings and explicitly models the relational nature of the information. |
| Outcome: | The proposed model outperforms several prominent large language models on the OpinionQA dataset, showing its unique advantages and complementary nature. |
Knowledge Graph Compression Enhances Diverse Commonsense Generation (2023.emnlp-main)
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| Challenge: | Existing models use commonsense knowledge graphs to extract subgraphs of relevant knowledge pertaining to concepts in the input but due to the large coverage and vast scale of ConceptNet, the extracted subgraph may contain loosely related, redundant and irrelevant information. |
| Approach: | They propose to apply a differentiable graph compression algorithm to extract subgraphs of relevant knowledge from input sentences. |
| Outcome: | The proposed algorithm achieves better quality-diversity tradeoff than a large language model with 100 times the number of parameters. |