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Assisted Debate Builder with Large Language Models

arXiv 2405.13015 ↗First posted 14 May 2024cs.CL cs.AIcited by 1

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Abstract (arXiv)

We introduce ADBL2, an assisted debate builder tool. It is based on the capability of large language models to generalise and perform relation-based argument mining in a wide-variety of domains. It is the first open-source tool that leverages relation-based mining for (1) the verification of pre-established relations in a debate and (2) the assisted creation of new arguments by means of large language models. ADBL2 is highly modular and can work with any open-source large language models that are used as plugins. As a by-product, we also provide the first fine-tuned Mistral-7B large language model for relation-based argument mining, usable by ADBL2, which outperforms existing approaches for this task with an overall F1-score of 90.59% across all domains.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 30 references that are in the map. First posted on arXiv 14 May 2024.

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9 of 30 references.

References · 9

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  3. 2023Mistral 7Bcited by 3,065
  4. 2022Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learningcited by 886
  5. 2023LLM-Pruner: On the Structural Pruning of Large Language Modelscited by 643
  6. 2024GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projectioncited by 447
  7. 2024Can Large Language Models perform Relation-based Argument Mining?cited by 23
  8. 2020Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluationcited by 20
  9. 2016A Comparative Study of Ranking-based Semantics for Abstract Argumentationcited by 15

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