"Let's Argue Both Sides": Argument Generation Can Force Small Models to Utilize Previously Inaccessible Reasoning Capabilities

• URL: http://arxiv.org/abs/2410.12997v1
• Date: Wed, 16 Oct 2024 19:49:30 GMT
• Title: "Let's Argue Both Sides": Argument Generation Can Force Small Models to Utilize Previously Inaccessible Reasoning Capabilities
• Authors: Kaveh Eskandari Miandoab, Vasanth Sarathy,
• Abstract summary: We propose Argument Generation as a method of forcing models to utilize their reasoning capabilities. Our method involves the generation of arguments for each possible inference result, and asking the end model to rank the generated arguments.
• Score: 0.8999666725996974
• License:
• Abstract: Large Language Models (LLMs), despite achieving state-of-the-art results in a number of evaluation tasks, struggle to maintain their performance when logical reasoning is strictly required to correctly infer a prediction. In this work, we propose Argument Generation as a method of forcing models to utilize their reasoning capabilities when other approaches such as chain-of-thought reasoning prove insufficient. Our method involves the generation of arguments for each possible inference result, and asking the end model to rank the generated arguments. We show that Argument Generation can serve as an appropriate substitute for zero-shot prompting techniques without the requirement to add layers of complexity. Furthermore, we argue that knowledge-probing techniques such as chain-of-thought reasoning and Argument Generation are only useful when further reasoning is required to infer a prediction, making them auxiliary to more common zero-shot approaches. Finally, we demonstrate that our approach forces larger gains in smaller language models, showcasing a complex relationship between model size and prompting methods in foundation models.

Related papers

• When factorization meets argumentation: towards argumentative explanations [0.0]
  We propose a novel model that combines factorization-based methods with argumentation frameworks (AFs) Our framework seamlessly incorporates side information, such as user contexts, leading to more accurate predictions.
  arXiv  Detail & Related papers  (2024-05-13T19:16:28Z)
• Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation [19.799266797193344]
  Argumentation-based systems often lack explainability while supporting decision-making processes. Counterfactual and semifactual explanations are interpretability techniques. We show that counterfactual and semifactual queries can be encoded in weak-constrained Argumentation Framework.
  arXiv  Detail & Related papers  (2024-05-07T07:27:27Z)
• Conceptual and Unbiased Reasoning in Language Models [98.90677711523645]
  We propose a novel conceptualization framework that forces models to perform conceptual reasoning on abstract questions. We show that existing large language models fall short on conceptual reasoning, dropping 9% to 28% on various benchmarks. We then discuss how models can improve since high-level abstract reasoning is key to unbiased and generalizable decision-making.
  arXiv  Detail & Related papers  (2024-03-30T00:53:53Z)
• Large Language Models as Analogical Reasoners [155.9617224350088]
  Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks. We introduce a new prompting approach, analogical prompting, designed to automatically guide the reasoning process of large language models.
  arXiv  Detail & Related papers  (2023-10-03T00:57:26Z)
• Retrieval-Augmented Generative Question Answering for Event Argument Extraction [66.24622127143044]
  We propose a retrieval-augmented generative QA model (R-GQA) for event argument extraction. It retrieves the most similar QA pair and augments it as prompt to the current example's context, then decodes the arguments as answers. Our approach outperforms substantially prior methods across various settings.
  arXiv  Detail & Related papers  (2022-11-14T02:00:32Z)
• Learning to Reason With Relational Abstractions [65.89553417442049]
  We study how to build stronger reasoning capability in language models using the idea of relational abstractions. We find that models that are supplied with such sequences as prompts can solve tasks with a significantly higher accuracy.
  arXiv  Detail & Related papers  (2022-10-06T00:27:50Z)
• Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI [60.142926537264714]
  We introduce the methodology of Faithfulness-through-Counterfactuals. It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation. It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
  arXiv  Detail & Related papers  (2022-05-25T03:40:59Z)
• Aspect-Controlled Neural Argument Generation [65.91772010586605]
  We train a language model for argument generation that can be controlled on a fine-grained level to generate sentence-level arguments for a given topic, stance, and aspect. Our evaluation shows that our generation model is able to generate high-quality, aspect-specific arguments. These arguments can be used to improve the performance of stance detection models via data augmentation and to generate counter-arguments.
  arXiv  Detail & Related papers  (2020-04-30T20:17:22Z)
• AMPERSAND: Argument Mining for PERSuAsive oNline Discussions [41.06165177604387]
  We propose a computational model for argument mining in online persuasive discussion forums. Our approach relies on identifying relations between components of arguments in a discussion thread. Our models obtain significant improvements compared to recent state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-04-30T10:33:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.