Belief Revision: The Adaptability of Large Language Models Reasoning

• URL: http://arxiv.org/abs/2406.19764v2
• Date: Thu, 17 Oct 2024 05:28:24 GMT
• Title: Belief Revision: The Adaptability of Large Language Models Reasoning
• Authors: Bryan Wilie, Samuel Cahyawijaya, Etsuko Ishii, Junxian He, Pascale Fung,
• Abstract summary: We introduce Belief-R, a new dataset designed to test LMs' belief revision ability when presented with new evidence. Inspired by how humans suppress prior inferences, this task assesses LMs within the newly proposed delta reasoning framework. We evaluate $sim$30 LMs across diverse prompting strategies and found that LMs generally struggle to appropriately revise their beliefs in response to new information.
• Score: 63.0281286287648
• License:
• Abstract: The capability to reason from text is crucial for real-world NLP applications. Real-world scenarios often involve incomplete or evolving data. In response, individuals update their beliefs and understandings accordingly. However, most existing evaluations assume that language models (LMs) operate with consistent information. We introduce Belief-R, a new dataset designed to test LMs' belief revision ability when presented with new evidence. Inspired by how humans suppress prior inferences, this task assesses LMs within the newly proposed delta reasoning ($\Delta R$) framework. Belief-R features sequences of premises designed to simulate scenarios where additional information could necessitate prior conclusions drawn by LMs. We evaluate $\sim$30 LMs across diverse prompting strategies and found that LMs generally struggle to appropriately revise their beliefs in response to new information. Further, models adept at updating often underperformed in scenarios without necessary updates, highlighting a critical trade-off. These insights underscore the importance of improving LMs' adaptiveness to changing information, a step toward more reliable AI systems.

Related papers

• Understanding the Relationship between Prompts and Response Uncertainty in Large Language Models [55.332004960574004]
  Large language models (LLMs) are widely used in decision-making, but their reliability, especially in critical tasks like healthcare, is not well-established. This paper investigates how the uncertainty of responses generated by LLMs relates to the information provided in the input prompt. We propose a prompt-response concept model that explains how LLMs generate responses and helps understand the relationship between prompts and response uncertainty.
  arXiv  Detail & Related papers  (2024-07-20T11:19:58Z)
• Reliable, Adaptable, and Attributable Language Models with Retrieval [144.26890121729514]
  Parametric language models (LMs) are trained on vast amounts of web data. They face practical challenges such as hallucinations, difficulty in adapting to new data distributions, and a lack of verifiability. We advocate for retrieval-augmented LMs to replace parametric LMs as the next generation of LMs.
  arXiv  Detail & Related papers  (2024-03-05T18:22:33Z)
• Where is the answer? Investigating Positional Bias in Language Model Knowledge Extraction [36.40833517478628]
  Large language models require updates to remain up-to-date or adapt to new domains. One key is memorizing the latest information in a way that the memorized information is extractable with a query prompt. Despite minimizing document perplexity during fine-tuning, LLMs struggle to extract information through a prompt sentence.
  arXiv  Detail & Related papers  (2024-02-16T06:29:16Z)
• FELM: Benchmarking Factuality Evaluation of Large Language Models [40.78878196872095]
  We introduce a benchmark for Factuality Evaluation of large Language Models, referred to as felm. We collect responses generated from large language models and annotate factuality labels in a fine-grained manner. Our findings reveal that while retrieval aids factuality evaluation, current LLMs are far from satisfactory to faithfully detect factual errors.
  arXiv  Detail & Related papers  (2023-10-01T17:37:31Z)
• Information Association for Language Model Updating by Mitigating LM-Logical Discrepancy [68.31760483418901]
  Large Language Models(LLMs) struggle with providing current information due to the outdated pre-training data. Existing methods for updating LLMs, such as knowledge editing and continual fine-tuning, have significant drawbacks in generalizability of new information. We identify the core challenge behind these drawbacks: the LM-logical discrepancy featuring the difference between language modeling probabilities and logical probabilities.
  arXiv  Detail & Related papers  (2023-05-29T19:48:37Z)
• Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge [72.63368052592004]
  We study LMs' abilities to make inferences based on injected facts (or propagate those facts) We find that existing methods for updating knowledge show little propagation of injected knowledge. Yet, prepending entity definitions in an LM's context improves performance across all settings.
  arXiv  Detail & Related papers  (2023-05-02T17:59:46Z)
• AES Systems Are Both Overstable And Oversensitive: Explaining Why And Proposing Defenses [66.49753193098356]
  We investigate the reason behind the surprising adversarial brittleness of scoring models. Our results indicate that autoscoring models, despite getting trained as "end-to-end" models, behave like bag-of-words models. We propose detection-based protection models that can detect oversensitivity and overstability causing samples with high accuracies.
  arXiv  Detail & Related papers  (2021-09-24T03:49:38Z)

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.