Fast and Accurate Contextual Knowledge Extraction Using Cascading Language Model Chains and Candidate Answers

• URL: http://arxiv.org/abs/2507.22921v1
• Date: Mon, 21 Jul 2025 14:31:16 GMT
• Title: Fast and Accurate Contextual Knowledge Extraction Using Cascading Language Model Chains and Candidate Answers
• Authors: Lee Harris,
• Abstract summary: We propose, implement, and apply the Language Model Chain (LMC) algorithm.<n>In this, a language model's response to a given prompt is only correct if it exists in the collection of possible answers.<n>We used the LMC algorithm to extract patient dates of birth from medical documents.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Language models can capture complex relationships in given text, but these are notorious for being costly and for producing information that does not exist (i.e., hallucinations). Furthermore, the resources invested into producing this information would be wasted if it were incorrect. We address these issues by proposing, implementing, and applying the Language Model Chain (LMC) algorithm. In this, a language model's response to a given prompt about given text is only correct if it exists in the collection of possible (i.e., candidate) answers, and text corresponding to incorrect responses is fed into a more predictive (but slower) language model. This process is repeated for a collection of language models, or until all predictions about the text are correct. We used the LMC algorithm to extract patient dates of birth from medical documents, and combining a collection of language models in a multi-stage cascade significantly increased prediction speed and accuracy over individual language models, while greatly reducing the number of corresponding hallucinations. We believe that the novel LMC algorithm significantly contributes to the knowledge extraction field, and that this should be explored much further in the future.

Related papers

• Ask a Local: Detecting Hallucinations With Specialized Model Divergence [0.16874375111244325]
  We introduce "Ask a Local", a novel hallucination detection method for large language models.<n>Our approach computes divergence between perplexity distributions of language-specialized models to identify potentially hallucinated spans.<n>Our results on a human-annotated question-answer dataset spanning 14 languages demonstrate consistent performance across languages.
  arXiv  Detail & Related papers  (2025-06-03T20:00:49Z)
• Large language models can be zero-shot anomaly detectors for time series? [9.249657468385779]
  sigllm is a framework for time series anomaly detection using large language models. We present a prompt-based detection method that directly asks a language model to indicate which elements of the input are anomalies. We show that the forecasting method significantly outperformed the prompting method in all 11 datasets with respect to the F1 score.
  arXiv  Detail & Related papers  (2024-05-23T16:21:57Z)
• Fine-tuning Language Models for Factuality [96.5203774943198]
  Large pre-trained language models (LLMs) have led to their widespread use, sometimes even as a replacement for traditional search engines. Yet language models are prone to making convincing but factually inaccurate claims, often referred to as 'hallucinations' In this work, we fine-tune language models to be more factual, without human labeling.
  arXiv  Detail & Related papers  (2023-11-14T18:59:15Z)
• Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval [56.65147231836708]
  We develop SWIM-IR, a synthetic retrieval training dataset containing 33 languages for fine-tuning multilingual dense retrievers. SAP assists the large language model (LLM) in generating informative queries in the target language. Our models, called SWIM-X, are competitive with human-supervised dense retrieval models.
  arXiv  Detail & Related papers  (2023-11-10T00:17:10Z)
• RegaVAE: A Retrieval-Augmented Gaussian Mixture Variational Auto-Encoder for Language Modeling [79.56442336234221]
  We introduce RegaVAE, a retrieval-augmented language model built upon the variational auto-encoder (VAE) It encodes the text corpus into a latent space, capturing current and future information from both source and target text. Experimental results on various datasets demonstrate significant improvements in text generation quality and hallucination removal.
  arXiv  Detail & Related papers  (2023-10-16T16:42:01Z)
• mFACE: Multilingual Summarization with Factual Consistency Evaluation [79.60172087719356]
  Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries. We leverage factual consistency evaluation models to improve multilingual summarization.
  arXiv  Detail & Related papers  (2022-12-20T19:52:41Z)
• Mixed Attention Transformer for LeveragingWord-Level Knowledge to Neural Cross-Lingual Information Retrieval [15.902630454568811]
  We propose a novel Mixed Attention Transformer (MAT) that incorporates external word level knowledge, such as a dictionary or translation table. By encoding the translation knowledge into an attention matrix, the model with MAT is able to focus on the mutually translated words in the input sequence.
  arXiv  Detail & Related papers  (2021-09-07T00:33:14Z)
• Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages [81.90356787324481]
  Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict. This work shows a comparison of a neural model and character language models with varying amounts on target language data. Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected.
  arXiv  Detail & Related papers  (2020-10-20T17:31:07Z)
• XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning [68.57658225995966]
  Cross-lingual Choice of Plausible Alternatives (XCOPA) is a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages. We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods falls short compared to translation-based transfer.
  arXiv  Detail & Related papers  (2020-05-01T12:22:33Z)
• Limits of Detecting Text Generated by Large-Scale Language Models [65.46403462928319]
  Some consider large-scale language models that can generate long and coherent pieces of text as dangerous, since they may be used in misinformation campaigns. Here we formulate large-scale language model output detection as a hypothesis testing problem to classify text as genuine or generated.
  arXiv  Detail & Related papers  (2020-02-09T19:53:23Z)

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.