Language Bottleneck Models: A Framework for Interpretable Knowledge Tracing and Beyond

• URL: http://arxiv.org/abs/2506.16982v1
• Date: Fri, 20 Jun 2025 13:21:14 GMT
• Title: Language Bottleneck Models: A Framework for Interpretable Knowledge Tracing and Beyond
• Authors: Antonin Berthon, Mihaela van der Schaar,
• Abstract summary: We recast Knowledge Tracing as an inverse problem: learning the minimum natural-language summary that makes past answers explainable and future answers predictable.<n>Our Language Bottleneck Model (LBM) consists of an encoder LLM that writes an interpretable knowledge summary and a frozen decoder LLM that must reconstruct and predict student responses using only that summary text.<n> Experiments on synthetic arithmetic benchmarks and the large-scale Eedi dataset show that LBMs rival the accuracy of state-of-the-art KT and direct LLM methods while requiring orders-of-magnitude fewer student trajectories.
• Score: 55.984684518346924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately assessing student knowledge is critical for effective education, yet traditional Knowledge Tracing (KT) methods rely on opaque latent embeddings, limiting interpretability. Even LLM-based approaches generate direct predictions or summaries that may hallucinate without any accuracy guarantees. We recast KT as an inverse problem: learning the minimum natural-language summary that makes past answers explainable and future answers predictable. Our Language Bottleneck Model (LBM) consists of an encoder LLM that writes an interpretable knowledge summary and a frozen decoder LLM that must reconstruct and predict student responses using only that summary text. By constraining all predictive information to pass through a short natural-language bottleneck, LBMs ensure that the summary contains accurate information while remaining human-interpretable. Experiments on synthetic arithmetic benchmarks and the large-scale Eedi dataset show that LBMs rival the accuracy of state-of-the-art KT and direct LLM methods while requiring orders-of-magnitude fewer student trajectories. We demonstrate that training the encoder with group-relative policy optimization, using downstream decoding accuracy as a reward signal, effectively improves summary quality.

Related papers

• Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering [66.5524727179286]
  NOVA is a framework designed to identify high-quality data that aligns well with the learned knowledge to reduce hallucinations.<n>It includes Internal Consistency Probing (ICP) and Semantic Equivalence Identification (SEI) to measure how familiar the LLM is with instruction data.<n>To ensure the quality of selected samples, we introduce an expert-aligned reward model, considering characteristics beyond just familiarity.
  arXiv  Detail & Related papers  (2025-02-11T08:05:56Z)
• Semantic Consistency Regularization with Large Language Models for Semi-supervised Sentiment Analysis [20.503153899462323]
  We propose a framework for semi-supervised sentiment analysis.<n>We introduce two prompting strategies to semantically enhance unlabeled text.<n> Experiments show our method achieves remarkable performance over prior semi-supervised methods.
  arXiv  Detail & Related papers  (2025-01-29T12:03:11Z)
• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context.<n>We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters.<n>We propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• Bridging LLMs and KGs without Fine-Tuning: Intermediate Probing Meets Subgraph-Aware Entity Descriptions [49.36683223327633]
  Large Language Models (LLMs) encapsulate extensive world knowledge and exhibit powerful context modeling capabilities.<n>We propose a novel framework that synergizes the strengths of LLMs with robust knowledge representation to enable effective and efficient KGC.<n>We achieve a 47% relative improvement over previous methods based on non-fine-tuned LLMs and, to our knowledge, are the first to achieve classification performance comparable to fine-tuned LLMs.
  arXiv  Detail & Related papers  (2024-08-13T10:15:55Z)
• Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models [0.0]
  Large Language Models (LLMs) have shown exceptional performance in text processing. This paper proposes a novel approach to training LLMs using knowledge transfer from a random forest (RF) ensemble. We generate outputs for fine-tuning, enhancing the model's ability to classify and explain its decisions.
  arXiv  Detail & Related papers  (2024-06-07T13:31:51Z)
• A Context-Aware Approach for Enhancing Data Imputation with Pre-trained Language Models [0.18416014644193068]
  CRILM uses pre-trained language models to create contextually relevant descriptors for missing values.<n>Our evaluations demonstrate CRILM's superior performance and robustness across MCAR, MAR, and challenging MNAR scenarios.
  arXiv  Detail & Related papers  (2024-05-28T00:08:29Z)
• Small Models are LLM Knowledge Triggers on Medical Tabular Prediction [39.78560996984352]
  We propose SERSAL, a general self-prompting method by synergy learning with small models.<n>We show that SERSAL attains substantial improvement compared to linguistic prompting methods.
  arXiv  Detail & Related papers  (2024-03-03T17:35:52Z)
• Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation [128.01050030936028]
  We propose an information refinement training method named InFO-RAG. InFO-RAG is low-cost and general across various tasks. It improves the performance of LLaMA2 by an average of 9.39% relative points.
  arXiv  Detail & Related papers  (2024-02-28T08:24:38Z)
• Improving Factual Consistency of News Summarization by Contrastive Preference Optimization [65.11227166319546]
  Large language models (LLMs) generate summaries that are factually inconsistent with original articles.<n>These hallucinations are challenging to detect through traditional methods.<n>We propose Contrastive Preference Optimization (CPO) to disentangle the LLMs' propensities to generate faithful and fake content.
  arXiv  Detail & Related papers  (2023-10-30T08:40:16Z)
• Evaluating and Explaining Large Language Models for Code Using Syntactic Structures [74.93762031957883]
  This paper introduces ASTxplainer, an explainability method specific to Large Language Models for code. At its core, ASTxplainer provides an automated method for aligning token predictions with AST nodes. We perform an empirical evaluation on 12 popular LLMs for code using a curated dataset of the most popular GitHub projects.
  arXiv  Detail & Related papers  (2023-08-07T18:50:57Z)

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.