A Context Alignment Pre-processor for Enhancing the Coherence of Human-LLM Dialog

• URL: http://arxiv.org/abs/2603.16052v1
• Date: Tue, 17 Mar 2026 01:36:14 GMT
• Title: A Context Alignment Pre-processor for Enhancing the Coherence of Human-LLM Dialog
• Abstract summary: This paper proposes a computational framework called the Context Alignment Pre-processor (C.A.P.)<n>Rather than operating during generation, C.A.P. functions as a pre-processing module between user input and response generation.<n>We argue that C.A.P. is not only a technical refinement but also a step toward shifting human-computer dialogue from one-way command-execution patterns to two-way, self-correcting, partnership-based collaboration.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have made remarkable progress in generating fluent text, but they still face a critical challenge of contextual misalignment in long-term and dynamic dialogue. When human users omit premises, simplify references, or shift context abruptly during interactions with LLMs, the models may fail to capture their actual intentions, producing mechanical or off-topic responses that weaken the collaborative potential of dialogue. To address this problem, this paper proposes a computational framework called the Context Alignment Pre-processor (C.A.P.). Rather than operating during generation, C.A.P. functions as a pre-processing module between user input and response generation. The framework includes three core processes: (1) semantic expansion, which extends a user instruction to a broader semantic span including its premises, literal meaning, and implications; (2) time-weighted context retrieval, which prioritizes recent dialogue history through a temporal decay function approximating human conversational focus; and (3) alignment verification and decision branching, which evaluates whether the dialogue remains on track by measuring the semantic similarity between the current prompt and the weighted historical context. When a significant deviation is detected, C.A.P. initiates a structured clarification protocol to help users and the system recalibrate the conversation. This study presents the architecture and theoretical basis of C.A.P., drawing on cognitive science and Common Ground theory in human-computer interaction. We argue that C.A.P. is not only a technical refinement but also a step toward shifting human-computer dialogue from one-way command-execution patterns to two-way, self-correcting, partnership-based collaboration. Finally, we discuss implementation paths, evaluation methods, and implications for the future design of interactive intelligent systems.

Related papers

• PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments [72.02445514666428]
  We introduce PERMA, a benchmark designed to evaluate persona consistency over time beyond static preference recall.<n>PerMA consists of temporally ordered interaction events spanning multiple sessions and domains, with preference-related queries inserted over time.<n> Experiments demonstrate that by linking related interactions, advanced memory systems can extract more precise preferences and reduce token consumption.
  arXiv  Detail & Related papers  (2026-03-24T14:04:11Z)
• Enhancing Dialogue Systems with Discourse-Level Understanding Using Deep Canonical Correlation Analysis [0.0]
  We propose a novel framework that integrates Deep Canonical Correlation Analysis for discourse-level understanding.<n>This framework learns discourse tokens to capture relationships between utterances and their surrounding context.<n>Experiments on the Ubuntu Dialogue Corpus demonstrate significant enhancement in response selection.
  arXiv  Detail & Related papers  (2025-04-12T06:19:08Z)
• A Multi-view Discourse Framework for Integrating Semantic and Syntactic Features in Dialog Agents [0.0]
  Multiturn dialogue models aim to generate human-like responses by leveraging conversational context.<n>Existing methods often neglect the interactions between these utterances or treat all of them as equally significant.<n>This paper introduces a discourse-aware framework for response selection in retrieval-based dialogue systems.
  arXiv  Detail & Related papers  (2025-04-12T04:22:18Z)
• Unsupervised Extraction of Dialogue Policies from Conversations [3.102576158218633]
  We show how Large Language Models can be instrumental in extracting dialogue policies from datasets. We then propose a novel method for generating dialogue policies utilizing a controllable and interpretable graph-based methodology.
  arXiv  Detail & Related papers  (2024-06-21T14:57:25Z)
• DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning [89.92601337474954]
  Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations. We introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding.
  arXiv  Detail & Related papers  (2023-06-15T10:41:23Z)
• Revisiting Conversation Discourse for Dialogue Disentanglement [88.3386821205896]
  We propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. We develop a structure-aware framework to integrate the rich structural features for better modeling the conversational semantic context. Our work has great potential to facilitate broader multi-party multi-thread dialogue applications.
  arXiv  Detail & Related papers  (2023-06-06T19:17:47Z)
• Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues [8.652711997920463]
  Existing works have improved the performance of dialogue systems by intentionally learning interlocutor personas with sophisticated network structures. This study proposes a method of learning to memorize entailment and discourse relations for persona-consistent dialogue tasks.
  arXiv  Detail & Related papers  (2023-01-12T08:37:00Z)
• Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
  We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs. We employ domain-adaptive training strategies to help the model adapt to the dialogue domains. Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
  arXiv  Detail & Related papers  (2023-01-10T13:18:25Z)
• Semantic Interactive Learning for Text Classification: A Constructive Approach for Contextual Interactions [0.0]
  We propose a novel interaction framework called Semantic Interactive Learning for the text domain. We frame the problem of incorporating constructive and contextual feedback into the learner as a task to find an architecture that enables more semantic alignment between humans and machines. We introduce a technique called SemanticPush that is effective for translating conceptual corrections of humans to non-extrapolating training examples.
  arXiv  Detail & Related papers  (2022-09-07T08:13:45Z)
• Advances in Multi-turn Dialogue Comprehension: A Survey [51.215629336320305]
  Training machines to understand natural language and interact with humans is an elusive and essential task of artificial intelligence. This paper reviews the previous methods from the technical perspective of dialogue modeling for the dialogue comprehension task. In addition, we categorize dialogue-related pre-training techniques which are employed to enhance PrLMs in dialogue scenarios.
  arXiv  Detail & Related papers  (2021-10-11T03:52:37Z)
• Ranking Enhanced Dialogue Generation [77.8321855074999]
  How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures to model the history. This paper proposes a Ranking Enhanced Dialogue generation framework.
  arXiv  Detail & Related papers  (2020-08-13T01:49:56Z)
• Is this Dialogue Coherent? Learning from Dialogue Acts and Entities [82.44143808977209]
  We create the Switchboard Coherence (SWBD-Coh) corpus, a dataset of human-human spoken dialogues annotated with turn coherence ratings. Our statistical analysis of the corpus indicates how turn coherence perception is affected by patterns of distribution of entities. We find that models combining both DA and entity information yield the best performances both for response selection and turn coherence rating.
  arXiv  Detail & Related papers  (2020-06-17T21:02:40Z)

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.