Score Before You Speak: Improving Persona Consistency in Dialogue Generation using Response Quality Scores

• URL: http://arxiv.org/abs/2508.06886v1
• Date: Sat, 09 Aug 2025 08:30:06 GMT
• Title: Score Before You Speak: Improving Persona Consistency in Dialogue Generation using Response Quality Scores
• Authors: Arpita Saggar, Jonathan C. Darling, Vania Dimitrova, Duygu Sarikaya, David C. Hogg,
• Abstract summary: Persona-based dialogue generation is an important milestone towards building conversational artificial intelligence.<n>We propose a novel framework SBS (Score-Before-Speaking), which outperforms previous methods.<n>We show that score-conditioned training allows existing models to better capture a spectrum of persona-consistent dialogues.
• Score: 2.150144047598779
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Persona-based dialogue generation is an important milestone towards building conversational artificial intelligence. Despite the ever-improving capabilities of large language models (LLMs), effectively integrating persona fidelity in conversations remains challenging due to the limited diversity in existing dialogue data. We propose a novel framework SBS (Score-Before-Speaking), which outperforms previous methods and yields improvements for both million and billion-parameter models. Unlike previous methods, SBS unifies the learning of responses and their relative quality into a single step. The key innovation is to train a dialogue model to correlate augmented responses with a quality score during training and then leverage this knowledge at inference. We use noun-based substitution for augmentation and semantic similarity-based scores as a proxy for response quality. Through extensive experiments with benchmark datasets (PERSONA-CHAT and ConvAI2), we show that score-conditioned training allows existing models to better capture a spectrum of persona-consistent dialogues. Our ablation studies also demonstrate that including scores in the input prompt during training is superior to conventional training setups. Code and further details are available at https://arpita2512.github.io/score_before_you_speak

Related papers

• X-TURING: Towards an Enhanced and Efficient Turing Test for Long-Term Dialogue Agents [56.64615470513102]
  The Turing test examines whether AIs exhibit human-like behaviour in natural language conversations.<n>Traditional setting limits each participant to one message at a time and requires constant human participation.<n>This paper proposes textbftextscX-Turing, which enhances the original test with a textitburst dialogue pattern.
  arXiv  Detail & Related papers  (2024-08-19T09:57:28Z)
• Pre-training Multi-party Dialogue Models with Latent Discourse Inference [85.9683181507206]
  We pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying. To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model.
  arXiv  Detail & Related papers  (2023-05-24T14:06:27Z)
• PK-ICR: Persona-Knowledge Interactive Context Retrieval for Grounded Dialogue [21.266410719325208]
  Persona and Knowledge Dual Context Identification is a task to identify persona and knowledge jointly for a given dialogue. We develop a novel grounding retrieval method that utilizes all contexts of dialogue simultaneously.
  arXiv  Detail & Related papers  (2023-02-13T20:27:26Z)
• GODEL: Large-Scale Pre-Training for Goal-Directed Dialog [119.1397031992088]
  We introduce GODEL, a large pre-trained language model for dialog. We show that GODEL outperforms state-of-the-art pre-trained dialog models in few-shot fine-tuning setups. A novel feature of our evaluation methodology is the introduction of a notion of utility that assesses the usefulness of responses.
  arXiv  Detail & Related papers  (2022-06-22T18:19:32Z)
• A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation [107.82729587882397]
  It is expensive to scale up current persona-based dialogue datasets. Each data sample in this task is more complex to learn with than conventional dialogue data. We propose a data manipulation method, which is model-agnostic to be packed with any persona-based dialogue generation model.
  arXiv  Detail & Related papers  (2022-04-21T03:49:54Z)
• Response Generation with Context-Aware Prompt Learning [19.340498579331555]
  We present a novel approach for pre-trained dialogue modeling that casts the dialogue generation problem as a prompt-learning task. Instead of fine-tuning on limited dialogue data, our approach, DialogPrompt, learns continuous prompt embeddings optimized for dialogue contexts. Our approach significantly outperforms the fine-tuning baseline and the generic prompt-learning methods.
  arXiv  Detail & Related papers  (2021-11-04T05:40:13Z)
• Data-Efficient Methods for Dialogue Systems [4.061135251278187]
  Conversational User Interface (CUI) has become ubiquitous in everyday life, in consumer-focused products like Siri and Alexa. Deep learning underlies many recent breakthroughs in dialogue systems but requires very large amounts of training data, often annotated by experts. In this thesis, we introduce a series of methods for training robust dialogue systems from minimal data.
  arXiv  Detail & Related papers  (2020-12-05T02:51:09Z)
• DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances [18.199473005335093]
  This paper presents DialogBERT, a novel conversational response generation model that enhances previous PLM-based dialogue models. To efficiently capture the discourse-level coherence among utterances, we propose two training objectives, including masked utterance regression. Experiments on three multi-turn conversation datasets show that our approach remarkably outperforms the baselines.
  arXiv  Detail & Related papers  (2020-12-03T09:06:23Z)
• The Adapter-Bot: All-In-One Controllable Conversational Model [66.48164003532484]
  We propose a dialogue model that uses a fixed backbone model such as DialGPT and triggers on-demand dialogue skills via different adapters. Depending on the skills, the model is able to process multiple knowledge types, such as text, tables, and emphatic responses. We evaluate our model using automatic evaluation by comparing it with existing state-of-the-art conversational models.
  arXiv  Detail & Related papers  (2020-08-28T10:59:31Z)
• Learning an Unreferenced Metric for Online Dialogue Evaluation [53.38078951628143]
  We propose an unreferenced automated evaluation metric that uses large pre-trained language models to extract latent representations of utterances. We show that our model achieves higher correlation with human annotations in an online setting, while not requiring true responses for comparison during inference.
  arXiv  Detail & Related papers  (2020-05-01T20:01:39Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)

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.