Towards Dialogues for Joint Human-AI Reasoning and Value Alignment
- URL: http://arxiv.org/abs/2405.18073v1
- Date: Tue, 28 May 2024 11:29:57 GMT
- Title: Towards Dialogues for Joint Human-AI Reasoning and Value Alignment
- Abstract summary: We argue that enabling human-AI dialogue, purposed to support joint reasoning (i.e., 'inquiry'), is important for ensuring that AI decision making is aligned with human values and preferences.
- Score: 3.432828370470386
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We argue that enabling human-AI dialogue, purposed to support joint reasoning (i.e., 'inquiry'), is important for ensuring that AI decision making is aligned with human values and preferences. In particular, we point to logic-based models of argumentation and dialogue, and suggest that the traditional focus on persuasion dialogues be replaced by a focus on inquiry dialogues, and the distinct challenges that joint inquiry raises. Given recent dramatic advances in the performance of large language models (LLMs), and the anticipated increase in their use for decision making, we provide a roadmap for research into inquiry dialogues for supporting joint human-LLM reasoning tasks that are ethically salient, and that thereby require that decisions are value aligned.
Related papers
- SAD: A Large-Scale Strategic Argumentative Dialogue Dataset [60.33125467375306]
In practice, argumentation is often realized as multi-turn dialogue.<n>We present the first large-scale textbfStrategic textbfArgumentative textbfDialogue dataset, consisting of 392,822 examples.
arXiv Detail & Related papers (2026-01-12T11:11:37Z) - We Argue to Agree: Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems for Tourism [32.411223995938144]
We introduce PACT, a dataset of Personality-driven Argumentation-based negotiation Conversations for Tourism sector.<n>This dataset features three distinct personality profiles, viz. Argumentation Profile, Preference Profile, and Buying Style Profile.<n>We conduct comparative experiments between pre-trained and fine-tuned LLMs for the PAN-DG task.<n>This underscores the effectiveness of PACT in enhancing personalization and reasoning capabilities in negotiation dialogue systems.
arXiv Detail & Related papers (2025-09-14T06:16:42Z) - DialogueReason: Rule-Based RL Sparks Dialogue Reasoning in LLMs [54.4857963044859]
We propose DialogueReason, a reasoning paradigm that uncovers the lost roles in monologue-style reasoning models.<n>Our work consists of an analysis of monologue reasoning patterns and the development of a dialogue-based reasoning approach.
arXiv Detail & Related papers (2025-05-11T16:39:58Z) - Leveraging Explicit Reasoning for Inference Integration in Commonsense-Augmented Dialogue Models [12.116834890063146]
Open-domain dialogue systems need to grasp social commonsense to understand and respond effectively to human users.
Existing approaches to commonsense-augmented dialogue rely on implicit reasoning to integrate commonsense inferences during response generation.
arXiv Detail & Related papers (2024-06-13T14:07:52Z) - COOPER: Coordinating Specialized Agents towards a Complex Dialogue Goal [43.78731248855523]
We argue that it is more feasible to accomplish complex dialogue goals by comprehensively considering and jointly promoting their different aspects.
We make complex dialogue goals more approachable and elicit greater intelligence via the collaboration of individual agents.
arXiv Detail & Related papers (2023-12-19T02:07:42Z) - Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems [29.394466123216258]
This study provides a comprehensive overview of the primary characteristics of a dialogue agent, their corresponding open-domain datasets, and the methods used to benchmark these datasets.
We propose UNIT, a UNified dIalogue dataseT constructed from conversations of existing datasets for different dialogue tasks capturing the nuances for each of them.
arXiv Detail & Related papers (2023-07-14T10:05:47Z) - A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects [100.75759050696355]
We provide a comprehensive overview of the prominent problems and advanced designs for conversational agent's proactivity in different types of dialogues.
We discuss challenges that meet the real-world application needs but require a greater research focus in the future.
arXiv Detail & Related papers (2023-05-04T11:38:49Z) - AugESC: Dialogue Augmentation with Large Language Models for Emotional
Support Conversation [54.29029787836075]
Crowdsourced dialogue corpora are usually limited in scale and topic coverage due to the expensive cost of data curation.
In this work, we leverage large language models for dialogue augmentation in the task of emotional support conversation (ESC)
We construct AugESC, an augmented dataset for the ESC task, which largely extends the scale and topic coverage of the crowdsourced ESConv corpus.
arXiv Detail & Related papers (2022-02-26T03:17:08Z) - Conversational Norms for Human-Robot Dialogues [0.32228025627337864]
This paper describes a recently initiated research project aiming at supporting development of computerised dialogue systems that handle breaches of conversational norms.
Our approach is to model dialogue and norms with co-operating distributed grammar systems (CDGSs)
arXiv Detail & Related papers (2021-03-02T13:28:18Z) - 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) - Rethinking Dialogue State Tracking with Reasoning [76.0991910623001]
This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data.
Empirical results demonstrate that our method significantly outperforms the state-of-the-art methods by 38.6% in terms of joint belief accuracy for MultiWOZ 2.1.
arXiv Detail & Related papers (2020-05-27T02:05:33Z) - Dialogue-Based Relation Extraction [53.2896545819799]
We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE.
We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks.
Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings.
arXiv Detail & Related papers (2020-04-17T03:51:57Z) - 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.