paper-with-me

홈 › Papers

Investigating Human Response, Behaviour, and Preference in Joint-Task Interaction

2020-11-27 · Alan Lindsay, Bart Craenen, Sara Dalzel-Job, Robin L. Hill, Ronald P. A. Petrick

Human interaction relies on a wide range of signals, including non-verbal cues. In order to develop effective Explainable Planning (XAIP) agents it is important that we understand the range and utility of these communication channels. Our starting point is existing results from joint task interaction and their study in cognitive science. Our intention is that these lessons can inform the design of interaction agents -- including those using planning techniques -- whose behaviour is conditioned on the user's response, including affective measures of the user (i.e., explicitly incorporating the user's affective state within the planning model). We have identified several concepts at the intersection of plan-based agent behaviour and joint task interaction and have used these to design two agents: one reactive and the other partially predictive. We have designed an experiment in order to examine human behaviour and response as they interact with these agents. In this paper we present the designed study and the key questions that are being investigated. We also present the results from an empirical analysis where we examined the behaviour of the two agents for simulated users.

📄 PDF Abstract BibTeX arXiv:2011.14016

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Interpreting Language Reward Models via Contrastive Explanations

2024-11-25 · Junqi Jiang, Tom Bewley, Saumitra Mishra, Freddy Lecue 외

Reward models (RMs) are a crucial component in the alignment of large language models' (LLMs) outputs with human values. RMs approximate human preferences over possible LLM responses to the same prompt by predicting and …

Attribute

Comparing Bad Apples to Good Oranges: Aligning Large Language Models via Joint Preference Optimization

2024-03-31 · Hritik Bansal, Ashima Suvarna, Gantavya Bhatt, Nanyun Peng 외

A common technique for aligning large language models (LLMs) relies on acquiring human preferences by comparing multiple generations conditioned on a fixed context. This method, however, relies solely on pairwise compari…

Towards Understanding Sycophancy in Language Models

2023-10-20 · Mrinank Sharma, Meg Tong, Tomasz Korbak, David Duvenaud 외

Human feedback is commonly utilized to finetune AI assistants. But human feedback may also encourage model responses that match user beliefs over truthful ones, a behaviour known as sycophancy. We investigate the prevale…

Text Generation

Imitating Human Behaviour with Diffusion Models

2023-01-25 · Tim Pearce, Tabish Rashid, Anssi Kanervisto, Dave Bignell 외

Diffusion models have emerged as powerful generative models in the text-to-image domain. This paper studies their application as observation-to-action models for imitating human behaviour in sequential environments. Huma…

Can LLMs Capture Human Preferences?

2023-05-04 · Ali Goli, Amandeep Singh

We explore the viability of Large Language Models (LLMs), specifically OpenAI's GPT-3.5 and GPT-4, in emulating human survey respondents and eliciting preferences, with a focus on intertemporal choices. Leveraging the ex…

Benchmarking