Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions

By Stephen Roller, Y-Lan Boureau, Jason Aaron Edward Weston, Antoine Bordes, Emily Dinan, Angela Fan, D. Gunning, Da Ju, Margaret Li, Spencer Poff and 6 more. 2020

62 citations recorded by the source corpus.

We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been built so far, and the gaping holes we have not filled yet. We present a biased view, focusing on work done by our own group, while citing related work in each area. In particular, we discuss in detail the properties of continual learning, providing engaging content, and being well-behaved -- and how to measure success in providing them. We end with a discussion of our experience and learnings, and our recommendations to the community.

Subfields: Artificial Intelligence, Natural Language Processing, LLM Agents & Tool Use

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Citations in the corpus

Llama 2: Open Foundation and Fine-Tuned Chat Models (2023, background): “Work from Roller et al. (2020) and Dinan et al. (2021) also illuminates the difficulties tied to chatbot-oriented LLMs, with concerns ranging from privacy to misleading expertise claims.”
BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage (2022, methodology): “The promise of such an approach is that the distribution of data will more closely match those organic users’ desires, rather than decided by the researchers themselves when creating datasets (Gabriel et al., 2020; Roller et al., 2020; Shuster et al., 2021b; Ouyang et al., 2022).”
Survey of Hallucination in Natural Language Generation (2022, methodology): “One of the possible reasons for self-contradiction is that current dialogue systems tend to have a short memory of dialogue history [160].”
Controlling Style in Generated Dialogue (2020, methodology): “However, our encoder/decoder-based Transformer generator was pretrained with Reddit context strings always passed into the encoder (Roller et al., 2020b).”
SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures (2021, methodology): “…neural generative dialogue models trained to mimic human English-language open-domain conversations have become engaging (Adiwardana et al., 2020; Roller et al., 2020b), but are still prone to uttering problematic language, e.g., displaying toxicity or bias, or agreeing with offensive statements…”
Learning from data in the mixed adversarial non-adversarial case: Finding the helpers and ignoring the trolls (2022, background): “The promise of this approach is that the distribution of data will more closely match those organic users’ desires, rather than decided by the researchers themselves when creating datasets (Gabriel et al., 2020; Roller et al., 2020; Shuster et al., 2020; Ouyang et al., 2022).”
Rome was built in 1776: A Case Study on Factual Correctness in Knowledge-Grounded Response Generation (2021, background): “One of the biggest challenges in the ﬁeld of conversational AI is to develop agents that can generate coherent and grammatical responses whilst also seamlessly blending in knowledge to produce informative responses (Roller et al., 2020a).”
Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents (2022, methodology): “Hence, finding a reliable evaluation technique is more difficult, and there is currently no single standard method that is agreed upon (Deriu 1https://parl.ai/projects/humaneval et al., 2021; Huang et al., 2020; Roller et al., 2020).”