CoGen: Learning from Feedback with Coupled Comprehension and Generation

Abstract: Systems with both language comprehension and generation capabilities can benefit from the tight connection between the two. This work studies coupling comprehension and generation with focus on continually learning from interaction with users. We propose techniques to tightly integrate the two capabilities for both learning and inference. We situate our studies in two-player reference games, and deploy various models for thousands of interactions with human users, while learning from interaction feedback signals. We show dramatic improvements in performance over time, with comprehension-generation coupling leading to performance improvements up to 26% in absolute terms and up to 17% higher accuracies compared to a non-coupled system. Our analysis also shows coupling has substantial qualitative impact on the system's language, making it significantly more human-like.

• The paper demonstrates that integrating comprehension and generation yields up to 26.07% improvement in generation and 19.48% in comprehension.
• The study employs a two-player reference game framework to refine models through continual human feedback, enhancing linguistic richness and alignment with human communication.
• The paper shows that the coupled approach improves data efficiency by reducing required human interactions to less than one-third compared to non-coupled baselines.

Coupling Language Comprehension and Generation for Improved Interactive Learning

The paper "COGEN: Learning from Feedback with Coupled Comprehension and Generation" by Mustafa Omer Gul and Yoav Artzi explores the symbiotic relationship between language comprehension and generation within the context of two-player reference games, enhancing systems through continual learning from user interactions. This research is particularly focused on integrating these processes to improve LLM performance through pragmatic coupling strategies—a significant aspect in interactive AI systems that seek to keep evolving with user input.

The core of this work lies in a thorough investigation of how comprehension and generation, usually treated as isolated processes, can be interconnected to foster reciprocal improvement. The authors deploy models in a controlled experimental setup involving dynamic interactions with human users, where the systems acquire feedback to refine both their comprehension and generation capabilities over time. The two roles—comprehension (as the listener) and generation (as the speaker)—are assigned strategically in the study to investigate the dynamics of coupling these abilities.

The experimental results presented are compelling, exhibiting substantial absolute performance improvements of up to 26.07% in generation tasks and 19.48% in comprehension tasks through the proposed coupled approach. Specifically, the coupled systems outperformed non-coupled baselines by 17.10% and 14.80% for generation and comprehension, respectively. Furthermore, notable data efficiency was demonstrated; coupled systems achieved superior performance with significantly fewer human interactions—a reduction to less than one-third compared to non-coupled baselines.

The paper utilizes the Rational Speech Act (RSA) framework as an inspiration for joint inference processes, where generating and understanding language occurs not in parallel silos but through an integrated loop of feedback and refinement. The authors emphasize that coupling at both inference and training stages transforms the qualitative dynamics of the learning process, exposing the generation model to human language more authentically, thus preventing the potential drift from human-like language patterns observed in systems that use their generated data exclusively.

One of the more nuanced insights from this study is the influence of coupling on language style and syntactic properties. The analysis indicates an increase in linguistic richness, with coupled models registering larger effective vocabulary sizes and better alignment with human language according to automated metrics like MAUVE. It suggests a tangible alignment with the human mode of communication—indicative of the model's refined ability to handle pragmatic aspects of language use, such as using context effectively to determine the details necessary for distinguishing the target from distractors.

The focus on continual learning from human interactions positions this research within the broader scope of efforts to create adaptive and contextually aware AI systems. The implications are manifold, laying the groundwork for developing conversational agents that not only adapt over time but do so by leveraging the inherent feedback loop between speaking and understanding. Such advancements could greatly impact conversational AI, making systems more natural and intuitive in their interactions.

In terms of future developments, this paper suggests multiple avenues, including exploring language variations in multilingual setups and deploying scenarios that involve more nuanced, multi-turn interactions. Additionally, refining reinforcement learning strategies to further enhance model adaptability while managing the complexity and costs of real-world deployments could also be promising directions.

Overall, "COGEN: Learning from Feedback with Coupled Comprehension and Generation" provides a meaningful contribution to the field, offering a robust framework for applying coupled language processes in practical AI systems and furthering our understanding of how these processes can interact to produce human-like language behavior dynamically in learning systems.