NewsInterview: a Dataset and a Playground to Evaluate LLMs' Ground Gap via Informational Interviews (2411.13779v1)

Abstract: LLMs have demonstrated impressive capabilities in generating coherent text but often struggle with grounding language and strategic dialogue. To address this gap, we focus on journalistic interviews, a domain rich in grounding communication and abundant in data. We curate a dataset of 40,000 two-person informational interviews from NPR and CNN, and reveal that LLMs are significantly less likely than human interviewers to use acknowledgements and to pivot to higher-level questions. Realizing that a fundamental deficit exists in multi-turn planning and strategic thinking, we develop a realistic simulated environment, incorporating source personas and persuasive elements, in order to facilitate the development of agents with longer-horizon rewards. Our experiments show that while source LLMs mimic human behavior in information sharing, interviewer LLMs struggle with recognizing when questions are answered and engaging persuasively, leading to suboptimal information extraction across model size and capability. These findings underscore the need for enhancing LLMs' strategic dialogue capabilities.

• The paper introduces a dataset of 40,000 interviews that exposes significant grounding gaps in LLMs during complex informational dialogues.
• It presents NewsInterview, a simulation environment that challenges LLMs with varied conversational dynamics and diverse interview personas.
• The study highlights the need for enhanced emotional intelligence and long-range planning in LLMs to achieve more human-like persuasive communication.

An Expert Overview of "NewsInterview: a Dataset and a Playground to Evaluate LLMs' Grounding Gap via Informational Interviews"

The paper "NewsInterview: a Dataset and a Playground to Evaluate LLMs' Grounding Gap via Informational Interviews" addresses a significant gap in the abilities of LLMs concerning grounded language and strategic dialogue. Through the creation of a new dataset and simulation environment, the authors provide both a foundational resource and testing ground to evaluate and enhance LLMs' capabilities in conducting journalistic interviews.

Data Collection and Insights

One of the paper's key contributions is the assembly of a large-scale dataset comprising 40,000 informational interviews sourced from reputable media outlets such as NPR and CNN. This dataset is an invaluable resource, given the paucity of naturalistic dialogue data available for studying grounding communication on this scale. The authors utilize this data to perform an in-depth discourse analysis, revealing that current LLMs fail to replicate the nuanced grounding language and strategic questioning observed in human interviewers. Human journalists employ acknowledgment statements and diverse questioning strategies to maintain engaging and effective dialogue—capabilities that LLMs currently falter in replicating.

Simulated Environment for Interview Evaluation

Beyond the dataset, the authors innovate with a simulated environment—NewsInterview—that is designed to probe and cultivate the strategic dialogue skills of LLMs. In this simulation, LLMs act as interviewers tasked with extracting information from sources exhibiting varied personas, such as "anxious," "avoidant," or "adversarial." This setup introduces diverse conversational dynamics that reflect real-world interviewing challenges. The paper finds that while LLMs can mimic certain aspects of human dialogue, they struggle significantly with persuasive communication and multi-turn planning. These deficiencies underscore the need for improved strategic dialogue capabilities in LLMs, particularly in the context of achieving long-horizon goals through conversation.

Implications for LLM Development

The findings hold crucial implications for the evolution of LLMs. From a theoretical standpoint, they prompt a re-examination of LLMing objectives to better incorporate emotional intelligence and strategic planning. Practically, the insights gleaned could inform the development of more nuanced and effective conversational agents capable of real-world applicability in fields such as journalism, customer service, and beyond. The integration of game-like environments with strategic constraints could offer fertile ground for future advancements in ethical AI design and deployment.

Future Directions

Future research inspired by this paper may look towards incorporating richer, long-range reward signals that incentivize grounding communication and strategic questioning. Such work could aim to advance the training protocols of LLMs, with the objective of achieving more human-like adaptability and intelligence in dialogue systems. Investigating methodologies that leverage the interaction between varying persona types and corresponding persuasive techniques may yield further breakthroughs in understanding and simulating human conversational dynamics.

In summary, the paper sets a pioneering path for developing LLMs into more sophisticated conversational partners by utilizing real-world datasets and strategic game simulation environments. While the challenges addressed clearly highlight significant shortcomings in current LLM capabilities, they also set clear goals for future improvements, laying a foundational framework for the next generation of interactive AI systems.