Summary of Newsinterview: a Dataset and a Playground to Evaluate Llms’ Ground Gap Via Informational Interviews, by Michael Lu et al.
NewsInterview: a Dataset and a Playground to Evaluate LLMs’ Ground Gap via Informational Interviews
by Michael Lu, Hyundong Justin Cho, Weiyan Shi, Jonathan May, Alexander Spangher
First submitted to arxiv on: 21 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses a crucial limitation in Large Language Models (LLMs) by focusing on grounding language and strategic dialogue in journalistic interviews. The authors curate a dataset of 40,000 two-person informational interviews from NPR and CNN, revealing that LLMs struggle with using acknowledgments and pivoting to higher-level questions. To facilitate the development of agents with longer-horizon rewards, the researchers create a realistic simulated environment incorporating source personas and persuasive elements. The experiments show that while source LLMs mimic human behavior in information sharing, interviewer LLMs struggle with recognizing answered questions and engaging persuasively, leading to suboptimal information extraction across model size and capability. This study underscores the need for enhancing LLMs’ strategic dialogue capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well computers can have conversations like humans do. Right now, computers are good at talking, but they often don’t understand what’s being talked about or know when to move on. To help them be better conversationalists, the researchers used a big dataset of interviews from NPR and CNN. They found that computers struggle with saying things like “I see” and moving on to more important questions. To make computers better at this, the researchers created a special environment for them to practice having conversations. The results show that while computers can be good at sharing information, they still have trouble knowing when someone has answered their question or being persuasive. This study shows that we need to make computers better at having meaningful conversations. |
Keywords
» Artificial intelligence » Cnn » Grounding
