Summary of Socio-emotional Response Generation: a Human Evaluation Protocol For Llm-based Conversational Systems, by Lorraine Vanel et al.
Socio-Emotional Response Generation: A Human Evaluation Protocol for LLM-Based Conversational Systems
by Lorraine Vanel, Ariel R. Ramos Vela, Alya Yacoubi, Chloé Clavel
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Social and Information Networks (cs.SI)
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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 proposes a novel neural architecture that includes an intermediate step in planning socio-emotional strategies before response generation, aiming to increase transparency and trustworthiness in large language models (LLMs). The approach is compared to open-source baseline LLMs and evaluated using both automated metrics and human annotations. A novel evaluation protocol is introduced, consisting of coarse-grained consistency evaluation and finer-grained annotation of responses on social and emotional criteria. The study shows that predicting a sequence of expected strategy labels and using this sequence to generate a response yields better results than direct end-to-end generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it possible for machines to have more natural conversations by planning out the emotions they use in their responses. Right now, large language models can respond in a way that’s relevant and impressive, but we don’t know how they’re doing it or why. The researchers created a new kind of neural network that thinks about what emotional strategies to use before generating a response. They tested this approach against other popular language models and found that it works better. They also developed a new way to evaluate the quality of responses by looking at both their consistency and how well they match certain social and emotional criteria. |
Keywords
» Artificial intelligence » Neural network
