Summary of Agreemate: Teaching Llms to Haggle, by Ainesh Chatterjee et al.
AgreeMate: Teaching LLMs to Haggle
by Ainesh Chatterjee, Samuel Miller, Nithin Parepally
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 The AgreeMate framework enables Large Language Models (LLMs) to engage in strategic price negotiations through natural language interactions. By applying recent advancements to a negotiation setting where agents bargain on goods using coarse actions, the authors demonstrate the effectiveness of LLMs as agents within a decoupled bargaining architecture. The study showcases the impact of prompt engineering, fine-tuning, and chain-of-thought prompting on model performance, as measured by novel metrics. Furthermore, attention probing reveals the models’ attention to semantic relationships between tokens during negotiations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The AgreeMate framework helps computers have smart conversations about prices. It’s like a game where two sides negotiate over goods. Researchers used special computer models called Large Language Models to play this game and found that making the models better at understanding language helped them make better deals. They even looked at what these models pay attention to during negotiations, which is important for making good agreements. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Prompt » Prompting
