Summary of Evaluating Very Long-term Conversational Memory Of Llm Agents, by Adyasha Maharana et al.
Evaluating Very Long-Term Conversational Memory of LLM Agents
by Adyasha Maharana, Dong-Ho Lee, Sergey Tulyakov, Mohit Bansal, Francesco Barbieri, Yuwei Fang
First submitted to arxiv on: 27 Feb 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 The proposed research addresses the gap in evaluating machine-generated responses in very long-term open-domain dialogues, which existing works have not explored beyond five chat sessions. The study introduces a machine-human pipeline using large language models and retrieval augmented generation techniques to generate high-quality, very long-term dialogues grounded on personas and temporal event graphs. Each agent can share and react to images, and the generated conversations are verified and edited by human annotators for consistency and grounding. A dataset called LoCoMo is collected, consisting of 300-turn conversations over up to 35 sessions. The study presents a comprehensive evaluation benchmark measuring long-term memory in models across question answering, event summarization, and multi-modal dialogue generation tasks. Experimental results show that large language models struggle with understanding lengthy conversations and comprehending long-range temporal and causal dynamics, although employing strategies like long-context LLMs or RAG can offer improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how to make machines talk for a very long time without getting confused. Right now, most research only looks at conversations that are five chats long. The researchers created a new way for machines to have conversations using big language models and special techniques. They used this method to generate very long conversations (up to 35 chats) about specific topics, like people’s personalities or events in time. Human judges checked the conversations to make sure they made sense and were connected to each other. The researchers also collected a huge dataset of these long conversations, which they called LoCoMo. They tested how well machines did at understanding these conversations and found that they still have trouble keeping track of what’s happening over a long time. |
Keywords
* Artificial intelligence * Grounding * Multi modal * Question answering * Rag * Retrieval augmented generation * Summarization
