Evaluating Very Long-Term Conversational Memory of LLM Agents (2024)

Abstract: Existing works on long-term open-domain dialogues focus on evaluating model responses within contexts spanning no more than five chat sessions. Despite advancements in longcontext large language models (LLMs) and retrieval augmented generation (RAG) techniques, their efficacy in very long-term dialogues remains unexplored. To address this research gap, we introduce a machine-human pipeline to generate high-quality, very longterm dialogues by leveraging LLM-based agent architectures and grounding their dialogues on personas and temporal event graphs. Moreover, we equip each agent with the capability of sharing and reacting to images. The generated conversations are verified and edited by human annotators for long-range consistency and grounding to the event graphs. Using this pipeline, we collect LOCOMO, a dataset of very long-term conversations, each encompassing approx. 600 turns and 16K tokens on avg., over up to 32 sessions. Based on LOCOMO, we present a comprehensive evaluation benchmark to measure long-term memory in models, encompassing question answering, event summarization, and multi-modal dialogue generation tasks. Our experimental results indicate that LLMs exhibit challenges in understanding lengthy conversations and comprehending long-range temporal and causal dynamics within dialogues. Employing strategies like long-context LLMs or RAG can offer improvements but these models still substantially lag behind human performance.1

heading: 1 Introduction
text: Despite recent advancements in dialogue models based on LLMs for extended contexts (Bertsch et al., 2023; Xiao et al., 2023), as well as the integration of retrieval augmented generation (RAG)
1Code and data are at https://snap-research.github.io/locomo
†Equal advising.
techniques (Shuster et al., 2021; Ram et al., 2023; Shi et al., 2023), there is still a need for thorough evaluation of their efficacy in handling very long conversations. Indeed, studies in long-term opendomain dialogues have concentrated on assessing model responses within limited contexts e.g., ∼1K tokens over five chat sessions (Xu et al., 2022; Jang et al., 2023b; Zhang et al., 2023). This long term evaluation is crucial for refining engaging chatbots capable of remembering key information from past interactions, to generate empathetic, consistent, and useful responses.
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To this end, we present the first study of very long-term open-domain multi-modal dialogues, closely mirroring real-world online interactions, collected via a human-machine pipeline where we first use LLM-based generative agents to generate conversations and then ask human annotators to fix any long-term inconsistencies in the conversations. Specifically, drawing on the understanding that real-world conversations are a complex blend of collective memories (Assmann and Czaplicka, 1995; Hirst and Manier, 2008), individual viewpoints (Hirst et al., 2018), external influences (Hirst and Echterhoff, 2012), and the unique persona of the speakers (Pruitt and Grudin, 2003; Cooper, 1999; Zhou et al., 2020; Shum et al., 2019), we create very long-term dialogues based on LLM agent with the following features: (1) a unique persona (§3.1); (2) a timeline of causally interlinked events in their lives (§3.2); and (3) reflect & response mechanism to respond based on dialogue history (like in Park et al. (2023)) and image sharing & image reaction behavior which sends or reacts to images (§3.3). Finally, human annotators fix longrange inconsistencies in dialogues, remove irrelevant images, and verify the grounding of dialogs to events (§3.4). With this pipeline, we create LOCOMO, a dataset of 10 very long-term dialogues, each consisting of 600 turns and 16K tokens on avg., over up to 32 sessions (see Figure 1, Table 1). Conventional approaches for evaluating conversational agents in open-domain dialogues involves directly evaluating the agent response based on past dialogue history. It often employs lexical overlap (Papineni et al., 2002) and semantic overlap (Zhang et al., 2019) between ground truth and the agent response, or consistency (Ghazarian et al., 2022), contradiction (Nie et al., 2021; Welleck et al., 2019), and empathy (Zhang et al., 2021a, 2022) of the agent response. However, these evaluation metrics are not well-suited for directly assessing the agent’s comprehension of long-term contexts. In this study, we present a holistic evaluation framework to assess an agent’s proficiency in managing and responding within long-term contexts (see Figure 2). First, agents need to “recall” past context correctly to integrate relevant infor-
mation into future responses. We present a direct examination of their memory via a question answering (QA) task (§4.1). We classify questions into five distinct reasoning types to evaluate memory from multiple perspectives: single-hop, multi-hop, temporal, commonsense or world knowledge, and adversarial. Second, agents also need to recognize long-range causal and temporal connections in the dialogues to generate empathetic and relevant responses. We propose a measurement of their causal and temporal understanding with an event graph summarization task (§4.2). In this task, the event graphs linked to each LLM speaker serve as the correct answers, and models are tasked with extracting this information from the conversation history. Third, conversational agents need to utilize relevant context recalled from past conversations to generate responses that are consistent with the ongoing narrative. We assess this ability via the multi-modal dialog generation task (§4.3).
We present extensive experimental results on the LOCOMO benchmark using instruction-based LLMs, long-context LLMs, and RAG techniques (§5). Our findings include: (1) Long-context LLMs and RAG demonstrate effectiveness in QA tasks, improving ‘memory’ capabilities of LLMs (with improvements ranging from 12-20%), but still significantly lag behind human levels (by 36%), especially in temporal reasoning, (by 41%); (2) longcontext LLMs demonstrate significant difficulty with adversarial questions in the QA task, showing a performance that is 65% lower than the base model. They are especially prone to misassigning dialogs or events to the wrong speaker. Moreover, they show poor performance on event graph summarization, indicating that they may grasp the factual elements within the entire conversation but do not accurately comprehend the context; and (3) RAG offers a balanced compromise, combining the accuracy of short-context LLMs with the extensive comprehension of wide-context LLMs, and does particularly well when dialogues are transformed into a database of assertions (observations) about each speaker’s life and persona.

heading: 2 Related Work
text: Long-term Dialogue. Recent approaches involve retrieving historical context from a range of previous dialogues and reasoning over retrieved segments in a temporal order (Lee et al., 2023b; Lu et al., 2023; Zhong et al., 2023; Liang et al.,
2023) and/or using events to scaffold the dialogues (Jang et al., 2023b; Zhang et al., 2023) to enable consistency in long-term conversations. Some limitations of such frameworks are: (1) The accuracy of retrieval can be compromised, as the retrieval model is generally trained on tasks focusing on semantic similarity rather than specifically on such dialogues. Additionally, real-world dialogues often feature co-references and missing content (i.e., anaphora) (Anantha et al., 2021), which further complicate the retrieval process (Mallen et al., 2023; Gao et al., 2023b; Liu et al., 2023b); (2) Challenges arise in reasoning over retrieved documents, especially when the model struggles to identify the correct context among the retrieved data (Liu et al., 2023a); (3) Reasoning over time intervals presents challenges. For example, the way a system responds about past events can vary depending on the amount of time that has passed since the last conversation (Zhang et al., 2023; Jang et al., 2023b). Therefore, it is essential to have conversations of considerable length, as well as a systematic evaluation framework, to accurately assess the effectiveness of approaches to long-term dialogue generation. We design a long-term conversation generation pipeline based on retrieval augmentation and events graphs and propose a framework for evaluating long-term dialog agents.
Multi-modal Dialogue. Multi-modal dialogue primarily consists of two types of tasks: imagegrounded dialogue and image-sharing dialogue. The image-grounded dialogue task is centered around responding to questions (Antol et al., 2015; Das et al., 2017; Kottur et al., 2019) or creating natural conversations related to specific images (Mostafazadeh et al., 2017; Shuster et al., 2018; Meng et al., 2020; Zheng et al., 2021). Conversely, the image-sharing dialogue task focuses on selecting images that semantically align with the provided dialogue context (Zang et al., 2021; Feng et al., 2022; Lee et al., 2023c). We use a method from the image-sharing dialogue task to create multimodal dialogs which are then evaluated as an image-grounded dialogue task.
Synthetic Evaluation Benchmark. Faced with a shortage of human-generated data and observing that LLMs are approaching the quality of humanlevel annotations (He et al., 2023; Lee et al., 2023a), there has been a surge in research drawing inspiration from this development. Consequently, numerous studies have started utilizing LLMs to
augment or synthesize large-scale dialogue benchmarks for assessing responses in everyday social interactions (Kim et al., 2023), examining responses in multi-modal environment (Feng et al., 2022), and evaluating responses that align with specific persona (Jandaghi et al., 2023). We leverage LLMs to create data but ensure its high quality with human verification and editing.

heading: 3 Generative Pipeline for LOCOMO
text: An overview of our generative pipeline for LOCOMO is shown in Figure 3. We create two virtual agents, named L1 and L2, each initialized with a LLM M (i.e., gpt-3.5-turbo). To start, unique persona statements p are assigned to each agent Li, ensuring the integration of distinct personalities into their dialogues (§3.1). To mirror real-life experiences, we create a temporal event graph G for each agent, which illustrates a realistic sequence of life events (§3.2). The LLM agent architecture (Park et al., 2023) is utilized for each agent Li, enabling them to effectively memorize and reflect conversation history into ongoing dialogues (§3.3). Further, each agent Li can share coherent images, thereby enhancing the multi-modal dialogue aspect. Finally, human annotators are tasked with manually filtering and refining the generated data (§3.4).

heading: 3.1 Persona
text: We select an initial persona statement pc from the MSC dataset (Xu et al., 2022), encompassing 4 to 5 sentences, and employ gpt-3.5-turbo as M to expand these into full persona statement p (See examples and prompt details in Appendix A.1). The generated statements typically include details about one or more of the following elements (Gao et al., 2023a): objectives, past experiences, daily habits, and interpersonal relationships, as well as name, age, and gender of the individual.

heading: 3.2 Temporal Event Graph
text: To utilize the real-life experiences of each agent in the conversation, we construct a temporal event graph, labeled as G, for each agent. This graph G, consisting of events ei, is produced by applying the condition of M (i.e., text-davinci-003) on a designated persona p. Each event ei is associated with a date of occurrence ti. G includes causal connections l = (ei, ej) that illustrate the causal relationships among events ei ∈ G and reflect a natural succession of events in an individual’s life. For each G, we create up to 25 events, spread across
a time frame of 6 to 12 months, in an iterative process that balances between inference time and the coherence of temporal and causal connections in the timeline. Initially, a small batch of k = 3 events is generated, which is then used iteratively as input prompt to create the subsequent batch of k events. See details in Appendix A.2.

heading: 3.3 Virtual Agent Architecture
text: Every agent Li incorporates modules from generative agent architecture (Park et al., 2023). The agent has two functions: (1) reflect & respond; and (2) image sharing & image reaction. The agent is asked to primarily use the reflect & respond function while employing image sharing & image reaction function judiciously and appropriately within the context of the conversation.
Reflect & Respond. The fundamental process for each agent to reflect and respond involves the concept of short-term and long-term memory. During inference, agent Li conditions its responses on both short and long-term memories, paralleling how humans remember recent conversations while also recalling distilled important experiences from long-term memory. Following each session k, each agent is asked to produce a summary wk that is then stored in the short-term Hs. This summary wk is generated by conditioning M on both the most recent session conversation history hk and the preceding summary wk−1 ∈ Hl. For each turn j within session k, a single turn of the conversation hkj is transformed into an observation okj and then stored in the long-term memory Hl. Then, agent Li generates a response in session k + 1 on the date tsk+1 by basing it on the latest summary wk, reflections based on the retrieved relevant observations o ∈ Hs, the ongoing conversation history in the current session hk+1 and persona statement p. Long-term temporal narratives are induced in the conversation by additionally conditioning the agent’s response on the subset of events in G that occur between the last and current session i.e. {e ∈ G | tsk < tei < tsk+1 }. See details in Appendix A.2.1.
Image Sharing & Image Reaction. The image sharing & image reaction functions are integrated to add a multi-modal dimension to the long-term dialogues.2 The image sharing function is called when the agent decides to send an image. This
2Image captions are also saved to long-term memory.
process includes: (1) Generate a caption c for the intended image using M; (2) Convert the caption c into relevant keywords w using M; (3) Use the keywords k to find an image through web search WEB(k)3; (4) Share the chosen image. Conversely, the image reaction function is triggered upon receiving an image from another agent and entails: (1) Generate caption c for the received image4; (2) Generate a reaction for the received image in response using M (See Appendix A.2.1).

heading: 3.4 Human Verification & Editing
text: In the concluding phase, human annotators are tasked with (1) editing the dialogue to eliminate long-term inconsistencies, (2) removing or substituting irrelevant images, and (3) verifying and editing for alignment between event graphs and the content of the conversations. Overall, we observed that annotators edited nearly 15% of the dialog turns and removed or substituted approx. 19% images present in the LLM-generated dataset. See examples of some edits in Appendix A.3.

heading: 4 LOCOMO Evaluation Benchmark
text: Based on the dialogues generated in section 3, we introduce an evaluation benchmark (see Figure 2) composed of three tasks to assess the accuracy of long-term memory. See statistics of the dataset and evaluation benchmark in Table 5 in the Appendix.

heading: 4.1 Question Answering Task
text: A conversational agent is expected to possess a memory to remember previous dialogues, reflecting it to create more engaging responses in future conversations. For a comprehensive assessment of this memory, we introduce a question-answering
3https://pypi.org/project/icrawler/ 4We use BLIP-2 (Li et al., 2023b) as the captioning model.
task divided into five distinct reasoning categories: (1) Single-hop questions require answers based on a single session; (2) Multi-hop questions require synthesizing information from multiple different sessions; (3) Temporal reasoning questions can be answered through temporal reasoning and capturing time-related data cues within the conversation; (4) Open-domain knowledge questions can be answered by integrating a speaker’s provided information with external knowledge such as commonsense or world facts; (5) Adversarial questions are designed to trick the agent into providing wrong answers, with the expectation that the agent will correctly identify them as unanswerable.
For each category, we calculate the F1 score for exact matches, following the normalization of both the predicted and the actual ground truth answers. However, evaluating long-form answers with automated metrics often presents challenges (Xu et al., 2023). LLMs tend to produce paraphrased responses in varied formats, complicating exact match evaluation. To simplify evaluation in our task, we ensure that answers in our QA annotations are directly taken from the conversations as much as possible. We instruct the LLMs to replicate the exact wording in the conversation when feasible and employ the F1 partial match metric for evaluating the predictions. Each QA sample is also annotated with the turn IDs in the conversation logs that contain the answer. We report the accuracy of retrieving the correct context for RAG models.

heading: 4.2 Event Summarization Task
text: The conversation is generated based on a temporal event graph G which is constructed by conditioning an LLM on a persona statement p, reflecting the chronological sequence of events in an individual’s life. A conversational agent is expected to not only comprehend the causal connections and the
sequence of events in G but also to recount these events as required. To evaluate the agent’s grasp of event dynamics, we introduce the event summarization task which challenges the agent to summarize the events within a designated timeframe and compares the agent’s summary with events in G. The events in LOCOMO are densely annotated lists of life events that are hard to summarize due to temporal and causal coreferences present in the dialogues, in contrast to existing summarization benchmarks of research papers (Li et al., 2023a), movie scripts (Chen et al., 2022), books (Kryściński et al., 2022), emails (Zhang et al., 2021b) etc.
Traditional metrics like BLEU (Papineni et al., 2002) and ROGUE (Lin, 2004) focus on lexical similarity between the reference and generated summaries, not meeting our needs as we emphasize factual accuracy in summarization. In this context, we employ FactScore (Min et al., 2023), a method that evaluates the factuality of generated text by decomposing both the reference and hypothesis into atomic facts. We adapt the metric to measure (1) precision of the summarized content by counting the number of atomic facts within the content that correspond with those in G; (2) recall of the summarized content by determining how comprehensively the atomic facts of G are represented within the content. We present the F1 score, derived from the calculated precision and recall.

heading: 4.3 Multi-Modal Dialogue Generation Task
text: The conversations in our dataset are anchored to specific personas p and corresponding events G tailored to p. The topics in conversations evolve from events that were introduced in earlier dialogues, spanning weeks or months. This structure allows for an assessment of whether conversational agents can sustain a coherent persona and a continuous narrative over time. For example, if a speaker recently had an injury, the next conversations would likely focus on them recuperating, rather than engaging in adventurous activities. We assess such consistency by measuring how closely the predicted multi-modal dialogues align with the ground truth multi-modal dialogues in our dataset, quantifying this alignment through MMRelevance (Feng et al., 2022), in addition to other NLG metrics.

heading: 5 Experimental Setup
text: For the question-answering and event summarization tasks, we replace images in LOCOMO with
their captions (Li et al., 2023b), and use state-ofart LLMs to reason over text-only dialogues interleaved with image captions. We use images directly for the multimodal dialog generation task only. See additional details in Appendix C.
Question Answering. We evaluate three types of models: (1) Base LLMs operating with constrained context lengths where earlier dialogues are omitted i.e., Mistral-7B-Instruct-v0.2 (Jiang et al., 2023), Llama-2-70b-chat (Touvron et al., 2023), and Llama-3-70B-Instruct5; (2) Long-context LLMs with an extended context window i.e., gpt-3.5-turbo 6, gpt-4-turbo 7, gemini-1.0-pro (Team et al., 2023) and claude-3-sonnet8; (3) Retrieval-augmented Generation (RAG) involves retrieving relevant context from a database of dialog history, observations (assertions about speakers; see §3.3, Figure 9), or session-level summaries (see §3.3, Figure 8). We employ DRAGON (Lin et al., 2023) as retriever and gpt-3.5-turbo as reader.
Event Summarization. We present experiments using Base and Long-context setups from the question-answering task, but refrain from including RAG since summarization requires a comprehensive understanding of the entire dialogue, rather than just retrieving a specific portion. We implement incremental summarization i.e., iteratively create a summary of a preceding sessions and then use that summary as a basis to summarize the subsequent sessions (Chang et al., 2023).
Multi-modal Dialogue Generation. We generate 50 conversations using our automated pipeline (without human filtering; §3) for training data and train three versions of MiniGPT-5 (Zheng et al., 2023): (1) Base trains on prior dialogue turns only; (2) + summary trains on prior dialogue turns and a global summary of the ongoing conversation; (3) + observation trains on prior dialogue turns and observations retrieved from conversation history. Each run is initialized with a MiniGPT-5 checkpoint finetuned on MMDialog (Feng et al., 2022).
5https://llama.meta.com/llama3/ 6https://platform.openai.com/docs/models/gpt-3-5 7https://platform.openai.com/docs/models/gpt-4-and-gpt-
4-turbo 8https://www.anthropic.com/news/claude-3-family

heading: 6 Experimental Results
text: We evaluate and analyze the comprehensive performance of all baseline methods for question answering (§6.1), event graph summarization (§6.2), and multi-modal dialogue generation (§6.3).

heading: 6.1 Question Answering Task
text: Tables 2 and 3 present the performance results for the question answering task. We find that: (1) LLMs with limited context length face challenges in understanding extremely long conversations due to truncated context windows. Despite gpt-4-turbo emerging as the top-performing model with an overall score of 51.6, it notably lags behind the human benchmark of 87.9; (2) longcontext LLMs can comprehend longer narratives, yet they are prone to generating hallucinations. gpt-4-turbo outperforms other approaches on overall performance, but its performance on adversarial questions drops to a mere 15.7%, as compared to 34.8% using gpt-3.5-turbo and 80.0% using llama-3-chat-70B with 4K context lengths. Similar trends are observed in gemini-pro-1.5 and claude-sonnet models as well. The overall performance of gpt-3.5-turbo increases with context length, mainly due to large improvements in single-hop and multi-hop scenarios, yet the per-
formance on adversarial questions drops dramatically. This indicates that LLMs can be easily misled into generating hallucinations when they are subjected to long contexts; (3) long-context LLMs struggle to utilize the recalled context correctly. The performance gap between single-hop and multi-hop question categories demonstrates that LLMs have become reasonably good at ’memorizing’ a large context window, but find it challenging to perform complex reasoning over the recalled context; (4) RAG is effective when conversations are stored as observations. There is a noticeable 5% improvement with gpt-3.5-turbo when the input is top 5 relevant observations instead of pure conversation logs. This improvement falters with an increase in the number of retrieved observations, suggesting that it is important to reduce the signalto-noise (SNR) ratio in retrieved contexts for models to utilize the context accurately. Conversely, using session summaries as context does not significantly improve the performance despite high recall accuracies9, likely due to loss of information during the conversion of dialogs to summaries.
The interesting finding is that time reasoning and open-domain knowledge questions are the most challenging scenarios. (1) LLMs face chal-
9For summary-based RAG models, the recall accuracy is based on retrieving the summary of the relevant session(s).
lenges in understanding time concepts within dialogues, which is consistent with findings from other single-turn-based benchmarks focused on temporal reasoning capabilities for LLMs (Wang and Zhao, 2023). (2) LLMs struggle with opendomain knowledge and degrade in the RAG setting. This suggests that while certain open-domain knowledge may be embedded within the model’s parameters, introducing improper context from inaccurate retrieval can lead to a decline in performance (Mallen et al., 2023).

heading: 6.2 Event Summarization Task
text: Table 4 presents results for the event summarization task. Powerful long-context models record the highest performance on this task. gpt-4-turbo leads to the highest scores in terms of ROUGE and FactScore metrics, followed by gemini-1.0-pro and claude-3-sonnet. The use of incremental summarization with Llama-3-70B-Instruct (4K context window) performs reasonably well compared to the long-context models, demonstrating only a 2.4% drop in ROUGE-L score. However, there is a nearly 10% drop in performance on the FactScore metric, suggesting that it fails to capture as much information as the long-context models. Nonetheless, there remains considerable scope for improving performance on this task. The event summarization task requires long-range dependency to understand the temporal and causal connections between the events discussed by the speaker in multiple sessions (see Figure 7). The wide gap between the best-performing model and the upper bound on this task suggests that longcontext models may not be proficient at utilizing their context appropriately, which also aligns with similar findings in Li et al. (2023a) as well as the QA task in LOCOMO.
From a manual analysis of predicted summaries, we identify five broad categories of event summarization errors made by LLMs: (1) missing information in events because the model fails to make temporal and/or causal connections over a lengthy conversation; (2) hallucinations i.e., models pad extra details that are either not present in the conversation or are part of a different event in the same session; (3) errors from misunderstanding of dialog cues such as humor or sarcasm is a distinctive issue with comprehension of dialogs; (4) inaccurate speaker attributions; and (5) insignificant dialogs that are wrongly considered as salient events. See examples in Table 6 in the Appendix.

heading: 6.3 Multi-Modal Dialog Generation Task
text: Figure 4 illustrates the effectiveness of various MiniGPT-5 training variants in multi-modal dialogue generation. Incorporating context into training enhances performance, with the inclusion of observation as context yielding significantly improved results. For instance, in Figure 4A, the retrieved observations contain information about the speaker’s experience in video game tournaments, which leads to the prediction of dialog and images that are more faithful to the speaker’s persona. This observation is consistent with earlier findings from the QA task as well (see Table 3). Also, we observe that the MM-Relevance score drops with an increase in the length of dialog history (see Figure 4B). Retrieval-augmented generation alleviates the drop in MM-Relevance to some extent.

heading: 7 Conclusion
text: We develop a human-machine pipeline to collect LOCOMO, a dataset of 10 high-quality very long conversations, each encompassing 600 turns and 16K tokens on avg., over up to 32 sessions, and propose an evaluation framework consisting of three tasks that evaluate models’ proficiency in long conversations. Our experiments show that LLMs struggle to comprehend long-term narratives within the dialog and fail to draw temporal and causal connections between events discussed by speakers.

heading: 8 Limitations
text: Machine-generated data. Our dataset is sourced primarily from text generated by LLMs. We pursued this method, which has quickly emerged as a popular alternative to time-intensive manual data collection (Kim et al., 2023; Jang et al., 2023b), to avoid the logistical and legal complexities of collecting very long-term real-world conversations at scale. We ensure that the dataset mirrors real-world interactions as much as possible by having human annotators verify and edit the generated conversations. However, we acknowledge that this dataset may not fully reflect the nuances of real-world online conversations.
Limited exploration of multimodal behavior. Since the images in our dataset are sourced from the web, they do not demonstrate the visual long-term consistencies that are usually exhibited in personal photos (e.g., appearance, home environment, people and pets, etc.). Consequently, we find that the
images in our dataset can be replaced with their captions without much loss of information, except for cases where OCR is required. Nevertheless, our work is a first step toward research into the multimodal aspect of very long-term conversations.
Language. Our LLM-based pipeline for generating long-term conversations has been developed for the English language only. However, our pipeline can be made to work with any other language using an LLM that is proficient at that language and appropriate translations of our prompts.
Closed-source LLMs. We use state-of-the-art LLMs in our dialog generation pipeline to create a dialog dataset that is as realistic as possible. Unfortunately, this meant employing LLMs that are not open-sourced and are only available through a paid API, similar to many concurrent works that generate synthetic conversations (Zhong et al., 2023; Lu et al., 2023). We will make the code for our generative pipeline publicly available in the hope that it can be made to work effectively with open-source LLMs in the future.
Evaluation of long-form NLG. LLMs are prone to generating verbose answers even when prompted to answer in short phrases. This creates challenges in evaluating the correctness of answers provided by LLMs and has been widely documented in NLP literature (Chang et al., 2023; Xu et al., 2023; Kr-
ishna et al., 2023). Our evaluation framework suffers from the same challenges when used for experimenting with LLMs.

heading: 9 Broader Impacts
text: We adopt and improve a framework of generative agents introduced in Park et al. (2023) for the generation of long-term conversations. Consequently, the ethical concerns of generative agents outlined by Park et al. (2023) apply to our work as well, especially since the goal of our framework is to make the conversations as realistic as possible.
Specifically, conversational agents that can pose as human beings with a realistic life, as enabled by the temporal event graphs in our framework, pose the risk that users may form parasocial relationships with such agents that may affect their lives adversely. We recommend that any practical deployment of the generative framework in our work be always prefaced with a disclaimer about the source of the dialogs.
Second, the use of multimodal LLMs (Zheng et al., 2023) to generate images conditioned on dialog can lead to the propagation of misinformation and social biases, especially if the conversational agent can be coerced into parroting false information or dangerous opinions.
Third, it is tempting to use generative agents to substitute real humans for a process, especially
when there are significant challenges in working with humans for a particular goal e.g., collecting real-world interactions between humans over a year or more. Care must be taken to ensure that such substitutes are not made in studies whose outcomes may be used to make real-world decisions with tangible impacts on humans. Our work is merely a study of model comprehension in very long-term conversations. We do not make any recommendations for real-world policies based on this study and advise potential users of our framework to avoid making such recommendations as well.

heading: A Generative Pipeline for LOCOMO
text: A.1 Persona
We assign unique persona statement p to each agent Li. For this, we select a range of initial persona statements pc from the MSC dataset (Xu et al., 2022), each encompassing 4 to 5 sentences. We employ gpt-3.5-turbo as M to expand these into full persona statement p, conditioning M on the chosen statements pc. The prompt used for converting a short list of speaker attributes from the MSC dataset (Xu et al., 2022) into a complete persona summary is presented in Fig. 5. We also use a single example of speaker attribute → persona summary as an in-context demonstration along with the prompt. A small selection of personas showcasing the diversity of speakers in the LOCOMO dataset is demonstrated in Fig. 5.
A.2 Temporal Event Graph
As outlined in Sec. 3.2, we use an iterative process for generating event graphs consisting of causally connected events based on a given persona summary. The base prompt for describing the constitution of the event graph, the nature of events and causal connections between events is shown in Fig. 6. First, the base prompt is used along with the prompt for event graph initialization to generate three independent events relevant to a given personality. Then, the base prompt is combined with the prompt for the iterative generation of events to continue generating events that are caused by one or more of the events that are already present in the graph. See an example of a persona and the corresponding temporal event graph in Fig. 7. In the example, Jack aspires to be a hotel manager. Consequently, he enrolls in a hotel management course in July, and after three months, he expresses his excitement about the course on social media. In
a similar vein, his passion for gaming results in an invitation from a well-known gaming company.
A.2.1 Virtual Agent Architecture As outlined in Section 3.3, the virtual agents in our generative pipelines are composed of two mechanisms, Reflect & respond (Park et al., 2023) and Image sharing & response.
Reflect & respond. This mechanism operates over a combination of short-term and long-term memory. The short-term memory is a summary of a session that is conditioned on the summary from a previous session. See the prompt given to LLMs in our pipeline for generating summaries, and an example of a generated summary, in Fig. 8. The long-term memory is a database of observations about each speaker, that are essentially assertive statements about the speaker’s persona and life. See the prompt given to LLMs in our pipeline for generating observations, and an example of observations extracted from a conversation, in Fig. 9. In practice, the conversation is annotated with turn IDs for each turn, and the model is also instructed to indicate the turn IDs that directly contribute to each observation. This allows us to keep track of the evidence when using observations as the context for RAG-based models used in our experiments (see Section 5).
Image sharing & response. See prompts for implementing image-sharing and image-response behaviors in Figure 10.
A.3 Human Filtering
Human annotators are instructed to edit the LLMgenerated conversations in the following scenarios:
• Remove an image if it is not relevant to the current dialog or the conversation.
• Add context about an image to the current speaker’s dialog if it is not discussed by them but the subsequent speaker has reacted to the image.
• Replace an image if it does not match the caption that was used to query for images.
• Edit the dialog when the information present in the dialog is inconsistent with something said (or shared through an image) in earlier or later turns.
• Edit the dialog to ensure that the details in the conversation are consistent with those given in the event for the session.
• Remove any events from the event graph if they do not appear in the conversation.
See an example of some edits in Fig. 11.

heading: B Dataset
text: B.1 Dataset Statistics
See a breakdown of the statistics of the conversations in the LOCOMO dataset in the top panel of Table 5. Also, see a breakdown of the statistics of the annotations in the evaluation benchmark in the bottom panel of Table 5.
B.2 Dataset License
The LOCOMO dataset will be released under the CC BY-NC 4.0 DEED license.10
B.3 Annotator Details
The annotators who worked on the LOCOMO dataset were in-house annotators and we were unable to obtain their demographics due to the confidential nature of such information.
10https://creativecommons.org/licenses/by-nc/4. 0/

heading: C Experimental Setup
text: C.1 Baselines
The conversations in the LOCOMO dataset are composed of natural language dialogs and images that require higher-order reasoning and multimodal coreference resolution, respectively. From initial studies, we observed that multimodal coreference resolution can be performed effectively by replacing images in LOCOMO with their captions gen-
erated using BLIP-2 (Li et al., 2023b), and using state-of-art LLMs to reason over natural language text interleaved with image captions. Hence, our experiments for the question answering and event summarization tasks are conducted using LLMs. We use the images directly only for experiments on the multimodal dialog generation task.
Question Answering. We carry out experiments using three distinct methodologies: (1) Base involves utilizing LLMs to directly conduct the task within a constrained context. The task description comes after the dialogue history. To accommodate the restricted context window size, earlier dialogues are omitted; (2) Long-context employs LLMs with an extended context window to expose the models to as much dialogue context as possible; (3) Retrieval-augmented Generation (RAG) involves retrieving relevant context from a database of dialog history, observations, or session-
level summaries. Observations are assertions about each speaker extracted from the dialog history as described in §3.3, see an example in Figure 9. Session-level summaries are concise summaries of the conversation that takes place in each session, see an example in Figure 8. For the retrieval model, we employ DRAGON (Lin et al., 2023). To assess the effectiveness in practical scenarios for Long-context and RAG, we draw comparisons using variants of gpt-3.5-turbo. We do not report the performance of long-context fine-tuned opensource models (Chen et al., 2023) or those utilizing sliding window (Bertsch et al., 2023; Dao et al., 2022) due to the variability inherent across different open-source models and the potential reduction in their capability on shorter context.
Event Summarization. We present experiments conducted in two distinct configurations. We use both the Base and Long-context setups from the
question answering task, but we refrained from including RAG since summarization requires a comprehensive understanding of the entire dialogue, rather than just retrieving a specific portion. A notable distinction in our approach, compared to the question-answering task, lies in our handling of the context. Specifically, we employ an iterative process of creating a summary of a preceding session and then use that summary as a basis to generate the summary for the subsequent session (Chang et al., 2023). Further, we use a single in-context demonstration of input and output to guide the model toward selecting only significant life events for the summary.
Multi-modal Dialogue Generation. For evaluating multi-modal dialogue generation, we train MiniGPT-5 (Zheng et al., 2023) on 50 conversations generated using our automated pipeline (without human filtering) as detailed in §3. Three distinct versions of the model were developed, each with varying training data: (1) Base trains on preceding dialogue turns; (2) + summary trains on both prior dialogue turns and a global summary of the ongoing conversation; (3) + observation trains on both preceding dialogue turns and relevant observations retrieved from the conversation history. For each of these models, we started with
a MiniGPT-5 checkpoint pretrained on the MMDialog dataset (Feng et al., 2022).
C.2 Implementation Details
We use OpenAI API, Gemini API, Claude API and Huggingface (Wolf et al., 2020), as of May 2024, with specific settings of temperature set to 0 and topp set to 1 for evaluation of the LOCOMO benchmark. All experiments, including those for RAG-based models, MiniGPT-5 training, and inference, are conducted on an Nvidia A6000 server with FP32. We report results from a single inference run for each model in our experiments. For MiniGPT-5, we used the hyperparameters recommended in the original codebase and trained our models for 10 epochs, which took approximately 30 hours on a single A6000 GPU.
We use the default implementations of BLEU11, ROUGE12, BertScore13, FactScore14 metrics in their respective Python packages in our evaluation protocol.
11https://www.nltk.org/_modules/nltk/translate/ bleu_score.html
12https://pypi.org/project/rouge/ 13https://pypi.org/project/bert-score/ 14https://github.com/shmsw25/FActScore

heading: D Results
text: D.1 Event Summarization Task See an example of the five broad categories of event summarization errors made by LLMs, outlined in Section 6.2, in Table 6.
D.2 Multimodal Dialog Generation Task Results from evaluation of various version of MiniGPT-5 model on the multimodal dialog generation task in the LOCOMO benchmark is shown in Table 7.

heading: Future Work
text: We will make the code for our generative pipeline publicly available in the hope that it can be made to work effectively with open-source LLMs in the future. Evaluation of long-form NLG. LLMs are prone to generating verbose answers even when prompted to answer in short phrases. This creates challenges in evaluating the correctness of answers provided by LLMs and has been widely documented in NLP literature (Chang et al. , 2023; Xu et al. , 2023; Kr-
ishna et al. , 2023). Our evaluation framework suffers from the same challenges when used for experimenting with LLMs